A Comprehensive Idea-Summary for the Website

Website Overview:

In everyday life and in special projects, we use a creative-and-critical Process of Design to solve problems by designing better products, activities, strategies, and explanations.  This includes almost everything we do in life.

Design Process is a cycle of creatively generating ideas and critically evaluating ideas.  During this flexible process of goal-directed improvising, to evaluate solution-options we use Quality Checks (by comparing Predictions or Observations with Goals that define quality) and Reality Checks (by comparing model-based Predictions about “how the world works” with Observations of reality), and we coordinate our problem-solving actions.

Quality Checks & Reality Checks are the essence of design & science, respectively.  Science is one use of design — during science we design experiments (to let us observe) and design explanatory models (to explain what we observe) so we can understand — and Science Process (Scientific Method) is one application of a broader Design Process.  My models for these methods are an extension of my PhD project when I developed a model of Scientific Method and used it as a framework for analyzing inquiry instruction.

People use design for "almost everything," and this wide scope lets teachers build educational bridges from life to design and then to science, and back into life:  students have used design, so we can build on what they already know, consistent with constructivist theories of learning;  using Design Process will help students learn scientific thinking skills;  design experiences in school will be useful in students’ lives, now and in the future, thus increasing their motivation to learn in school.

We can teach Design Process (and Science Process) using classroom activities or with computer-based instruction modules that match the learning styles of many students, use less classroom time if modules are assigned as homework, and reduce preparation time for teachers.

A wide variety of student experiences with design-inquiry can be coordinated over time and across many subject areas (in sciences, arts, engineering, humanities) to improve their creative-and-critical thinking skills in each area and promote a transfer of skills from one area to another, and into life.  In early K-12 the coordinating can be done by one teacher of many subjects, and in later K-12 or college by cooperation among many teachers of different subjects.   [ We can Design Curriculum-and-Instruction for Ideas-and-Skills Education in a Wide-Spiral Curriculum that can help students achieve some of the new K-12 Standards for Science Education.  I want to work with other educators in collaborations to improve education. ]  [ Why we should teach Design Process and How to teach Design Process by combining Experiences with Reflection to teach Principles ]

A beneficial design activity, for K-12 through college and beyond, is a Learning Strategy in which students metacognitively “think about thinking” so they can improve their thinking and learning, in an effort to achieve their full potential and “be all they can be.”  In cognitive-and-metacognitive Learning Strategies, students learn about learning skills (from others and by observing themselves) and then Plan (by evaluating strategies and choosing one), Observe their own strategy-applying actions and the results, evaluate the strategy and their strategy-application so they can re-Plan, in a cycle of design (Plan-Observe-Plan...) that continually improves the quality of their learning, thinking, and performance.

Students can use Design Process for all classes, in Learning Strategies to improve their conceptual knowledge (ideas) and procedural knowledge (skills), and for Design Activities (problems, projects, or papers) to apply their knowledge.  To evaluate the wisdom of their personal strategies for living, they can ask “are my decisions helping me achieve my goals for life?”

I recommend reading the SHORTER Page-Summaries before the LONGER Page-Summaries below:

 ▓  Recognizing Opportunities and Solving Problems    (into left frame`)

I recommend reading the revised version of this page-summary.

What is a problem?  In the context of design,

a problem is any situation where you have an opportunity to make things better.  These opportunities occur often in many areas of life, for a wide range of objectives, so (as explained below) we use a process of problem-solving design for "almost everything we do in life."

problem solving is converting an actual current situation (the NOW-state) into a more desirable future situation (a GOAL-state you want to achieve)."

You can make a difference by “making things better” when you choose any aspect of life and increase its quality, or when you minimize a potential decrease of quality.

 ▓  Objectives of Design   (into left frame`)

A Wide Range of Objectives:  Designers combine creativity and critical thinking to solve problems in a wide range of design fields — such as engineering, architecture, mathematics, music, art, fashion, literature, education, philosophy, history, business, athletics, medicine, law, and science — where the objective is to design (to find, invent, or improve) a better product, activity, strategy, and/or explanatory theory.  These objectives extend far beyond the "design fields" to include almost everything we do in life, as explained here and illustrated with examples here.  How can I justify this claim for "almost everything we do"?  Here are three reasons:

I recommend reading the condensed-and-revised version for the rest of this page-summary.

A Wide Range of Strategies:  My definition of strategies is very broad so it includes personal strategies (for actions in everyday life) and professional strategies, and strategies to improve learning and strategies to improve physical skills.   {of course, there is lots of overlap in these categories-for-strategies}

Generating Ideas and Evaluating Ideas:  I define design thinking broadly, by claiming that you use a process of design whenever you creative Generate Options (for a problem-solution) and critically Evaluate Options, so you can make a decision.  This creative-and-critical process, operating in Cycles of Design, is outlined in the simplest model for Design Process, in Stage 1.

Description versus Prescription:  The framework for my model of Design Process is mainly descriptive (of what is), but in this website the supplements are often prescriptive (for what should be).  Although I recommend using a high quality of creative thinking (to Generate Options) and critical thinking (to Evaluate Options), I don't claim that “it isn't design thinking unless the quality of thinking is high.”  Instead, I just encourage high-quality thinking, and describe ways to think more effectively.    { framework plus supplements – Should our views of design-thinking be descriptive or prescriptive? }

The wide scope of design — which includes science, because Science is Design* — is educationally useful in many ways, because it lets teachers build Educational Bridges (from life to design & science, then back into life) that can help students increase their Motivations for Learning and Transfers of Learning, with instruction using a wide variety of Design Activities, as explained in summaries of these four pages.

* In two types of design the main problem-solving objectives are solving "problems" (by designing a Solution in General Design) and answering problem-questions (by designing an Explanatory Theory/Model in Science-Design).

Is "design" a verb, noun, or adjective ?  It can be any of these, when used in different contexts:  We will use clever design thinking to design a satisfactory design during a process of design (of design-actions) in our design project.

 ▓  HOW should we teach Design Process?    (put into left frame`)

I recommend reading the revised version for the shaded parts of this page-summary.

The main teaching strategies involve timings, by using a Sequence of Activities (to provide Experience before Principles) and a Sequence of Principles (to teach Principles in a logical progression):

• A Sequence of Activities  —  Students do design activities` to gain Experience with the process of design;   they ask “what did we do, how, and why?” in Reflection on their experiences;   you help them understand (through discovery + explanation) how their experiences illustrate the Principles of Design Process.    /    In practice these related aspects of learning (in experience, reflection, principles) will overlap — although "experience before principles" should be an essential feature of instruction — and discussions (in student groups or as a whole class) can be useful throughout the process of learning.

• A Sequence of Principles  —  A strategy of progression, beginning with simplicity and gradually building understanding in easy-to-master steps, is used by most teachers in most of their instruction.  To help students understand the Principles of Design Process, we can use the 5-stage progression outlined in An Overview of Design Process.

These two sequences, for Activities & Principles, can be combined in many ways, with teachers deciding the details of sequencing and pacing.  For example,

Teaching in Spirals:  A flexibility in returning to earlier stages in a progression can be useful for a “spiral instruction” experience in which students will see/think/learn with their current perspectives, knowing more than they did earlier.  These repetitions-in-a-spiral produce distributed learning (from many experiences) that is more effective than massed learning (all at once)."  Spiral Instruction can be part of a Wide Spiral Curriculum.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

These two sequences let us design instruction that combines the benefits of experience and organization:

Experience increases Understanding:  When students begin with Experiences of Design Process — so they already have done everything in Design Process physically-and-mentally during Design Activities, and mentally during Reflection Activities — they can more easily learn Principles of Design Process through guided-discovery learning* supplemented by explanation-based learning.   /   * "guided discovery"?  Students can “discover” during initial experiences with design, and later (as in a spiral curriculum) teachers can build on the foundation of what students already know.  A teacher's goal-directed “guiding” can include reflection requests (to promote metacognitive reflection during a design experience or after it) and, individually or in group discussions, guiding students toward learning principles of Design Process.

Organization increases Understanding:  The logical organization of Design Process — which can be learned through a 5-stage progression of instruction — makes it easier to understand, remember, and use.

Principles of Design Process:  The main principles — shown in Diagram 3b` and explained in Stages 1-3 — are the Cycles of Design (Generation-Evaluation-Generation-...) that occur when creative Generation is guided by critical Evaluation in Quality Checks (done mentally or physically) in which Quality is defined by Goals, and the analogous Cycles of Science that occur when creative Generation is guided by critical Evaluation in Reality Checks.  And there are other principles, such as designing experiments that can be done mentally and/or physically (done from Stage 1 onward, but not explained until Stage 4), and "learning from experience" with Cycles of Plan-and-Monitor (explained in Stage 2b), and more.

 ▓  Design Process – an Overview    (into left frame`)

I recommend reading the version for the shaded parts of this page-summary.

Goals for Design Process:  My model* of Design Process is intended to be useful for description (to accurately describe the problem-solving process of design used by experts in General Design & Science-Design) and for education that will help students:
• understand the process of design and, more generally, the nature of design-and-science,
• improve their skills with solving problems (in design) and answering questions (in science).

{more about these goals}

* actually, Design Process is a family of models, as explained below.

I recommend reading the SHORTER VERSION of this page-summary, because it's condensed-and-revised, is now shorter and better.

Five Stages:  Some useful principles for design-thinking are explored in a 5-stage progression of learning that begins with simplicity and gradually moves into complexity, as we explore a family of related models for Design Process.  But even in Stage 4, any “feeling of complexity” will be reduced by meaningful understanding, and a logical progression of learning (from Stage 1 through 2a and 2b to 3 and 4) makes it easier to understand the meaningful “story” in a process of design.

with Simplicity and Depth:  Basically, a problem-solving process of design is simple;  we creatively Generate ideas and critically Evaluate these ideas, in creative-and-critical Design Cycles of Generate-and-Evaluate, as described in Stage 1.  But the process is full of details that are interesting (if you're interested in education, in how we think, learn, and perform) so there are plenty of fascinating ideas to explore more deeply in the models-for-process described in these five stages.  We also can look at individual components of the overall process, in functionally related Modes of Thinking-and-Action that are the basis for a semi-model of Design Process, which becomes a model when the modes are logically organized (as in the 5 Stages) to show how sequences of thinking-and-actions are flexibly coordinated during a process of design.     { no model, semi-model, or model }

in a Family of Related Models:  All five stages describe the same process of design.  But each stage looks at this process from a different perspective and with a different level of detail.  The 5 stages, each with its own distinctive verbal-and-visual description, form a family of related models-for-process that all describe the same process of design, so Stage 1 = Stage 2a = Stage 2b = Stage 3 = Stage 4.  But even though all stages are equivalent in the thinking-and-actions that can be used (and typically are used) for doing design, each stage gives you a different perspective on the process, and can help you construct a different understanding of the process.  A progression of stages is useful for learning the thinking-and-actions, and therefore is useful for instruction, because it provides options...

for Flexibility in Teaching:  Using these stage-models in a sequence to teach principles allows instructional flexibility.  Teachers can design different instruction activities for students (to provide experience + reflection + principles) at each stage in their learning, perhaps by using a whole-part-whole approach to "let students sometimes focus on parts... and at other times do the whole process."  In this way, as students move through the stages they can do more modes of thinking-and-action, and understand more (at a deeper level) about the logically organized functional relationships between modes.

Options:  You can read the 3 paragraphs below, about teaching and learning, or skip ahead to Stage 1.

Two Sequences for Teaching:  We should teach Design Process using a sequence of principles (as in this 5-Stage Progression of Learning) operating within a sequence of activities (experience, reflection, principles) that provides experience-before-principles.  While you study the explanations of principles (in the stages below) and diagrams (on the right side`), imagine a classroom in which students already have done everything in a process of design, and have reflected on their thinking-and-actions, so they will be able to connect their personal experiences with the logical principles of Design Process.

Learning by Discovery for Students:  When a teacher gently guides students in "reflection on their thinking-and-actions" so they self-discover principles of Design Process, students are using a process of inquiry to learn the skills of inquiry-process.  This learning-by-discovery becomes easier when we build educational bridges for "transfers of learning from life into school" by helping students "realize that during a process of design they are using skills they already know" because they use design-thinking for almost everything they do in life.  Students already know the basic principles of design — due to their many prior experiences of using design-thinking in everyday life and in school, especially in their most recent design experiences — so with “discovery learning” they are just making their own prior knowledge more explicit-and-organized within the logical framework of Design Process.  In addition to this familiarity, the simplicity of Stage 1 — in which the main problem-solving process is just a cycle of Generating Ideas and Evaluating Ideas — will help students feel comfortable with doing design, and learning the principles of Design Process.    { To help students recognize the design-thinking they already know and have been using in life and in school, we can ask reflection-questions before, during, and after their experiences with design. }

Learning by Discovery and from Explanations:  We should supplement this discovery learning with explanation-based learning to produce a creatively designed eclectic blending of instruction.

Learning by Discovery for Teachers:  If you (*) want to explore, you can discover principles of Design Process by studying each of the main diagrams (1, 2a, 2e/3a/3b, 4a/4b) while thinking about the thinking-and-actions we use during a process of problem solving, and asking “what part of the problem-solving process is represented by each part of this diagram?”   /   * Who is "you"?  This page-summary, along with the rest of the website, is designed for educators (including teachers), not students.  Because teachers have more problem-solving experience than students, a strategy of unguided learning-by-discovery will work better for teachers.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

• Stage 1 — Cycles of Design

In this stage you see an overview of Design Process, which is explored more deeply in the next four stages (2a, 2b, 3, 4) of a progression for learning.

Diagram 1` shows a preliminary foundation-process (to Choose an Objective and Define Your Goals for a Solution) plus a design-cycle of GENERATE-and-EVALUATE in which you GENERATE Options and EVALUATE Options.

Define and Solve:  First you define a problem by defining its objective & goals, as shown at the top of Diagram 1.  Then you try to solve the problem by using cycles of Generate-and-Evaluate, in the bottom of Diagram 1.   /   It can be educationally useful to think about Design Process as these two steps, Defining and Solving.  And this is approximately accurate in describing a process of design, because Defining does tend to happen early in a Design Project, followed by Solving.  But these "steps" should not be interpreted rigidly, because overlaps-in-timing occur with a mixing of interactive modes.  One flexibility in timing is shown in Diagram 2a by two arrows (large downward, small upward) between the top and bottom.

To begin a Design Project you Recognize a Problem and Choose an Objective (Define an Objective) and Define your Goals for the properties (characteristics + constraints) you want in a Solution for the Problem, based on old information you already know;  and you can Prepare by finding new information.   Then you try to design a Solution by using...

iteration in Cycles of Design:  After this foundation-process (when you Choose-and-Define, Prepare) you use creative thinking to GENERATE Options (that are potential Problem-Solutions) and use critical thinking to EVALUATE Options.  You continue creative-and-critical thinking in iterative Cycles of Design,* trying to find a satisfactory Solution.

analogous Cycles of Science are explained in Stage 3, which explores both cycles (for Design and for Science) in more depth.

* iteration is "a procedure in which repetition of a sequence of operations yields results successively closer to a desired result." - Merriam-Webster Dictionary

Is this page-summary (for An Overview of Design Process) really a summary?  No, it's a revision of the full-length page, and it includes many new details (developed since beginning this revision) that I hope you'll find interesting and useful.

Grayscale Versions of Diagrams (without colors and background-shading) let you print the diagrams using less ink, especially colored ink.  Or you can compare diagrams in grayscale (on left side) and color (on right side) with this link`.    { I.O.U. - After I made conversions from color to grayscale, a few color diagrams have been revised in minor ways;  in November I also will revise their grayscale versions. }

• Stage 2a — Quality Checks

Diagram 2a shows that — although in many cycles you generate-and-evaluate many OPTIONS — in each cycle you evaluate one Option;  you CHOOSE an Option, and EVALUATE THIS OPTION.   How?

You imagine using this Option in a Mental Experiment and make Predictions so you can compare these Predictions (expected properties) with your Goals (desired properties) in a Prediction-Based Quality Check that helps you evaluate the Option’s overall quality.

Or you actually use this Option in a Physical Experiment and make Observations so you can compare these Observations (observed properties) with your Goals (desired properties) in an Observation-Based Quality Check that helps you evaluate the option’s overall quality.

In each type of Quality Check, an Option's overall quality is defined by your GOALS.

Cycles of Design:  Diagram 2a shows the cyclic nature of GENERATE-and-EVALUATE with two arrows, ↑ and ↓ .   After you "GENERATE Options" and "CHOOSE an Option to evaluate", you then (↓) mentally "EVALUATE this Option" with Prediction-Based Quality Checks and/or Observation-Based Quality Checks, and use this evaluative feedback when you (↑) "GENERATE Options" to begin a new Cycle of Design.   {more about Cycles of Design}

You continue cycles of GENERATE-and-EVALUATE until you decide to accept an option as a solution, or you delay work on the project, or abandon it.

Learning and Teaching:  Students can discover principles of Design Process with guided reflection on their experiences when, during Stages 1 and 2a, you encourage them to ask “what did we do [to make Predictions & Observations, and use them], and how [by comparing our Predictions & Observations with our Goals-for-an-Option], and why [to evaluate the quality of an Option, with quality defined by our Goals].”  In this way, students first understand the concepts and functional relationships.  Then you explain the terms-for-concepts and representations-of-relationships verbally (as in this section) and verbally/visually (in Diagram 2a).

Multiple Quality Checks → Quality Status:   In all 5 stages of this progression, Quality Checks are used to estimate an overall Quality Status (that can range from low to high) for each Option.  How?  By combining evaluations (both old and new) from multiple Quality Checks (Mental & Physical) that compare many Predictions & Observations (from many Experiments, run mentally & physically) with many Goals — so “all things are considered” — for each of the competitive Options, to help you make decisions about Options in evaluation that is (in a productive meaning of the term) argumentation.

Optimizing a Solution:  Usually, when you have "many Goals" there will be "tough competition because several options offer different benefits, with each better for some Goal-criteria" so you must set priorities (by defining the importance of each Goal) and use trade-offs (by more effectively achieving some Goals at the expense of others) to design an optimal Solution that is best, when all things are considered, for achieving an overall combination of your prioritized Goals.

Mental-and-Physical Experimenting:  An essential feature of Design Process is the “parallel relationship” between mental experimenting & physical experimenting (used in prediction-based & observation-based Quality Checks) that you see in Diagrams 2a and 2e.   {more about the educational benefits of logically organizing Principles of Design to show functional relationships between modes of thinking-and-action}

• Stage 2b — Learning from Experience

A process of design is a way to learn from experience, when you make plans and then adjust if, based on experience, this seems wise.  Now or later, you can see how basic cycles (to Generate-and-Evaluate, in Stages 1 & 2a) operate within broader cycles (to Plan-and-Monitor, in Stage 2b) by learning more about Stage 2b.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

• Stage 3 — Design Cycles and Science Cycles

When you compare Diagrams 2a and 2e` you'll see that 2e includes five new concepts:  two Design Cycles, a Reality Check, a Science Cycle, and "doing a Mental Experiment using Model + Logic".   But what is meant by "new"?  These concepts describe old thinking-and-actions that already were being done in Stages 1 and 2a.  But now they are being newly described, in a different model within...

A Family of Related Models:  An introduction to the 5 Stages explains how in all stages the representations of Design Process — verbal (in this page-summary) and visual (in the right-side diagrams) — describe the same process of design.  The stages differ only in perspective (re: which aspects of the process are being emphasized) and in the level of detail for describing various modes of thinking-and-action.

Cycles of Design using Quality Checks is the focus of Stages 1 & 2a.  Interactions between successive Design Cycles occur, with Evaluation guiding Generation, when you Evaluate an Option — by asking, with a Quality Check, “in what ways do an option's properties (predicted or observed) differ from the desired properties that are defined as Goals?” — and then use this evaluative feedback to guide your Generation of Options in the next Design Cycle of Generate-and-Evaluate.

Diagram 2e shows, with blue arrows, two kinds of Design Cycles.  A creative Generation of Options can be guided by critical Evaluation of Options in two ways, by using Quality Checks based on the results of Mental Experimenting or Physical Experimenting.

Diagram 2e also has a Reality Check that compares Predictions—and—Observations.   As described in Diagram 3a, in a Reality Check (aka Model Check or Prediction Check) you compare Model-based Predictions (made in a Mental Experiment by using a theory-based explanatory Model plus If-Then Logic) with Reality-based Observations (made in a Physical Experiment by using Observation Detectors) to see how closely they match, to check the accuracy of Predictions (the Predictive Accuracy) for a Model-Option that could be used in a Model-based Explanation.  In a Reality Check, you compare “the way you think the world is” (according to your theory-based Model) and “the way the world really is.”  Reality Checks also can be used in a Science Cycle to revise a Model so it will be a closer match with Reality, so its Predictive Accuracy will improve.

Below 2e, Diagram 3a simplifies the bottom part of 2a/2e to focus attention on 3 key elements (Goals, Predictions, Observations) being used in 3 Comparisons:  two are the Quality Checks of Stage 2a, the Prediction-Based Quality Checks and Observation-Based Quality Checks that are used to evaluate the quality of Solution-Options in General Design;  the other is a Prediction-and-Observation Reality Check that is used to determine Predictive Accuracy, which usually is the main criterion used to evaluate the quality of Model-Options in Science-Design (aka Science), although other factors also are considered.

The triangular shape of 3a can help students avoid a possible misconception.  Teachers can use 3a (and 3b) to explain that Quality Checks on the left & right sides of Diagrams 2a & 2e use the same GOALS (not different Physical Goals & Mental Goals) for comparisons with PREDICTIONS & OBSERVATIONS.   {a shape-variation of 3a combines this "same goal" concept with the mental/physical parallels of 2a/2e and, later, 4a}

More about Science:  Diagram 3c (which is not part of the “basic progression” in Stages 1-4) shows details of Making-and-Using Predictions in the Hypothetico-Deductive Reasoning that is based on Reality Checks and is the logical foundation of modern science.  What are the relationships between a Model and Explanation and Hypothesis?

Cycles for General Design and Science-Design

Diagram 3b` combines ideas from 2e (using prediction-based & observation-based Quality Checks in two types of Design Cycles, using Reality Checks in Science Cycles) and 3a (using 3 elements in the 3 two-way comparisons that are Quality Checks & Reality Checks, and "avoiding a possible misconception" by visually clarifying that both kinds of Quality Checks, prediction-based & observation-based, use the same Goals), and 3b introduces new ideas:  Options can be "for a Solution [in General Design] or Model [in Science-Design]" and "doing a Mental Experiment using Model + Logic" lets us "revise Model?" in a Science Cycle.    { As explained above, "new" just means "newly described in a different model within A Family of Related Models." }

Earlier, I describe a family of models that all "describe the same process of design," differing only in perspective and in levels of detail.  If I had to choose one favorite perspective-and-level, it would be Diagram 3b in Stage 3.

3b shows Cycles of Generation-and-Evaluation for two kinds of design – for General Design and Science-Design, which usually is just called Science.*  The top of 3b describes two kinds of objectives — "a Problem-Solution [in General Design] and a Theory-Based Explanatory Model [in Science-Design]" — so we can "GENERATE Options ... for a Solution or Model."    { terms for instruction: In school, activities that let students do General Design and/or Science-Design can be called Design-Inquiry and/or Science-Inquiry. }

During a process of General Design, a Design Cycle (completed by blue arrow-lines in 2e and 3b) occurs when GENERATION (symbolized by purple boxes) is guided by EVALUATION, when critical feedback from a Prediction-Based Quality Check or Observation-Based Quality Check guides your creative revision of a Solution-Option (for a product, activity, or strategy) in creative-and-critical Guided GENERATION.   /   Diagram 3b shows two kinds of Design Cycles, on the left & right sides, initiated by considering the results of either a mental or physical experiment.  But in either case, usually “all things are considered” in multiple Quality Checks (mental & physical, old and new) when determining a Quality Status for an Option, so in practice both "kinds of Design Cycles" are used in quick succession with iterative Design Cycles of Generation-and-Evaluation in which all available information is used.

During a process of Science-Design, a Science Cycle (completed by yellow-green arrow-lines) occurs when GENERATION (symbolized by a purple box) is guided by EVALUATION, when critical feedback from a Reality Check is used to guide your creative revision of a Model-Option (an option for a theory-based explanatory Model that is used in a Mental Experiment to make Predictions) in creative-and-critical Guided Generation.  The concept that Science-Design is a special type of Design — so it uses Design Cycles, in addition to Science Cycles — becomes easier to understand when we recognize that scientists often consider many evaluation-criteria (not just Predictive Accuracy) when they are evaluating a theory.

Both kinds of cycles (the two Design Cycles used for all design, plus the Science Cycles used for science) are shown in Diagram 3b because both can be used (and often are used) in each type of design project, with crossover thinking-and-actions that occur because "the improvised thinking-and-actions of creative designers... are not confined by the main objective, so in a project for General Design a designer sometimes does Science in Cycles of Science, ... and during a project for Science a designer sometimes does General Design in Cycles of Design" and because a scientist considers multiple factors when evaluating a theory-based Model.

In 3b, two features — a "?" in each purple box, and the dashed lines — indicate that both cycles (for Design & Science) are optional, because you can decide (after you evaluate an Option in a Quality Check or Reality Check) whether or not to revise this Option.

And near the top of 2e, two arrows (large ↓, small ↑) show that although five actions (choose Objective, define Goals, Prepare, Collaborate, Communicate) usually are done at the beginning of a Design Project, their timings are flexible so any of the actions (especially Prepare, Collaborate, Communicate) also can be done later.

Multiple Reality Checks → Status of Models:  Similar to using multiple Quality Checks to Evaluate many Options, you Evaluate many competitive Models by combining evaluations (old and new) from multiple Reality Checks that compare many Predictions & Observations (from many Experiments) with each other — so “all things are considered” — for each of the Models.

Connections with Other Models:  Cycles of Science also appear in other models for Science Process, explicitly in Learn By Design and implicitly in Predict-Observe-Explain, and in most other models.  These similarities will make it easier to connect Design Process with the research of other educators and the work of other curriculum developers, and apply it for designing instruction.

• Stage 4 — Deeper Understanding (+ Experiments) 

This stage, building on the foundation of Stages 1-3, will help teachers & students develop a deeper understanding of Design Process.

You can more easily compare three related diagrams — 2e and 3b (used in Stage 3) plus 4a — in another diagrams-page`.*   Stage 4 features Diagram 4a, which is complex but is easy to understand if you study it one part at a time.  4a introduces one new idea ("Design an Experiment" in the central box) and it expands a verbal/visual description for two kinds of design (on the left & right sides of 4a) that began in Stage 3.

* Or, to make side-by-side comparisons of any diagrams you want, scroll the left & right frames for 2e-3b-4a-4b-4bi` or 1-2a/2e-3a/3b/3bi/3c-4a-4bi` or 1-2a/2e... + Stage 2b` or only diagrams for Stage 2b`;  and with a right-click on these links, you can open the left-and-right in a new tab or new window.

Two Cycles of Generation-and-Evaluation

As explained in Stage 3 (using Diagram 3b) and shown on the left & right sides of Diagram 4a`, in creative-and-critical Cycles of Design you GENERATE-and-EVALUATE ideas for Solution-Options in General Design and for Model-Options in Science-Design (i.e., in Science).

For another visual perspective on these old ideas, Diagram 4a shows how:

    in General Design your creative GENERATION of Solution-Options can be guided by critical EVALUATION from Quality Checks.  This guiding is symbolized by upward blue arrow-lines that connect Evaluation with Generation to complete two Design Cycles, using feedback from either a Prediction-Based Quality Check or an Observation-Based Quality Check.   /   An advantage of 4a, compared with 3b, is that 4b shows a single Design Cycle (not
    in Science-Design your creative GENERATION of Model-Options can be guided by critical EVALUATION from Reality Checks.  This guiding is symbolized by yellow-green arrow-lines that connect Evaluation with Generation to complete a Science Cycle.  This same Science Cycle also is shown in the center (as in Diagram 3b) with a yellow-green line pointing from Reality Check to "revise Model?"  The " ? " shows that sometimes, but not always, you revise a Model so its Predictions (made in a "Mental Experiment using Model + Logic") will more closely match the known Observations, because a better match between Reality-based Observatons and Model-based Predictions improves the Predictive Accuracy of an Explanatory Model in a Reality Check.

Along with this creative-and-critical Guided Generation you can GENERATE Options (for Solutions or Models) in other ways that include Free Generation and Revision by Creative Analysis.  The wide variety of creative-and-critical ways to Generate-and-Evaluate, during a Design Cycle or Science Cycle, are symbolized by "analyze (compare-and-revise)" on the left & right sides of Diagram 4a.    {more about critical-and-creative analysis}

Design an Experiment

During any process of design you can use Guided Generation for Three Kinds of Options, for Solutions & Models, plus Experiments.  You EVALUATE Options (for Solutions or Models) "by using information (Predictions & Observations) from Experiments," so an important part of Design Process is to Design an Experiment, which Diagram 4a` describes in a new centrally-located box.

Design an Experiment has the same left/right split (for the two types of design) as in the boxes for GENERATE and EVALUATE, with Solution-Options (for General Design) on the left, and Model-Options (for Science-Design) on the right.

You Generate-and-Evaluate Options for Experimental Systems — guided by evaluative feedback from Quality Checks and/or Reality Checks, as indicated by the blue and yellow-green arrow-lines — so you can choose an Experimental System.  On the left side, in General Design an Experimental System is an Option-in-a-Situation (a Solution-Option operating in a Situation, an "operation-Situation for a Solution-Option").  On the right side, in Science-Design an Experimental System is a Model-Using Situation (a Situation in which a Model-Option could be used in a Mental Experiment to make Predictions, an "application-Situation for a Model-Option").    {more about Experimental Design}

Below this you "imagine" or "actualize" to run a Mental Experiment or Physical Experiment, with both possible for either type of design, because although above here the left/right split is for General Design & Science-Design, below (in the large light-blue box) the left/right split is for Mental & Physical experimenting.

Experimental Design is usually a sub-objective that helps you achieve your main objectives of designing a Solution or Model.  But sometimes, especially in Science, designing an Experiment (which is a special kind of activity) is the main objective.  In either case, whether as a sub-objective or main objective, experiments — when we design, do, and use them — are the central core of a design process.  Experiments (mental & physical) are very important in the practical everyday work of designers.

Sequences in Design Process? — No and Yes

Why is it "No and Yes"?  Because during a process of design by experts, there is no step-by-step sequence of actions that is rigidly followed or uniformly used (therefore it's No) but (Yes) some combinations of actions do occur.  I want to describe this process accurately, so Design Process is not a rigid sequence (No) but (Yes) it "does describe short-term sequences of actions and explain how these sequences are flexibly-and-skillfully coordinated by expert designers."*  No and Yes are both important, the "no" for knowing what Design Process IS NOT and "yes" for understanding what it IS.

Earlier in Stage 4 you've seen the main short-term sequences:  you design experiments so you can do them to make Predictions (in Mental Experiments) and Observations (in Physical Experiments) that you use in the Quality Checks & Reality Checks of Design Cycles & Science Cycles.

* How are sequences "flexibly-and-skillfully coordinated"? 

At the top of Diagram 4a you "Make Decisions..." based on evaluative feedback (shown by blue and yellow-green dotted lines) from all sources, from multiple Quality Checks & Reality Checks and more.  This evaluation-information is used for Guided Generation (in cycles of Design & Science) and also in other ways:

At the beginning of a Design Project, you "Make Decisions about... Design Project" by Choosing an Objective and Defining Goals for a Solution.  Later, during the process of design you "Make Decisions about Design-Actions" by using Metacognitive Awareness plus Coordination Strategies.  And eventually you "Make Decisions about... Design Project" by deciding whether to accept one option as a satisfactory solution (or explanation), or to continue the project, or to delay, abandon, or revise it.

Visual Simplicity

Diagram 4b` is like 4a, except the top part ("Make Decisions...") is gone, the box for Experimental Design is simplified, and "analyze (compare-and-revise)" is gone so it's easier to see the Design Cycles and Science Cycle that are key features of Design Process.  I think 4b is more elegant and artistic.  Teachers can first use 4a because its “extras” call attention to actions (Making Decisions, analyzing) and details (of Experimental Design), and then shift to 4b whose relative simplicity allows a more effective focusing on essentials.

Or you may prefer 4b⁺ (which is 4b + analyze) to remind students about this important kind of design-thinking.

Below 4b/4b⁺ is a 4b-isolation.  The visual simplicity of isolation diagrams will help students "learn how to interpret a whole diagram and recognize parts of the whole that form a Design Cycle, or Science Cycle, or another sub-process within a whole process of design... and develop metacognitive coordination strategies" for making decisions about design-actions.   {to see 5 isolations for Diagram 4b click the 5 top-of-page boxes}

Below are some comments about the visual representation of cycles in Diagrams 3b and 4a & 4b, which are easier to compare in this page` because it omits Diagram 3a.

Diagrams 4a & 4b show a Science Cycle twice, with an arrow-line on the right side, and in the middle when a Reality Check stimulates you to ask "revise Model?"

Diagram 3b uses arrow-lines to show a Design Cycle on the left side (using feedback from a Prediction-Based Quality Check) and right side (using an Observation-Based Quality Check).  In 4a & 4b these merge to form one arrow-line for "Design Cycles" as a reminder that ideally, when you're evaluating competitive Options, "you combine multiple Evaluations" so "all things are considered" including all Quality Checks, both Mental and Physical.

• Stage 2b — A Broader Cycle

Stage 2b uses Diagram 2b` to explain how the basic design-cycles of GENERATE-and-EVALUATE (featured above in Stages 1, 2a, 3, and 4) operate within larger design-cycles of PLAN-and-MONITOR in which we "learn from experience."  How?

If your objective is to develop a strategy, you first make a PLAN for what to do.  Then you MONITOR (you do the PLAN and observe what happens).  Using this experience, you adjust (if you think it will help) when you PLAN for "what to do" the next time, when you will MONITOR (do-and-observe), ... and you continue using these Cycles of PLAN-and-MONITOR.

Or, using terms from Stage 2a:  To PLAN, you assign a Quality Status for each Option — by mentally considering all available evaluation-information (old and new) from multiple Quality Checks (Mental & Physical, by comparing Predictions & Observations with Goals) for all Options — and you CHOOSE an Option to use for a Physical Experiment.  To MONITOR, you do a Physical Experiment (by using the Option in a Situation) so you can make Observations that will be mentally considered when you Generate Options to begin a new design-cycle of PLAN-and-MONITOR.

Or, looking at cycles-within-cycles:  during PLAN you mentally GENERATE-and-EVALUATE in Design Cycles (using all evaluative feedback from all Quality Checks, Mental & Physical, old and new) so you can CHOOSE an Option for a Physical Experiment in which you MONITOR (do-and-observe) so your new Observations can be used when you re-PLAN (in Design Cycles of GENERATE-and-EVALUATE, then CHOOSE) during the next cycle of PLAN-and-MONITOR.

All of the thinking-and-actions in Stage 2b already are in Stage 2a.  There is nothing new.*  Stage 2b is just a useful way to think about how we coordinate Mental Generating-and-Evaluating + Choosing (during PLAN) with Physical Experimenting-and-Observing (in MONITOR).

The perspective of Stage 2b — with two interactive cycles, with cycles of GENERATE-and-EVALUATE operating inside broader cycles of PLAN-and-MONITOR — is especially useful for the process of designing strategies, including metacognitive Strategies for Learning-and-Performing.  But other types of Design Projects also use a similar process of design (*) that we can understand more accurately-and-thoroughly by using this “two cycles” perspective.

 ▓  Science is Design    (into left frame`)

I recommend reading the revised version of this page-summary.

Design includes General Design and Science-Design (i.e. Science).

Different Objectives:   In these two types of design, the problem-solving objective differs;  it's an experiment and/or explanation in Science-Design (trying to solve a knowledge-problem by improving our understanding);  in General Design (trying to solve a problem by improving some other aspect of life) the objective is a product, strategy, or activity.

Similar General Objectives:   In both types of design, the objective is to solve a problem by recognizing "an opportunity to make things [our understanding, or another aspect of life] better," and then designing a solution that will make it better.  But it's convenient to use different terms to distinguish between the main objectives in Science (when you want to answer a question by designing an Explanatory Model) and in General Design (when you want to solve a problem by designing a Solution).

Similar Process:   In both designs, the process is similar in most ways because each uses the same basic process of design by creatively Generating Ideas, and critically Evaluating Ideas using Quality Checks.  But a special kind of Quality Check (using Reality Checks) is the logical foundation of Science.

Crossover Thinking-and-Actions for Objectives & Process:   Whether a project's main objective is General Design or Science-Design, sub-objectives may involve the other type of design;  or a main objective can include aspects of both General Design and Science.   Then during a process of design, trying to achieve this objective, the improvised thinking-and-actions of creative designers (as individuals and in groups) are not confined by the main objective, so in a project for General Design a designer sometimes does Science in Cycles of Science, as explained in Stages 3 and 4 of a Teaching/Learning Progression;  and during a project for Science a designer sometimes does General Design in Cycles of Design for sub-projects or spinoff projects, or to solve process-problems that occur during a project.

Objectives of Science:  The overall objective of Science is to improve our understanding of nature by designing experiments (to increase our knowledge about “what happens”) and designing explanations (for “how-and-why things happen”).

Comparisons, Bridges, Natures

Using Comparisons for Education:  We can use Design Process as a logical framework for comparing General Design and Science-Design,* to help students understand their similarities (these are opportunities for transfer), interactions & overlaps, and differences.     (*and comparing sub-fields within each)

Bridges between Design and Science:  We can teach principles of Design Process (in the context of experience & reflection & discussion) to build educational bridges between General Design (in Engineering & other areas) and Science, to help students achieve important goals for Science & Engineering Practices in the new Science Education Standards which include understanding...

The Natures of Engineering and Science:  The logical organization of Design Process — as in a creative coordinating of mental-and-physical experimenting (described in Stages 2-4) — can be used for verbal-and-visual instruction to help students understand the Nature of Engineering and Nature of Science.  As part of this, students will learn to appreciate the connections between Concepts and Practices of Science, with the Concepts of Science being developed by asking-and-answering questions using the Practices of Science, which is using Science Process:

 ▓  Science Process – an Overview    (into left frame`)

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

The main goals of science are to learn more about nature and improve our understanding of nature.  How?  By using a process of thinking-and-actions that I'll call Science Process.

This overview can be shorter than my Overview of Design Process because Science is a special type of Design, and Science Process is a special type of Design Process.  Therefore, this overview just supplements the overview of Design Process — in which you creatively generate options, and critically evaluate these options by using Quality Checks, with quality defined by your goals for a solution — by describing what is "special" about Science Process.  We'll do this while looking at two objectives — designing Experiments (to learn more about nature by observing it) and designing Explanations based on theories & models (to improve our understanding of nature) — that help us pursue the main goals of science.

Designing Experiments

Although in science the long-term main objective is designing Explanations (based on theories & models), the sub-objective of designing Experiments is very important in the everyday lives of most scientists.

Goal-Criteria:  In science you want an experiment to be scientifically useful, to provide information — Observations (aka Data) and Predictions — about “what happens” in the part of nature you are studying, because this will help you design Explanations about “how-and-why it happens” to improve your understanding.

More:  The main section about Designing Experiments (Mental & Physical) begins with a reminder of why you design experiments, so you can do them and use them.  Then it has a brief summary, and links for "MORE" where you'll find many ideas to stimulate Creative-and-Critical Thinking in Experimental Design.

Experimental Design as a Main Objective:  Usually designing an Experiment is a useful sub-objective that will help you achieve your main objective.  Occasionally, Experimental Design is the main objective, and this can be "very important in everyday lives" because it's a common focus when writing grants for funding a science lab.

Designing Explanations — How do we evaluate Quality?

This section describes how, in Science-Design, we use Quality Checks (as in General Design) to evaluate the Quality of a Model-Based Explanation.

We want our Explanations (and Explanatory Models, or Explanatory Theories)* to be plausible and useful, to have Plausibility and Utility.    { Actually, we want an Explanation to be correct — to be true because it corresponds to reality — but being plausible (seeming likely to be correct) is all we can claim with a logically justifiable confidence. }

* Explanation, Model, Theory:  When we design an explanation for “what, how, and why”, we can generate-and-evaluate options for explanations and for models, theories, and hypotheses, which are closely related because "all are [used in our] human efforts to describe-and-explain."  Therefore, this website often will describe the designing (the generating-and-evaluating) of explanatory theory-based Models because this is an essential part of the process when we design model-based Explanations.   /   In addition to their interest in Observations from Experiments, scientists usually think-and-communicate about Theories and theory-based Models, not Explanations.  But I'll include Explanations in this website because they are emphasized in NGSS, the new k-12 Science Standards.

Evaluating Plausibility:  Most scientists have decided — based on The Logical Foundations of Science — that the best way to estimate the plausibility of an Explanatory Model is to evaluate its Predictive Accuracy with "Mental-and-Physical Reality Checks (aka Model Checks or Prediction Checks) by comparing Model-based Predictions* with Reality-based Observations."  Reality Checks are used in a special kind of Quality Check to evaluate the Quality of Predictive Accuracy for a Model, to see how closely “the way things are in your thinking” (when using this Model to predict) matches “the way things are in reality.”   /   * We make Predictions in a two-step process by mentally constructing a System-Model, and using If-Then Logic.

Evaluating Plausibility-and-Utility:  When evaluating an Explanatory Model by using Quality Checks, usually the most important goal-criterion (used to define the Quality of a Model) is its plausibility that is evaluated mainly by using Reality Checks to determine the Predictive Accuracy of a Model, but also in other ways.  In a broader evaluation of plausibility-and-utility, scientists (and other designers, which includes all of us)* also consider other goal-criteria for a Model:  its empirical Predictive Contrast (with other competitive models);  its internal characteristics, and external relationships with accepted models;  its cognitive utility (to stimulate creative-and-critical productive thinking) and its related utilities for education, scientific research, and achieving personal goals.  To evaluate all model-options, we use these goal-criteria in Quality Checks to estimate a Quality Status for each model, and then decide which model(s) provide the best explanations (for "what, how, and why") for a particular Experimental System, in a process of evaluating-and-deciding.   {more about other goal-criteria}

* I'm defining "scientists" broadly, because everyone uses theory-based explanatory models in all of life, not just in science.

Quality Checks in Science-Design

In both kinds of design "the process is similar in most ways" because Quality Checks are the focus of action in all design, so Science-Design uses the two types of Design Cycles (based on Prediction-Based & Observation-Based Quality Checks) and also Science Cycles that use Quality Checks in a special way: "the logical foundation of Science ... is a special kind of Quality Check" that, as described above, uses Reality Checks "to evaluate the Quality of Predictive Accuracy for a Model."  But even though Predictive Accuracy is "usually the most important goal-criterion (used to define the Quality of a Model)," scientists "also consider other goal-criteria."  When scientists consider all goal-criteria, not just Predictive Accuracy, their use of Quality Checks — for the purpose of determining how closely a Model-Option matches all of their Goals for a Model — is similar to the way multiple Quality Checks are used in General Design, to estimate the Quality Status for a Model-Option based on evaluations of its overall Quality "when all things are considered."

Two Strategies for Evaluation in Science:  Diagrams 4c and 4d` show the process of design that scientists use if they consider only Predictive Accuracy (in 4c) or if they also consider other goal-criteria (in 4d).  In 4c, typical Quality Checks (not the "special" Quality Checks based on Reality Checks) are absent.  In 4d when a wide variety of goal-criteria are being considered, many different goal-criteria — Predictive Accuracy (evaluated in Reality Checks) plus "Predictive Contrast,... internal characteristics and external relationships,... cognitive utility" and more — are being used when Goals (for the desired properties of a Model, for its Predictive Accuracy, Predictive Contrast,...) are compared with Obervations (of observed properties) in an Observation-Based Quality Check, or compared with Predictions (for predicted properties)* in a Prediction-Based Quality Check.

* A scientist could use "predicted properties" as a basis for evaluation during a Guided Generation of new Model-Options, to predict the properties of a Model-Option that has not yet been observed “in action” with Reality Checks or in other ways.

note:  Above 4c is a related diagram, a "transition-to-4c" with gray-shading to show parts of the entire diagram (4b) that are not used in Science-Design when Predictive Accuracy is the only goal-criterion being considered.  To convert 4b into 4c, these gray-shaded parts were deleted.

also: my original model of Scientific Method (with diagrams that show 9 aspects of science) was developed as part of my PhD project.

 ▓  Design Process — what it IS and IS NOT    (into left frame`)

I recommend reading the revised version for the SHADED PARTS of this page-summary.

An accurate understanding of Design Process requires knowing what it is and also what it isn't.  Design Process is not a rigid sequence of steps.  Instead it's a flexible framework (outlined in an Overview of Design Process) that can help students master the typical thinking-and-actions used by expert designers when they solve problems, in a process "analogous to a hockey skater's goal-directed structured improvising ... but not the rigid choreography of a figure skater."  When we ask “Is there a method?”, why is the best answer No-and-Yes?

Will teaching Design Process be beneficial for students? (YES)   This question is more important, and the answer is YES if Thinking Strategies for metacognitive coordination (like those in Design Process & Science Process) will help students "coordinate their thinking-and-actions in productive ways."  Reasons to think this will occur are summarized in why we should teach Design Process.

Partially-Examined Stereotypes about Models-for-Process:  Productive educational uses of models-for-process, including Design Process, can be hindered by inaccurate stereotypes (misconceptions) of “what a process-model must be,” based on past experiences with simplistic models that are overly rigid.  This is why some educators — motivated by their noble desire to avoid a restrictively rigid “method” for problem-solving process — describe models in ways that could be interpreted as implying that all instruction should use no model or a semi-model.

But these implications seem rare when we fully examine the criticisms.  Or at least they should be rare, because we should not give up too easily.  Instead of just thinking “some previous models were not educationally useful, so let's give up” we should try to do better, and that has been my goal in developing Design Process.  One reason for refusing to say "let's give up" is that what educators don't like (an oversimplistic rigidity in models-for-process) is what Design Process is not, as explained below.

Non-Rigidity and Non-Uniformity:  When we observe expert designers, we do not see a series of “steps” in a rigid sequence (because an expert's process of design is not rigid) that is used by all designers in all situations (because their process is not uniform).  Both of these — not rigid, and not uniform — are essential characteristics of Design Process.

Design Process is not rigid.

Design Process does describe short-term sequences of actions and explain how these sequences are flexibly-and-skillfully coordinated by expert designers when — using coordination strategies based on metacognitive awareness (of the current situation) and conditional knowledge (about how to make progress) — they make action-decisions about what to do next.

The flexibility of Design Process is analogous to an expert hockey player's process of goal-directed structured improvisation, guided by a strategic action-coordinating plan that is intentionally flexible, open to real-time adjustments in response to an awareness of what is happening during a game.  In hockey and design, the objective is to develop adaptive expertise.

"By contrast, Design Process is not similar to the rigid choreography of a figure skater. ... It is not a rigid pathway to follow, but it does provide an overview-map of ‘process’ that shows possibilities for creatively rational wandering," and it helps students develop effective strategies for coordinating their action-decisions during a process of design.

Semi-Rigid and Semi-Uniform:  Do we use sequences during a process of design?  No and Yes.  We say "no" because each process of design is different (is not uniform) and is not rigid, so a model for Design Process is not rigidly uniform.  But it's "yes" because during design we do use sequences in ways that are flexibly improvised (not rigidly followed) and (due to non-uniformity) vary from one design project to another.    Do designers use sequences? - No and Yes

Design Process is not uniform.

A model of Design Process can be used to describe many types of design & views of design, and for many teaching strategies.

Many Types of Design:  We use a process of design for almost everything we do in a wide range of design-fields.  Design Process describes a basic process that is used with variations in different fields, among groups within each field, and from one project to another.  A “typical process of design” also tends to change over time.   {process & skills - general and field-specific}

Many Views of Design are held by scholars who study design, and by teachers, about how to interpret the nature of design and the process used by designers.  {The Nature of Design-and-Science}

Many Strategies for Teaching:  The flexibility of Design Process lets teachers use it in ways that are personally customized for their own situation (re: level of students, culture of school & community,...) and educational philosophy (re: goals for students, types of instruction,...).

Model = Framework + Supplements:  In a personalized Strategy for Teaching, a teacher can describe many Types of Design and many Views of Design, using only one model of Design Process.  How?  By combining its one framework (for actions) with many different supplements that differ in their descriptions of the framework's action-components.   The framework of any model describes its essential characteristics;  the non-essential supplementations can vary.   {non-essential does not mean not-important, as in this example from d.school}

For example, in this website the main pages for describing the framework are overviews of Design Process and Science Process.  But all pages include supplements, especially for explaining how to design more effectively (as in tips for “how to do it better” to improve creative-and-critical Productive Thinking in 10 Modes of Action), but also for other purposes.

Customized supplementations (and customized applications for instruction) give Design Process pedagogical flexibility, so it can be useful in Many Strategies for Teaching.

You may find it useful to think about different supplementations as being analogous (in some ways) to variations on a theme in music.

Perspectives about Process:  In a very important kind of supplementing, Design Process is a family of models (in a 5-stage progression for learning) with each model showing the same process "from different perspectives and with increasing levels of detail."  This allows Flexibility in Teaching, because teachers can decide which model(s) to use, when and how.  And they can supplement with details about analytical thinking skills used by designers, such as those described in the new Standards for Science Education.

Perspectives of Fields:  Another kind of perspective is illustrated by my original model of Science Process, which was a unifying synthesis of ideas about science, gathered from scholars in a wide range of fields.  This model, when used in supplements with different emphases, can describe “the nature of science” from the perspectives of different fields (science, philosophy, history, sociology, psychology, education), and of individual scholars (and the sub-communities they form) within each field.   {examples of different perspectives}

Views about Science:  Science is a special type of design, a distinctive variation that emphasizes some components of design-thinking.  Therefore, we can describe Science Process in terms of Design Process, which can be used as a framework for comparing General Design (or sub-areas like Engineering) and Science, to help students understand their similarities and differences (in objectives & process), opportunities for bridges & transfers, and the relationships of Science, Technology, and Society.  Every teacher can describe their own view of the relationships between Science and General Design.

Descriptive and/or Prescriptive:  One variable in Views of Design is whether the intention is descriptive (to describe how design IS done) or is also prescriptive (to explain how design SHOULD BE done).  My model for Design Process is mainly descriptive, which is one reason for my claim that the scope of design is extremely wide so it includes "almost everything we do in life."  But much of this website is prescriptive, with supplementary recommendations for how to more effectively do design and teach design.  I think this approach — beginning with what IS, and moving toward what SHOULD BE — is a useful strategy for teaching.    Philosophy of Science - Descriptive and Prescriptive

{ full page for What Design Process IS NOT }

 ▓  Motivations for Learning    (put into left frame`)

I encourage you to read the first part of the full page`.  Here is a brief outline of its ideas:

    students who believe that Learning in School is Learning for Life (so they can achieve their goals for life) with Personal Education (by adopting a proactive problem-solving approach to Learning for Life, trying to “make life better” by improving their personal knowledge of ideas-and-skills);
    students want Enjoyable Activities (that are "intrinsically interesting, challenging in a good way, and fun") plus long-term satisfactions;
    Educational Teamwork with Matching Goals occurs when teachers' goals match students' goals;  a teacher can achieve better matching by adjusting to students, and persuading students;
    in effective Motivational Persuasion we consider all aspects of total motivation – intrinsic, personal, interpersonal, and extrinsic, all hopefully based on good values & priorities – that contribute to how a student thinks about their strategies-and-actions aimed at getting “what they want” in their whole life as a whole person, then we use words and actions to persuade students that we have good intentions (we care for them and are trying to help them improve their lives) and we are competent (in defining worthy educational goals, and helping students achieve these goals), and with words-and-actions we share our enthusiasm for the joys of thinking-and-learning.

Moving Beyond Simple Motivation

Perceptions of Self - Accuracy plus Optimism 

The second part of the Motivations-Page` begins by asking “What should we do if a student is motivated, but doesn't feel confident about their ability to succeed?”  We can help students develop accuracy (in self-perception) plus optimism (about their potential for improvement and growth) with a “not yet” attitude toward failure so they can learn from all of their experiences and “do it better” in their future.

This optimism is easier if students have an incremental theory of intelligence — believing that their intelligence, and intellectual performance, can be improved, can “grow” through their efforts — because an "incremental" view-of-self promotes a confident belief that their efforts to self-improve (as with personal education for life) will be rewarded. {This claim is supported by recent research.}   One way to encourage an "incremental growth" view of intelligence is with a brain-as-muscle analogy, by explaining how our muscles and brains both improve when they are used, to help students develop a growth mindset. (videos for students - by Carol Dweck)

The self-perceptions of a student will affect their performing and/or learning for knowledge that is conceptual and/or procedural (for ideas and skills and skills-with-ideas), is mental or mental/physical.    {more about self-perception}

Diversity and Equity

Promoting Diversity:  Due to the diversity of students (in abilities, experiences, and motivations) some students will succeed in design/inquiry activities, beyond their success in traditional school activities.  The intellectual-and-emotional rewards of this success — for a wider range of students — will improve their self-image and their motivations for learning, if they begin to see their schoolwork as part of a personal education that is motivated and guided by their pursuit of personal goals for life.

Equity in Education:  Providing a wider range of opportunities for success-and-satisfaction can be especially helpful for students (such as girls, minorities, and poor) who traditionally are under-represented in STEM fields like Science and Engineering.  This expansion of opportunities, to improve equity in education, is one reason (among many) to use eclectic instruction that includes Design Activities for Inquiry (with Argumentation) and Thinking Strategies.

Building Transfer-Bridges:  We can help a wider range of students take advantage of their opportunities, in school and in life, by Building Educational Transfer-Bridges (from life into school & back into life, and between subject-areas) that will improve their confidence about learning and motivations to learn and transfers of learning.  To improve diversity & equity in STEM (Science, Technology, Engineering, Math) we can use a 5-step progression of building bridges.

Two different reasons to avoid investing effort in Personal Education — when a lower-performing student thinks “why bother? it won't help me,” and a higher-performing student thinks “why bother? I'm doing fine so I don't need it” — require different approaches by teachers.  (and by others who care: counselors, coaches, parents, classmates,...)

How can we motivate students who think "why bother?"  In the full page, Motivational Persuasion (Part 2) begins by stating the obvious, that "dedicated teachers [and others who care]... try to help more students decide to invest more of the intelligent effort (by working smart and working hard) that will help them improve their learning, performing, and enjoying."  Then it looks at strategies — customized for each "reason to avoid investing effort" and for individual students within each group — for using eclectic instruction, and for pep talks to “be all you can be.”  (also, Thinking Strategies can produce a cycle of mutual support)   And it ends by considering Two Kinds of Personal Benefits for a student, by Using Education to Help Yourself and Help Others.

{ full page for Motivations to Learn }

A Variety of Options:  The objectives of design include almost everything students do (in life and school), so teachers of any subject, with students of any age, have a wide range of options for Design Activities that are Thinking-and-Learning Activities for ideas-and-skills.  This wide scope makes it easier to find Design Projects that are life-relevant, that will be fun now and useful later for students, who thus will be more motivated to think-and-learn during their design activities in school.

Choices by Students & Teachers:  The wide scope of design thinking lets activities be designed by students (because they will “do design” in any activity they are motivated to choose, although with some activities they can learn more than with others) and by teachers who carefully design goal-directed Aesop's Activities.

Inquiry Activities

What is inquiry?  Opportunities for inquiry occur when a deficiency of knowledge — in conceptual knowledge (so students don't understand) or procedural knowledge (so they don't know what to do, or how to do it) — stimulates thinking, and students are allowed to think-and-learn on their own.  But students won't be "on their own" for too long, becoming too frustrated, if inquiry experiences are part of an eclectic mix.

Two Types of Inquiry:  In two kinds of design the problem-solving objectives — where a problem is defined broadly as "any opportunity to make things better" — are improving our understanding in Science-Design (i.e. in Science), and improving some other aspect of life in General Design.  Although in both types of design the objective is to solve a problem, it's convenient to describe the main objectives as asking questions & seeking answers (in Science), and defining problems & seeking solutions (in General Design).  Therefore, we can design two types of Learning Experiences:  in Design-Inquiry students solve problems with General Design (which includes engineering);  in Science-Inquiry they answer questions, to improve their understanding, with Science.  Because these two inquiries use a similar process of design — and due to overlaps because an engineer sometimes does science (why?) and a scientist sometimes does engineering — students will combine both types of inquiry in many activities, which thus become Design-and-Science Inquiry.  Due to these overlaps, we can build educational bridges from design-inquiry to science-inquiry, and vice versa.

Another Type of Design Activity

In addition to Inquiry Activities, students can use a process of design to develop cognitive-and-metacognitive Thinking Strategies that will improve the quality of their performing and/or learning when their objective is personal education to “make life better” by improving their own ideas-and-skills.

 ▓  Transfers of Learning    (put into left frame`)

I recommend reading the revised version for most parts (all except the ending) of this page-summary.

Based on principles of learning, we have logical reasons for expecting transfers of knowledge to be increased by teaching Design Process.

I recommend reading the revised shorter version of this page-summary.

Part 1 — Two Principles for Increasing Transfer

Transfer is important.  How People Learn` describes it as "the ultimate goal of learning" so it's "a major goal of schooling," and they recommend (based on research about learning) that to increase transfer we should:

A) teach knowledge in multiple contexts, as in a Wide-Spiral Curriculum with Educational Bridges from Life to School (with Design in all subject areas, including Science) and back into Life;  this is possible because design-thinking is used for almost everything we do in life.   When we teach principles of design-thinking in multiple contexts, "we can show how the problem-solving strategies in different areas are related to Design Process (because it's used for solving problems in each area) and thus are related to each other," by using logic that is analogous to the mathematical Transitive Relationship: if A=B and A=C and A=D, then B=C=D. (or in this case, with equalities replaced by similarities, B≈C≈D)

B) teach knowledge in a form that can be easily generalized, as with a model of Design Process that is constructed using general modes of thinking-and-action — Definition (of objectives & goals), Generation (of solution-options, and information about options) and Evaluation (of these options), with Coordination (of actions in a process of design) — that can be adapted for generalized use in all subject areas and in most areas of life.    { This generalizing of procedural knowledge allows it to be logically organized, which also increases transfer. }

Although "we have logical reasons for expecting transfers of knowledge to be increased by teaching Design Process," we also have reasons for appropriate humility when we are "deciding what kinds of conclusions [about transfer] are justifiable, and with what levels of confidence."

Part 2 — Metacognitive Strategies for Increasing Transfer

Teaching for Transfer:  Teachers can use a process of design to develop improved Teaching Strategies for promoting transfer.

Learning for Transfer:  An important Teaching Strategy is motivating students to develop-and-use their own Thinking Strategies and helping them do this more effectively.

These cognitive-and-metacognitive Transfer Strategies — with objectives for teaching & learning, viewed from the perspectives of teachers & students — are similar, so Part 2 will shift back & forth between the two perspectives.  Transfer Strategies are especially valuable because all learning involves transfer when people build new knowledge on the foundation of existing knowledge, as described in Constructivist Theories of Learning.

I recommend reading the revised shorter version of this page-summary.

Remembering-and-Transfering are closely related,* so strategies that improve a remembering of knowledge — in storing, retaining, and recalling — also will improve a transfering of knowledge, although some strategies-for-remembering are more effective for promoting transfer.

Transfer to Different Contexts:  * We call it remembering when the context-of-storing and context-of-recalling are very similar;  as this similarity decreases, the amount of transfering increases.

Transfer to Different Times:  A transfer of learning requires that you "remember" learning from the past so it can be used in the present, or from the present so it can be used in the future.

Past - Present - Future

Students (and teachers) can develop Thinking Strategies to improve their performance now and/or in the future.

Learning from the Past:  In the present, students often can improve their current transfer-performance by intentionally recalling for transfer — by asking “what have I learned in the past that can help me now?” — to help them recall ideas-and-skills knowledge they learned in the past, so they can use this knowledge.

Learning for the Future:  In the present, students often can improve their future transfer-performance by intentionally learning for transfer, in ways that will make their ideas-and-skills knowledge more easily available for personal use in the future, by asking “what can I learn now that will help me later?”

Both strategies are useful "often" but not always, because sometimes metacognition is a distraction that should be avoided, with effective regulation of metacognition.

Some interactions between timings and priorities — is your main objective to improve your performance now, or later? — are examined in Performing and/or Learning.

Here are three ways to apply strategies for connecting past, present, and future.

Reminding and Self-Reminding:  When teachers use guiding, one useful function is reminding students to think about what they already know.  A student who wants to be self-reliant, not dependent on external guiding, can do proactive self-reminding with a metacognitive strategy of trying to intentionally recall ideas or skills that have been useful in similar situations in the past, and thus might be useful now.

Reflection and Self-Reflection:  In one type of guiding a teacher promotes reflection to increase the awareness that puts knowledge into memory-storage so this knowledge will be available for recalling in the future.  Students can promote their own metacognitive self-reflection, with or without a conscious intention to learn.

Intentions to Store & Recall:  During a learning of knowledge, storage-in-memory is almost always improved by awareness, and often even more by awareness with intention to store-in-memory.  Later, retrieving this knowledge can occur by spontaneous recall (without conscious effort) or intentional recall (with self-reminding).

One useful view of how we "remember" during transfer distinguishes between two dimensions of transfer, backward/forward and low/high.

Developing Conditional Knowledge and Organizing Procedural Knowledge are two ways that teaching Design Process can improve transfers of Procedural Knowledge — in all types of design, both General Design and Science — for creative/critical thinking skills and for Coordination Strategies to combine these skills into a productive process of design:

• Developing Conditional Knowledge

When you try to choose productive design-actions, guided by a Coordination Strategy, you use Conditional Knowledge to find a WHAT-and-WHAT matching "between a recognized need (it's WHAT you want to do) and a capability (for WHAT you can do)."

When students learn a skill (or idea) they should know HOW to use it, and also WHAT it lets them do (WHY to use it) and WHEN to use it.   The why-and-when is their Conditional Knowledge:  they ask WHY (what are the skill's functional capabilities? what can I accomplish by using it? why might it be useful?) and WHEN (what are the conditions-of-application in which the skill is useful? what kinds of situation-cues will help me recognize these conditions?).  Students should ask these WHY-and-WHEN questions for individual skills, and for combinations of related skills.

Students can intentionally learn for transfer (for remembering-and-using the skill in new situations in their future) by creatively imagining that “if the situation is     or     or     during a process of design, then I can use this skill”, so in the future these situation-cues will be reminders to use the skill.  To build a wider scope-of-transfer, students can be flexibly creative when imagining multiple situations in which the skill might be useful.

A teacher should encourage & guide this “what and why/when” questioning, to help students improve their Conditional Knowledge for the ideas-and-skills they are learning, so in the future they will be able to recognize WHAT-and-WHAT matches in a variety of situations, for a variety of skills.

• Organizing Procedural Knowledge

Educational Benefits:  Research shows that a logical organization of Conceptual Knowledge leads to better understanding, transfering, and applying.  A logical organization of Procedural Knowledge, as in Design Process, should be similarly helpful for improving Conditional Knowledge and in other ways.

A simple way to illustrate the benefits of conceptual organization is by comparing three quizzes, with 22 meaningless letters, 6 meaningful words, and 1 interesting story.  For example, students can learn the meaningful “story” in Design Process gradually, a little more during each stage of a 5-stage progression for learning.  Because they are moving from simplicity to complexity with gradual steps of learning, even in the final stage any “feeling of complexity” will be reduced.

For example, in a 5-stage progression for learning moving from simplicity to complexity, even in the final stage any “feeling of complexity” will be reduced because you have been learning the meaningful “story” in a process of design gradually, a little more in each stage.

How People Learn describes how organization of knowledge improves transfer and is a foundation for expertise, including adaptive expertise that "is flexible and more adaptable to external demands," that uses metacognitive strategies to cope with new situations, and pursues lifelong learning to continually improve ideas & skills.  Regarding education, the authors wonder "whether some ways of organizing knowledge [and some kinds of learning experiences] are better at helping people remain flexible and adaptive to new situations."  I think these worthy objectives — to improve transfer and expertise, especially adaptive expertise — can be promoted by teaching Design Process, which is logically organized yet functionally flexible, so it encourages a combining of technical expertise with a flexible attitude, an ability-and-willingness to improvise.

We can use the organized framework of Design Process — with visual/verbal integration for mental-and-physical “parallels” used in Cycles of Generation-and-Evaluation (used for Design and Science) and for other functional relationships — to help students understand the integrated coordination of problem-solving skills within each design experience, and also between design experiences in different subject areas to increase the transfer between areas.

Another way to organize knowledge — by developing Conditional Knowledge so you know the functional capabilities of each idea & skill, so you know WHY to use it and WHEN — is explained above.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised and is now better — shorter in some parts, but longer (with more-and-better ideas) in other parts.

Education to Prepare for Life:  Schools cannot prepare students for every challenge they will face in a rapidly changing modern world.  But we can help them cope with new challenges by improving their problem-solving abilities so they can adjust by using the adaptive expertise they have developed, and by learning new ideas-and-skills when necessary.  As explained in ancient wisdom, “Give a man a fish and you feed him for a day. Teach him how to fish and you feed him for a lifetime."

Transfer of Design-Skills into Life:  We use design-thinking, which includes scientific reasoning, for almost everything in life.  This wide scope lets us build educational bridges to help students increase their transfers of learning — from life into school, across a wide range of subjects, and back into life — and improve their motivations to learn and their lifelong learning-performing-enjoying.

Transfer of Science-Skills into Life:  In all of life (not just in science) we use our personal theories about “how the world works” to understand “what is happening” and to imagine “what will happen” when we make predictions that, along with our values & priorities, can help us make wise decisions, personally and professionally, to help us achieve our goals in life.  In all areas of life we use the logic of science (to evaluate, infer, persuade) whenever someone makes a claim and we ask “what is the evidence-and-logic supporting this claim?”  Teachers can use this wide scope by combining Scientific Logic with generalized Critical Thinking in activities that include lively debates and analysis of Logical Fallacies.  This is one way to motivate a wider range of students so they will want to improve their skills with the Design Thinking they use for all kinds of STEM/STEAM Education and beyond, because critical evaluation of ideas is essential for skillful reading/writing & listening/speaking, and thus for many of the worthy educational goals in Common Core and Next Generation Science Standards.

Appropriate Humility, to Avoid Two Errors:  Improved scientific reasoning will promote the logically appropriate humility (with a confidence that is not too little, not too much) described by Bertrand Russell:  "Error is not only the absolute error of believing what is false, but also the quantitative error of believing more or less strongly than is warranted by the degree of credibility properly attaching to the proposition believed, in relation to the believer's knowledge."  An appropriate humility will help us avoid 2 of these 3 kinds of error.      {Accurate Understanding - thus appropriate humility? - and Respectful Attitudes} {The Rationality-and-Idiocy of Postmodernism}  {a revised-and-condensed version of Education to Prepare for Life with Transfer of Skills into Life when students improve their skills of creative-and-critical productive thinking that can be improved by using a wide variety of Strategies for Thinking taught using Strategies for Teaching}

{ shorter summary for Transfers of Learning, and full page }

 ▓  Cognition-and-Metacognition    (into left frame`)

In this page-summary:  Cognition and Metacognition, The Value of Feedback - Two Metacognitive Strategies - Learning and/or Performing - Regulating Metacognition.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised and is now better — shorter in some parts, but longer (with more-and-better ideas) in other parts.

Cognition-and-Metacognition

Metacognition:  Thinking is cognition.  When you ask “how can I think more effectively?” and think about thinking — to improve the quality of your thinking, learning, and performing — this is meta-cognition, which is cognition about cognition.

Cognition and Metacognition are closely related aspects of thinking, with mutually supportive interactions, so we can develop strategies for using cognition-and-metacognition together in synergistically productive combinations.  But sometimes it's useful to think about the distinctive characteristics of either cognition or metacognition, and how we can use each more effectively.  We can think about aspects of cognition-and metacognition: combining them into strategies (for design and education) by using metacognitive knowledge (general & personal) in cycles of Plan-and-Monitor (we monitor by doing & observing, to learn from experience), with on-and-off regulation of metacognition.

The Value of Metacognition is recognized by educators & psychologists, who describe why-and-how metacognition is valuable`.  One reason for this value is because...

Metacognitive Reflection is the essential strategy for learning from experience.  You do it whenever you remember “what happened” and think about it.  Usually, reflection helps you learn more from an experience, especially when learning (not performing) is your main objective.  And reflection is part of a teaching sequence (experience, reflection, principles) that is useful for learning many ideas-and-skills.  You can do reflection with or without using Design Process, and either way it usually is productive.  But in many situations using Design Process can help make a process of metacognitive reflection more effective.

Formative Feedback:  A skillful use of Formative Feedback (which includes Formative Assessment) is one of the most important aspects of effective teaching.  By helping students improve their cognitive/metacognitive skills, we can help them “learn how to learn” more effectively when they become more skillful in generating-and-using their own personally customized Formative Feedback.

Two Metacognitive Strategies — for Design and Education

Students can use a process of design, which is made more effective by knowing-and-applying principles of Design Process, to develop & improve two closely related kinds of cognitive-and-metacognitive strategies:

• Coordination Strategies are used to coordinate a process of design (for General Design or Science-Design) and make effective action-decisions about “what to do next” by combining Metacognitive Awareness (of the problem-solving process that is being used during a design project) and Conditional Knowledge (by knowing what each of your skills lets you accomplish, and the conditions in which it will be useful).

Educational Value:  In my opinion, helping students improve their Coordination Strategies (to coordinate thinking skills into whole-process skills) will be one of the most valuable educational outcomes of teaching Design Process.  But because Strategies to Coordinate a Process of Design are a special type of Strategy for Learning-and-Performing — so these strategies are very closely related — the most valuable outcome actually is helping students develop-and-apply better...

• Learning Strategies — with the goal of improving your quality of learning, thinking, and/or performing — are an important part of Personal Education.  Anyone can use a process of design (based on general & personal Metacognitive Knowledge) to develop improved Strategies for Learning (and/or Performing) for a wide variety of situations that include Coordinating a Process of Design.  The most basic Learning Strategy is using metacognitive reflection to learn more from experience;  reflection can be done with or without using Design Process, but often is more effective with it.

Teaching Strategies:  Using a similar process, teachers develop their own Teaching Strategies that include “external” metacognition (empathetic metacognition?)* by observing students while asking “what are they thinking? how? why?”  These observations help teachers when they ask, “how can I design instruction that will help students think-and-learn more effectively?” and “how should I guide students” by promoting their metacognitive reflection, asking or answering questions and giving tips to adjust the difficulty of inquiry activities, providing formative feedback, and in other ways.   /   * If self-metacognition is thinking about how you are thinking, then does analogous empathetic metacognition occur when you think with empathy about how-and-what others are thinking?

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

Learning and/or Performing and/or Enjoying

When you're doing an important job and you want the best possible performance now, you're on-task with a Performance Objective.

When you want the best possible learning now, so you can improve your best possible performance later, you're on-task with a Learning Objective (i.e. an Education Objective).

These two related objectives can differ in their priorities.  In the blend of performing and/or learning you want,* how much value do you place on present performance (short-term excellence) and/or improved future performance (for long-term excellence)?

In this "and/or", the "and" is possible when your short-term and long-term objectives are supportive, so you can achieve both objectives.  For example, focusing on peak performance now may help you learn how to improve performance in the future.  But sometimes trying to learn for the future will reduce your current performance, or (as explained below in "Fun and Satisfaction") focusing on short-term performance will hinder your long-term learning.  The "or" is a recognition that sometimes you cannot maximize both performance and learning, so you must set priorities by asking “Do I want to maximize performance now, or learning later, or an optimal combination of both?”

Short-Term Fun plus Long-Term Satisfactions:  * With a broader perspective on life, you want a "blend of performing and/or learning" and/or enjoying.  Often there is a strong link between performing and enjoying, as in competitions with self (by trying for “personal records” to be the best you can be) and with others, where you experience “the thrill of victory, the agony of defeat.”  This connection between performing-and-enjoying strengthens the direct appeal of maximizing performance-and-fun now compared with learning now so you can improve your maximum performance-and-fun later.  The simple appeal of wanting "fun now" is powerful, and it must be considered when we plan instruction that will motivate students to also want Learning for Life so they can increase their long-term enjoyment.  As an essential part of our instruction, we can develop Educational Teamwork and use Motivational Persuasion to help students shift their balance from only Short-Term Fun toward also Long-Term Satisfactions.  The tensions between motivations, and strategies for combining motivations, are examined in Tennis and Other Games.

When you use a process of design to develop cognitive-and-metacognitive Strategies your main objective can be to improve your Learning, or Performing, or some combination of Learning/Performing. (or Learning/Performing/Enjoying)   Therefore, we can call these Strategies for Learning, or Strategies for Learning-and-Performing, or Strategies for Performing-and-Learning.

For example, in most activities of life you can improve your performance, now and in the future, by using two general metacognitive strategies for transfer by:

    using past learning to improve present performance, perhaps by thinking “how can I do it better?” or “what have I learned in the past that will help me now?”, or maybe by not asking these metacognitive self-questions);*
    using present learning to improve future performance (perhaps by thinking “what can I learn now that will help me perform better in the future?”).
    * The benefits of sometimes avoiding "metacognitive self-questions" is one way to Regulate Metacognition.

The full-length section has brief illustrations from welding, basketball, classrooms, and tennis.

Distinguishing between learning and performing is useful for understanding the differences between formative feedback & summative assessment (as in a basketball practice & tournament game) and for deciding how you want to use metacognition of various types, and when, by developing-and-applying the very important skill of...

Regulating Metacognition

Sometimes you'll want to stimulate performance by using metacognition.  At other times you will “go with the flow” (don’t think about thinking, just think) to allow performance by avoiding metacognition.  You can make better regulation decisions about turning metacognition on and off — by deciding when to use metacognition (for improving the quality of your thinking-and-actions) and when to avoid it (so you can focus on what you're doing) — if you increase your general & personal metacognitive knowledge.  You also can stimulate learning by using metacognition, so you can improve your performance in the future.   {performing and/or learning}

As an example of metacognitive timing, do you want to observe your thinking-actions and learning-results during a lecture or afterward?

The full-length section, which I recommend reading, describes "thoughts about effective regulation of metacognition," about Opportunities for Metacognition (during interludes "when you're not deeply engaged in productive thinking-and-action"), Timings of Preparation-and-Production (illustrated by action-decisions while writing a paper), Using Intuition Wisely (should you trust it? maybe) and Avoiding Procrastination (are you avoiding a high-priority task because it's unpleasant? or it seems overwhelming?), Knowing Yourself (as a "personal" part of your metacognitive knowledge), Learning and/or Performing (usually metacognition is useful for learning, but sometimes not for performing), and Skillful Metacognition because "in situations where metacognition is unproductive — if there is too much introspection of the wrong kind or with the wrong timing — the difficulty is not metacognition, it's unskillful metacognition, due to a deficiency in skillfully regulating metacognition."

If "Actual Performance = Potential Performance – Distracting Interference",
then "you want to increase Potential Performance — which depends on Abilities (inherited & developed) and Preparation (for a particular Performance-Situation) — and decrease Distracting Interference.  To maximize Actual Performance, an effective regulation of metacognition... turns it on (when it will improve Potential Performance) and off (when it would be a Distracting Interference)."   /   This formula-for-performance is a principle of some strategies for using metacognition (with an "Inner Game" approach and in other ways) that are examined in Regulating Cognition-and-Metacognition for Optimal Performance.

{ my full page for Metacognition + links to other authors }

 ▓  Metacognitive Strategies for Learning    (into left frame`)

A Change of Terms:  In the shorter page-summary (which I recommend reading first) the term is changed from "Strategies for Learning" to "Strategies for Thinking" but (I.O.U.) the terms in this page-summary have not been changed.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter in most parts, but with new ideas in some parts.

WHY are these strategies beneficial?  WHAT can you achieve?  HOW can you develop strategies?

WHY

Strategies for Learning (to improve a skill) can increase the quality of your learning and/or performing and/or enjoying.

WHAT

Students (and others) who are motivated by their desire for better Personal Education` will develop cognitive-and-metacognitive strategies to improve their skills for many aspects of their own education, so they can:  improve their learning (with Learning Strategies) and transfers of learning (with Transfer Strategies) when they read, listen, watch, do;  improve their thinking/performing (and learning) with Coordination Strategies for solving problems (in design-inquiry) and answering questions (in science-inquiry);  use meta-strategies, such as strategies to guide their Personal Education and to regulate metacognition;  and more.   {more about strategies for Coordinating Design-Actions - Learning & Teaching + Learning and/or Performing that use a special kind of cognitive-and-metacognitive knowledge}

Learning Strategies for Physical Skills:  We can use a similar process of design to develop-and-improve a wide variety of strategies that include mental Strategies for Learning, plus mental-and-physical Strategies for Skills that usually are mental-and-physical Skills.

HOW — Learning from Experience

A process of design (which is outlined in five stages of a Progression for Learning Design Process) is used to develop all strategies, including Strategies for Learning. {This process is also used for other objectives, to design products, activities, and theories.}   What is the process?  In simplest form, it's just...

Learning from Experience:  you make a plan for what to do;  you do it and observe what happens;  using your experience, you adjust (if you think this will help) when you plan for "what to do" the next time, when you will do-and-observe, ... and you continue using these cycles of design.

Using Design Process:  This "learning from experience" occurs naturally for all of us, in all areas of life.*  But in many situations this process will be more effective when we use the principles of design-thinking outlined in Design Process.  And as a bonus, when students learn principles of design-thinking this will help them improve their skills with design-thinking, which will transfer to other design projects in many areas of life.

* Metacognitive Reflection is the essential strategy for learning from experience.  You do it whenever you remember “what happened” and think about it.  Usually, reflection helps you learn more from an experience, especially when learning is your objective;  and reflection is part of a sequence (experience, reflection, principles) that is useful for learning many ideas-and-skills.  You can do reflection with or without using Design Process, and either way it usually is productive.  But "in many situations" using Design Process can help make a process of metacognitive reflection more effective.

I recommend reading the SHORTER VERSION of this page-summary, because it's revised, is now better and shorter.

HOW — Using Cycles of PLAN-and-MONITOR

Here is the basic "learning from experience" process of design that we use to develop all strategies (including Strategies for Learning), described using principles of Design Process:

Diagram 1` shows that to begin you Choose an Objective for a Learning Strategy (to improve a Learning Skill) and Define your Goals (for desired Results when you apply the skill), and Prepare (by finding information about the skill and strategies for improving it).  We'll imagine that your Objective (the skill you want to improve) is learning from lectures;  your Goals (desired Results) are increased quality-and-quantity of learning, with increased accuracy-and-thoroughness of your understanding & remembering.

Then you GENERATE Options for a Strategy (to improve your skill in learning from lectures) and EVALUATE Options — using all available prediction-based & observation-based Quality Checks by mentally comparing your Goals (for desired Results) with your mental Predictions (about expected Results) & physical Observations (of actual Results) — and you continue doing this in a Two-Step Cycle of Design.

As shown in Diagram 2b and (in simplified form) 2c, this basic cycle of GENERATE-and-EVALUATE operates within a broader cycle of PLAN-and-MONITOR:  you PLAN (GENERATE-and-EVALUATE so you can "CHOOSE a Strategy-Option" for the first lecture) and MONITOR (you "USE the chosen Strategy" and "OBSERVE the Situation, your Actions, the Results");*  then you ask “do I want to adjust my Strategy and/or Strategy-Applying Actions?” when you re-PLAN (GENERATE-and-EVALUATE, CHOOSE) for the second lecture, in which you MONITOR (USE-and-OBSERVE);  then you PLAN for the third lecture, and MONITOR in the third lecture;  you continue using these cycles of PLAN-and-MONITOR (i.e., you PLAN, USE-and-OBSERVE,...) to Learn from Experience and improve your skill in learning from lectures.

* Either you can observe, or another person can observe and provide feedback.  A valuable “master skill” is improving your ability (and willingness) to learn from others, with Productive Responses to Critical Feedback.

As part of a re-PLAN you can revise your Objective or Goals, or Learn/Prepare more thoroughly, as shown with two arrows connecting the top of Diagram 2b/2c` with cycles of PLAN-and-MONITOR.

Multiple Quality Checks, done Mentally:  Diagram 2d shows that, during a PLAN, multiple Quality Checks (Mental & Physical, old and new) can be used in an effort to “consider all things.”  And 2d emphasizes that almost everything during Generation-and-Evaluation is done mentally, even in Observation-Based Quality Checks when you are mentally "comparing your Goals with... Observations."  And usually you OBSERVE mentally when you USE a chosen Strategy-Option in a Physical Experiment. 

Diagram 2d includes "Design an Experimental System" that can be used to produce new Observations and/or new Predictions to use — shown by the yellow and green arrow-lines — in a re-PLAN when you re-GENERATE and re-EVALUATE so you can re-CHOOSE a Strategy for the next Experiment(s).

2d also recognizes that, at any time, one option is to "compare Predictions with Observations in a Reality Check" to test the Predictive Accuracy of your Theory-Based Explanatory Models for “the way the world is.”

Quality Control for Strategy-Actualizing Actions

Quality Control is the process of trying to control (to observe-and-improve) your Quality of Actualization, to improve your Strategy-Applying Actions that actualize your Learning Strategy.    { MORE – You can use Quality Control for Actualizations of all problem-Solutions, to improve the quality of actualized Strategies, Activities, Products, and Explanatory Theories. }

Frequency Control is the process of trying to control (to observe-and-improve) your frequency of Strategy-Actualization.  Your goal is to develop self-knowledge and self-discipline so you will use each Learning Strategy at every appropriate time (no more, no less), whenever it will help you learn, think, and/or perform more effectively.  This is important because a potentially valuable Learning Strategy will be actually valuable only when you use it.

How?  When you design a Strategy for Learning, an effective PLAN requires thinking about your Strategy and also your Strategy-Actualizing Actions.  Why?

Strategy-plus-Actions produces Results:  When you actualize a Strategy with your Strategy-Actualizing Actions (also called Strategy-Applying Actions), this converts it from an imagined strategy-idea into actual strategy-in-action, so your Strategy becomes an Actualized Strategy that produces actual Results.   {color coding: It's logical, is analogous to pigments.}

Two Levels of Goals:  Your main goals are to achieve desired Results.  But you can improve Results by improving the quality of your Strategy-Applying Actions, so desired Actions are sub-goals that will help you achieve your main goals.

Quality Checks for Results & Actions:  By itself, a Strategy does not produce Results.  Instead, the observed Results are produced by an Actualized Strategy with two parts, your Strategy and your Actions in actualizing the Strategy.  {color coding: For strategies, as with pigments, blue + red produces purple.}   Therefore, during a re-PLAN you ask “should I revise the Strategy and/or my Actions?” so you EVALUATE both parts, using Quality Checks for Results (by comparing your Goals for desired Results with predicted Results or observed Results) and for Actions (by comparing your Goals for desired Actions with predicted Actions or observed Actions).

Using Imagination to Evaluate a Strategy:  The observed Results depend on your Strategy (so you ask “could it be effective IF it's done well?”) and your Strategy-Applying Actions (so you ask “was it done well?”) operating in the context of an external Situation.  Distinguishing between the effects of these factors (Strategy + Actions, and Situation) is important.  You can use your imagination to predict how your Strategy-Applying Actions could improve, how likely this is, and how an improved Actualized Learning Strategy (as it could be in the future) would change your learning-Results.  These predictions will help you decide whether to retain your current Strategy as-is, or adjust it, or generate a new Strategy.

The Timing of Metacognition

During cycles of PLAN-and-MONITOR you use metacognitive observations of your thinking-Actions and your learning-Results.  When you observe, what timing will be most beneficial?  Should you do metacognitive observing during a lecture (to perhaps make immediate adjustments, or for later use in a re-PLAN), or avoid metacognition during a lecture (so you can focus on the lecture) and then “observe by remembering” after it's over?

These important questions (with answers that may differ when your objective shifts between Performing and Learning) occur when you want to regulate metacognition by deciding whether to "stimulate productivity by using metacognition" or "allow productivity by avoiding metacognition," whether you should turn metacognition on (because this will increase Potential Performance) or off (when it's a Distracting Interference you want to reduce).  You can make better decisions about regulating metacognition if you improve your Metacognitive Knowledge:

Metacognitive Knowledge — General and Personal

Designing a maximally effective Strategy for Learning-and-Performing usually requires building a strong foundation of general Metacognitive Knowledge — about persons (how we think, learn, perform) and tasks (situations, requirements, outcomes) and strategies (each with its pros & cons) — plus personal Metacognitive Knowledge by “knowing yourself” based on observations of yourself (as the person) in the context of various tasks using different strategies.    { This general & personal knowledge is actually Cognitive-and-Metacognitive Knowledge about cognition, metacognition, and their interactions. }

You also develop skills-knowledge about specific objectives.  For example, if you want to improve your skill in learning from lectures, you develop knowledge about this skill by finding strategies from others (by searching for what they recommend, and why) and yourself (by remembering what you've done in the past, and the Results).  Learning from lectures is especially interesting due to "the wide variety of strategy-options... you can use to prepare for a lecture... and learn during it (with complex multi-tasking by quickly shifting attention between listening & seeing, thinking & writing, plus metacognitive observing) while minimizing distractions (that can begin externally or internally), and learn after it (by reviewing your notes & memories while partially-learned ideas are still fresh in your mind)."  {quotation is condensed from the full-length section}

Metacognitive Knowledge — knowing Design Process

An extremely useful kind of metacognitive knowledge is understanding — in a variety of ways, both conceptually and procedurally — the principles of Design Process, because we use these thinking-and-actions in a process of design to solve problems in General Design and Science-Design which includes almost everything we do.  This knowledge (especially Conditional Knowledge) is used to develop-and-apply cognitive/metacognitive Strategies to Coordinate a Process of Design.

A Strategy to Coordinate a Process of Design is a special type of Strategy to Perform-and-Learn.  In all Performing-and-Learning Strategies, we use cycles of PLAN-and-MONITOR.  But compared with a typical Learning-Skill Strategy, a Coordination Strategy has two special features:

First, with Coordination we have a wider variety of options.  By contrast, if you want to improve your learning from lectures, your Strategy for a previous lecture probably will be very similar to the Strategy you choose for your next lecture, after a re-PLAN in which you ask “do I want to adjust my Strategy and/or Strategy-Applying Actions?”  This similarity, in previous choices and next choice, usually occurs for most Learning Skills, but not always because sometimes you will find or invent an innovative new Strategy.  But in a process of design, when you decide “what to do next” you can Choose from a wide variety of thinking-and-actions that are used in a process of design, so your previous Action-Choice and next Action-Choice are often (but not always) very different.

Second, with Coordination the objective is the process itself.  During a Coordination Strategy, your Metacognitive Knowledge (about principles of Design Process) is about the process, and is used in a process of design that "will be more effective when you use the principles of design-thinking outlined in Design Process."  But for a typical Learning Strategy, your Metacognitive Knowledge is about a Learning Skill (such as learning from lectures), not about the process of design.

Here is a summary of similarities and differences for two kinds of Strategies for Performing-and-Learning:

 ▓  Creative-and-Critical PRODUCTIVE THINKING    (into left frame`)

This is a long page-summary because I think these ideas are fascinating and important.  I hope you also will enjoy them.

GUIDED GENERATION (of Ideas for Options)

A Brief Overview-Summary:  When your creative Generating of Ideas` — by finding (old) or inventing (new) — is stimulated/guided by a critical Evaluating of Ideas, this creative-and-critical process is Retroductive Generation, aka Retroduction using Retroductive Logic.

Perspective:  Although creative generation is the focus here, for wise productive thinking "critical evaluation is more important" because it helps avoid unwise action.

A Wide View:  Diagrams 1 & 2a/2e & 3b` show the Big Picture of how we "creatively GENERATE Options" and "critically EVALUATE Options" to GENERATE-and-EVALUATE in iterative Cycles of Design.  You can get a verbal/visual overview of the GENERATION-and-EVALUATION that occurs in Cycles of Design (using Quality Checks) and Cycles of Science (using Reality Checks) by studying Stage 3 of a progression for learning Design Process.

Zooming In:  This section is a closer examination of productive interactions between creative thinking and critical thinking.  Later, in Free Generation we'll look at strategies for minimizing the unproductive interactions that occur when creativity is being hindered by critical thinking and/or knowledge-of-ideas.

Here is an outline of the creative-and-critical process of Guided Generation for three types of objectives, for designing a Model (in Science-Design), Solution (in General Design), or Experiment (in both types of design).

• Generating Model-Options by Retroduction, for Science-Design:

    You use deductive logic to make predictions with an if-then question: “If this is the model, then what will be the observations?”
    By contrast, with retroductive logic (in retroduction) the if-then question is past tense (thus the "retro" in retroduction) and is reversed: “These were the observations, so what could be the model?”   In creative retroductive generation, you do many mental experiments, each time “trying out” a different Model-Option (old or new) with the purpose of finding a Model whose Predictions match the known Observations.  Your purpose is to find a model that, if true, would explain what already has been observed.    {timing: Is it logically valid to use retroductive post-diction instead of pre-diction?}

• Generating Solution-Options by Retroduction, for General Design:

    You use deductive logic to predict the properties of an Option.  In a Predictions-Based Quality Check you compare these Predicted Properties with the Goal-Properties you want.
    In creative retroductive generation, you do many mental experiments, each time “trying out” a different Solution-Option (old or new) with the purpose of finding a Product whose Predicted Properties (or Observed Properties, if known) match your desired Goal-Properties.

• Generating Experiment-Options by Retroduction, for all design:

    You use deductive logic to predict “what kinds of things might happen, and what you could learn” if you do a particular experiment.  You compare these predictions with your goals-for-experimenting, with what you want to know, defined by asking “what information (Predictions or Observations) would be useful?”
    In creative retroductive generation, you do many mental experiments, each time “trying out” a different Experiment-Option (old or new), asking “what kinds of things might happen, and what would we learn?” for the purpose of finding Experiments that could produce useful new Predictions or Observations.

I.O.U. - Soon, probably sometime April 18-21, I'll more carefully describe the 4-step process of "Generating Solution-Options by Retroduction" that is outlined above and is shown in the diagram below.   Briefly, the 4 steps are:  choose an Option that already exists;  predict its properties;  compare these Predicted Properties with your Goal-Properties;  revise the old Option to get a new Option by using the process of retroductive generation that is labeled "(4)" below, which includes getting "Predicted Properties of Option" by using deduction.

These three uses of Retroduction (for Science-Design and General Design) are explained, verbally & visually, in Stage 4 of a Progression for Learning Design Process.

Similarities & Differences

Retroduction is partially analogous for Model-Options and Solution-Options, because Science-Design and General Design are closely related but are not identical.

Basic Process:  For each objective, you "do many mental experiments..." in a similar creative-and-critical process of Retroductive Generation, so you can make mental Predictions to compare with Observations (to evaluate Model-Options) or with Goals (to evaluate Solution-Options).  But in General Design there is another way to evaluate;  you also can compare Goals with Observations (not just with Predictions), to evaluate the quality of Solution-Options.

Mental and Physical:  Predictions (made in Mental Experiments) are useful in all design, but are essential in Science-Design because Predictions are part of its main objective, which is developing an Explanatory Model that allows accurate predicting and understanding.  By contrast, accurate Predictions are just a useful sub-objective in General Design, because mental Predictions (along with physical Observations) provide information about the properties of Options, thus helping you find a satisfactory Solution (for a product, activity, or strategy) that is your main objective.

Evaluation Criteria:  Your creative generation is stimulated-and-guided by critical evaluation in Reality Checks (to generate options for a theory-based explanatory Model) or Quality Checks (to generate options for a problem-Solution when the objective is a product, activity, or strategy).   How is quality defined?  in a Quality Check (Mental or Physical), the quality of a Solution is defined by your Goals;  in a Reality Check, the quality of a Model is defined by Reality.    {checking – for Quality & with Reality}

Divergent Generation:  When you generate options (by Guided Invention + Free Invention) and search for the best option, a divergent search due to tough competition — because several options offer different benefits, with each better for some goal-criteria but not others — is more likely with products (or activities, strategies) or experiments, compared with models because (despite the radical relativism of postmodernism) there is a correct model/theory, although...

New Objectives:  In a different kind of divergent search, you "do many mental experiments" while asking “what Problems might be solved by the current Solution-Options?”  This question stimulates creative retroduction (based on the known Properties of known Options) to search for new Objectives that have Goals matching the known Properties, to find new applications for current Options (as-is, or revised so they are more effective for a different application), to find new Objectives for new Design Projects.

Stimulating and Guiding

Your "creative Generation of Ideas" can be "stimulated-and-guided by a critical Evaluation of Ideas."  How?

Stimulating:  During a comparative Quality Check, when you see differences between an Option's Properties (predicted or observed) and the Goal-Properties you want, this awareness of mis-matching can lead to dis-satisfaction and a motivation to search for better options, which stimulates your Generation of Ideas.

Guiding:  In a Quality Check, quality is defined by Goals that become an “aiming point” when your awareness of Goals “pulls your ideas” toward them, when you creatively imagine possibilities while asking “how can I generate options with Properties that more closely match my Goals?”, as in the children's game where feedback that “you're getting warmer” guides your goal-directed searching in productive directions.

Education for Guided Generation

By teaching principles of Design Process in the context of Design Experiences we can help students understand Guided Generation, and be more effective in stimulating-and-guiding their own creativity.  The value of using retroductive interactions (between creative thinking and critical thinking) is emphasized in Design Process, as explained in Stages 3 & 4 of a Progression for Learning Design Process.  Teaching principles of Design Process can be especially useful for helping students improve their Strategies for Coordinating Design-Actions.

What is Critical Thinking?  (is it fault-finding negativity?)

The meaning(s) of words is important: in the context of design, critical thinking {noun} is "disciplined thinking that is clear, rational, open-minded, and informed by evidence" (from dictionary.com), and critical {adjective} is "involving skillful judgment as to truth, merit, etc" but also "inclined to find fault or to judge with severity."  These two meanings of critical can cause confusion;  the first ("involving skillful judgment") is correctly associated with critical thinking in problem solving & education, but the second ("to find fault") can be an unfortunate implication of critical thinking due to a common meaning of critical in other contexts.  Instead of critical thinking, maybe a better term would be evaluative thinking because it's less likely to be misunderstood.  But in this website I'll continue to call it critical thinking, for two reasons:  it's the usual custom in education;  and it avoids the awkward redundancy of "evaluative Idea-Evaluation" when describing the productive combining of creative Idea-Generation and critical Idea-Evaluation in Cycles of Design that are the foundation of Guided Generation.

During a process of design, critical thinking is not just negative fault-finding.  When an Option is evaluated by critical thinking, this critical evaluation produces an output (which is critical feedback) that can range from positive to negative, or anything in-between.  Almost always it's in-between, so the critical feedback is a mixture with some positives (because some properties of an Option closely match the desired properties that are your goals) and some negatives (for other properties).  Due to this mixture of positive-and-negative, it's possible (but not necessary) for critical thinking to hinder creativity, as you'll see in "Reducing Harsh Attitudes" below.

FREE GENERATION (of Ideas for Options)

Productive Thinking occurs "when you effectively combine Creative Thinking and Critical Thinking with Knowledge-of-Ideas."  How can you "effectively combine" these aspects of productive thinking?

Critical Thinking and accurate Ideas-Knowledge should always be mutually supportive, because critical evaluation helps you construct accurate ideas, and the more you truly know (when your ideas are accurate, are true because they correspond to reality) the better you can evaluate.

In contrast with the consistency of this mutual support between Critical Thinking and Ideas-Knowledge, two things can happen when Creative Thinking is combined with Critical Thinking or with Ideas-Knowledge.  The result can be either productive — as in Guided Generation when critical Evaluation stimulates-and-guides creative Generation, and when Knowledge about an old Option allows creative Generation-by-Revision — or it can be unproductive in the two ways explained below, because Creative Thinking can be hindered by its interactions with Critical Thinking (if the result is a perception of Harshly Critical Attitudes) or with Ideas-Knowledge (if this produces Restrictive Assumptions about “the way things must be”).

• Reducing Harshly Critical Attitudes  (real or perceived)

A productive generation of useful ideas requires critical evaluation.  But if people are criticized with a harsh attitude, this can stifle creativity.  Here are two ways to reduce potential stifling, by developing better attitudes (in a Creative Community) and by using strategies (to temporarily liberate creativity from the effects of critical thinking):

Developing a Creative (and critical) Collaborative Community

How?  Basically, just encourage everyone to be creative in Generating Ideas, both individually and as a community.

An important part of a creative community is encouraging feedback from others, because...  people can learn from experience whether the experience is first-hand (when they observe) or second-hand (when another person observes and provides feedback).  Both types of experience can be sources of learning when we get feedback about an option that is being proposed.

Unfortunately, the effects of evaluation can be unproductive because critical feedback (based on evaluative critical thinking) is almost always "a mixture with some positives... and some negatives."  Both aspects of feedback should be useful, but sometimes are not.

The positive aspects of critical feedback will help build shared enthusiasm, in a community, for working together to achieve common goals.  Many benefits are produced when positive reinforcements are common in a community.  One creativity-stimulating benefit is to help persuade everyone that maximizing a creative generation of useful ideas is valued more highly than minimizing non-useful ideas. (with "useful" and "non-useful" being defined by the evaluations of others)     /   * And positive feedback is almost always possible.  Even when it's tough to find things to praise in a colleague's proposal (and this rarely occurs), usually it should be easy to appreciate the effort invested, and find a way to sincerely praise the person.

The negative aspects of critical feedback are usually productive because accurate negative feedback is necessary for a critical Evaluation of Options, for helping a community revise a problem-solving Option or find/invent a better Option.  Accurate evaluation-and-feedback is essential during a process of pursuing the community's common goal, which is designing the best possible Solution for a Problem.  But...

In all critical feedback, any negative aspects should be purely technical (regarding the Option, with a goal of making the design project more effective) but not personal.  If any "technical" criticism is partially personal (due to personality conflicts, unhealthy competitions, jealousy, a desire for revenge, or other unproductive motives) it's a "harsh attitude" that is real, that should be eliminated in a community, or at least reduced to a minimum.  In the rest of this section the perspective shifts from the provider of critical feedback to its receiver, by describing the benefits of encouraging decreases in perceived harshness.

Productive Responses:  To get maximum benefits from second-hand experience and to promote cooperative collaboration in a community, everyone should improve their ability-and-willingness to learn from others, with Productive Responses to Constructive Feedback that include perceiving negative aspects of feedback as intended to be helpful rather than harshly critical.*  This attitude, if widely adopted in a community, will produce expectations that explaining “how it seems from my perspective” will be viewed as helpful productive communication, not a harsh criticism of other perspectives.  In this atmosphere of mutual trust and friendly cooperation, colleagues will be more likely to share ideas that stimulate each other's thinking and promote collaborative creativity.   { More important, they probably will improve the overall quality of their collective critical thinking and their evaluation of options, so they will design a better problem-solution. }

* Forgiving:  Even if there are rational reasons to suspect that some negative aspects of critical feedback are “partially personal” instead of “purely technical” so there is "a harsh attitude that is real," forgiving is possible and is usually productive.  And it's usually best to give a “benefit of doubt” by initially assuming that all critical feedback (positive & negative) is intended to be helpful.   { Usually, some positive aspects of feedback also are partially personal. }

Gracious Feedback:  But some people might be less able-and-willing to "learn from others" so everyone should use a Golden Rule with Empathy by treating others in ways the others want to be treated.  This requires sensitively aware empathetic thinking to decide if another person will view feedback as helpful, instead of just assuming they should view it this way, so they will — to decide whether it's wise to share feedback, and (if yes) how to share ideas in a way that will be perceived as helpful.   {empathetic understanding —(wise filtering)→ feedback}

Evaluative Response:  Of course, even when you view external feedback as "intended to be helpful" the feedback should be critically evaluated.  In fact, the second step of a productive response is to use your own critical thinking to ask “is this feedback accurate?” and your answer can be “yes” or “partially” or “no”.  For the benefit of everyone involved in a process of design, you may want to logically-and-respectfully explain why you reached any of these conclusions, with your own evaluative feedback about their evaluative feedback.  In a creative community it should be acceptable — because usually it's productive — to vigorously argue for an idea that you (or others) have proposed.

Developing effective thinking strategies for learning-and-performing in all areas of life, including a nurturing of creativity in individuals and in a community, depends on effectively learning from experience.  When we learn from the experience of others, through their critical feedback and in other ways, we're taking advantage of opportunities to "view situations from new perspectives" so we can Reduce Restrictive Assumptions that limit our creativity.

And we can use character-based principles – as in The 7 Habits of Highly Effective People – to improve the interpersonal dynamics of a community so (quoting from a summary of Habit 6 which builds on 1-2-3 and 4-5, plus 7) the community's independent people and their interdependent relationships will help them be more effective in "the adventure of finding new solutions" because "the habit of creative cooperation... lets us discover jointly things we are much less likely to discover by ourselves. ... When people begin to interact together genuinely, and they're open to each other's influence, they begin to gain new insight."  A careful empathy-based cultivation of Emotional Bank Accounts will help members of a community build the mutual trust that encourages open-and-honest communication in a Creative Community.

more:  One context-for-application, which illustrates general principles, is Developing a Creative Community for Lab Education.

I.O.U. - Soon, by mid-June, this section will be condensed-and-revised for the shorter Executive Summary.

Creative without Critical (by using a strategy of Brainstorm-and-Edit)

Confidence in Creativity:  Another approach is to temporarily minimize criticism by self and others.  Why?  Because a freedom-from-criticism can help inspire confidence in your own creativity, and (in a group) the creativity of your collaborators.  How?  We'll look at a controversial technique (with a wide range of views about its pros & cons) that can stimulate creativity when it's used wisely.

More strategies for stimulating creativity — by combining Free Generation with Guided Generation or Analysis-and-Revision — are below.

COMBINING Ways to Generate

Guided Generation and Free Generation can be combined because Guided Generation should be free, and Free Generation can be guided.  This compatibility is due to...

An Important Distinction:  The main requirement for Free Generation` is freedom from the restrictive assumptions that occur when a knowledge of “the way things have been” becomes a freedom-restricting certainty about “the way things must be.”  But these past-oriented assumptions should be different than the future-oriented awareness in a goal-directed creativity with Guided Generation.

Ideally, during Guided Generation you want freedom of thinking about “HOW to achieve a Solution” with no unintentional restrictions, while you intentionally let your thinking be guided by your Goals for “WHAT should be achieved in a Solution.”   But before this, you also can aim for a Free Generation of ideas when you're thinking about Goals for WHAT should be.  In both phases of design — for WHAT (while you're Defining a Problem, including Goals for a Satisfactory Solution) and HOW (while you're Solving the Problem) — you want a Free Generation of ideas with "a flexible style of thinking that lets you expand the range of options you generate."

You don't have to adopt a one-strategy approach for Generating Options.  You can “try out” a variety of freely generated options in the multiple mental experiments of Guided Generation.  And in case your thinking is being unconsciously restricted by consciously trying to achieve a match between Option-Properties and Goal-Properties, you ALSO can try to temporarily ignore evaluation by using a strategy of Brainstorm-and-Edit.

One factor in productive Free Generation is freedom from worries about the humiliation that is felt when there is a perception of harsh attitudes implying “your idea was dumb” so “you're not very smart.”  This harsh attitude, which differs from the beneficial goal-directed awareness of Guided Generation, should be minimized.  When using a strategy of Brainstorm-and-Edit for the purpose of “feeling more free” in the brainstorming phase, the later editing phase does require logical evaluation, but "this should be done carefully, with sensitivity," to establish a feeling of creative comfort in a creative community so your future brainstorming will be more productive.

Using Analysis-and-Revision:  A common strategy for Guided Generation is to combine Analysis (into features) with Revision (of features) by thinking about ways to revise each feature, in an effort to get a closer match with your Goals.

Innovation:  The full page describes a way to think about innovative revision in terms of landscape-topology,* by imagining local maximums (produced by minor revisions of an old Option) and a global maximum (found by comparing all of the maxima produced by revisions).   {* If your thinking is stuck in a "small hill" rut, you may not realize that larger hills exist, with a higher maximum than on your familiar small hill.}   { This metaphor of “small hills and big hills” also is useful for thinking about the re-optimizing of procedural ecology for a physical skill.  Recognizing the "adding of value" that occurs during an optimization when you "climb a new hill" is a reminder to think about ways a new kind of Option might be developed-and-improved, to think about its potential — what it could be after it's been improved by revisions — instead of just rejecting a new Option immediately because in its undeveloped state it isn't as good, in all ways, compared with a highly-developed old Option. }

PRODUCTIVE THINKING — An Overview

I.O.U. — This will be written later.  Until then, you can read a condensed summary and (about twice as long, but still fairly short) the full-length overview.

at any age?

Grade-Level Standards for Science & Engineering Practices

update:  After examining NGSS more thoroughly, and hearing a webinar by NSTA,* I think NGSS & NSTA agree with what I say below.   /   * In the webinar, Heidi Schweingruber began by emphasizing the sophisticated reasoning abilities of young children, who are competent to engage in all of the Science/Engineering Practices. {This is consistent with Bruner's claim that with effective teaching, students can learn ideas [and also skills?] with intellectual integrity at any age.}   Later presenters agreed with Heidi.

NGSS has four grade-levels, for K-2, 3-5, 6-8, and 9-12.  But I think all students can learn all basic skills of Design Process – which includes most of the Scientific & Engineering Practices – from the beginning of their classroom experiences with science-inquiry and design-inquiry.  In an effective spiral curriculum for ideas-and-skills education students will continually increase their level of mastery (for thinking skills & thinking process) but at each grade-level they will use & improve the same basic skills for their process of creative-and-critical thinking.

Ideas-Learning ≠ Skills-Learning:  For a coordinated teaching of IDEAS, distinguishing between grade levels (as in Science Standards) is useful, to insure that early learning will form a solid foundation for later learning.  But a different teaching strategy is better for SKILLS because in a process of design we use only a few basic thinking skills.  By contrast, the number of ideas is very large, so a coordination of “what to teach and when” is essential.  Also, we use design-thinking for almost everything in life so students already are familiar with the skills of design-thinking, which makes it easier for them to learn (by "discovery" and with explanations) the principles of Design Process.

To teach thinking skills-and-process, a useful strategy is whole-part-whole instruction to "let students sometimes focus on parts ... and at other times do the whole process", with teachers adjusting the level of difficulty for both.

Concerns about the Effects of Exams

In the full page, my concluding commentaries describe:  how the Standards and Design Process have similar goals for ideas-and-skills education;  why eclectic instruction (that includes learning from explanations, by discovery, during activities) is beneficial;  and, most important, why I'm concerned about an over-emphasis on ideas (relative to skills) in standards-based exams, unless these differ significantly from current exams based on NCLB policies, due to the detrimental effects of these exams:

    The NGS-Standards emphasize the value of skills that help students learn ideas (in science-inquiry). ... We also should emphasize the learning of science skills (with science-inquiry) and learning of design skills (with design-inquiry) simply to improve these skills, independent from their role in learning ideas. ...  Along with many other educators, I think high-stakes exams that over-emphasize ideas (relative to skills) influence curriculum & instruction in ways that are not beneficial for students, in the long run.

Below you can see my concerns — in Ideas versus Skills and Five Rational Reasons for Teachers to Not Teach Thinking Skills — and a potential partial solution by using Conceptual Evaluations of Instruction Quality.

Feedback for NGSS in January 2013:  I suggested writing a Glossary of Terms (for theory, model, hypothesis, explanation, plus predictions & observations/data, experiment,...) for Science & Engineering Practices, to clarify meanings-of-terms and intentions-for-terms.

 ▓  Curriculum & Instruction (design and adoption)    (into left frame`)

I recommend reading the revised version for the SHADED PARTS of this page-summary.

For a Goal-Directed Designing of Curriculum & Instruction, we can:

• define goals for desired outcomes, for ideas-and-skills we want students to learn,

• design instruction with learning activities and teaching strategies that will provide opportunities for experience with these ideas & skills, and help students learn more from their experiences.

Ideas-and-Skills:  We can define goals for ideas (what students know) that are conceptual knowledge, and skills (what they can do) that are procedural knowledge.  Our goals for ideas-and-skills include ideas, and skills, and skills-with-ideas (i.e. skills combined with ideas in productive interactive combinations, as when solving problems with design-inquiry in General Design, or answering questions with science-inquiry in Science-Design).

and More:  In a wider view, we can think about a system of goals — to promote a wide range of desirable outcomes that are COGNITIVE (for ideas & skills, using multiple intelligences) and AFFECTIVE (for motivation & attitudes`) and PHYSICAL (for nutrition, health & fitness, physical skills), plus other worthy goals (for compassion, ethics, character,...) — and ask, “How much of our educational resources (time, people, money,...) should be invested in each goal?”  The discussion below recognizes this wider context, but will focus on cognitive goals for ideas & skills.    {Educational Goals for Many Types of Knowledge}

Goal-Directed Instruction:  We can design activities to teach specific educational goals, with goal-directed Aesop's Activities.

Eclectic Instruction:  If different kinds of instruction will be useful for teaching various aspects of ideas-and-skills (≡ ideas + skills + ideas with skills), then we should try to combine different kinds of instruction in an eclectic blend.

Ideas plus Skills:  Many prominent educators emphasize the importance of defining knowledge widely, to include ideas-and-skills.   The new Standards for K-12 Science Education (NGSS) recommend instruction that "integrates the knowledge of scientific explanations (i.e., content knowledge) and the practices [i.e., procedural knowledge] needed to engage in scientific inquiry and engineering design." (and I'm hoping NGSS won't increase the negative effects of exams)   Also, Robert Marzano's New Taxonomy of Educational Objectives has three systems (Self-System, Metacognitive System, Cognitive System) and a Knowledge Domain that includes Information, Mental Procedures, Physical Procedures;  and Models of Problem Solving & Learning (from educational researchers at CRESST) provide a framework for thinking about an ideas-and-skills curriculum that uses Design Process to improve the mutually supportive interactions between ideas and skills. 

What factors influence a teacher's decisions about what to teach, and how?  When teachers make decisions, a major factor — which unfortunately is a common obstacle hindering effective education for ideas-and-skills that will be most useful in 21st century life — is the difficulty of developing...

Exams to Evaluate Skills:  For each educational goal, we can ask “what evidence would show that a student has achieved this goal?”  To get information about goal-achievements, exams are useful.  When evaluations of ideas-and-skills are done well and used wisely, they can be very valuable for increasing student motivations and providing formative feedback.  But compared with our evaluations of ideas-knowledge, it's much more difficult & expensive to measure skills-knowledge in our evaluations of learning (for students) and teaching (for teachers & schools & states).  This difficulty in assessing skills causes difficulties in persuading teachers that they should place more emphasis on teaching skills-knowledge — with high-stakes “standardized exams” often producing undesirable distortions of curriculum-and-instruction — because there are...

5 Rational Reasons

for Teachers to Not Teach Thinking Skills

Teachers have rational reasons to say either “yes” or “no” to ideas-and-skills education in which the balance is shifted toward more emphasis on skills, due to factors that include "mutually supportive interactions" but also "competitive tensions" between teaching ideas and skills.  In education, an important principle is that...

A teaching method will produce large-scale improvements only if the method is educationally effective AND is widely adopted by teachers, schools, districts, and states.  How do teachers (and their districts & states) make decisions about adoption?

Factors in Deciding What-and-How to Teach  —  The decisions of a teacher are based on many factors.  What you see below is my simplified description of a complex reality, shared with justifiable humility.

5 Rational Reasons

It seems to me that, for any method of instruction that increases the emphasis on thinking skills — whether or not principles of problem solving are explicitly taught, by using Design Process or in other ways — the rate of adoption can be reduced by five related practical concerns of teachers:

• Ideas versus Skills:  With limited teaching time we may not be able to maximize a mastery of both ideas and skills.  If teachers want to teach lots of ideas, they will not want to invest the classroom time that is required to also teach skills.

• A Distorted Definition for Quality of Teaching:  If (due to distorted values for the goals of a curriculum) the quality of teaching is defined mainly by students’ performance on standardized exams that emphasize ideas,* teachers (and their schools) who want a high rating will “teach to the exam” by emphasizing ideas, even if they personally would rather increase the emphasis on skills.  By using definitions that emphasized learning-of-ideas, we're “hoping for A, while rewarding B.”     {* Usually judgments of teaching quality – by self and by colleagues, administrators, students, and parents – are based on multiple criteria, but if the results of standardized exams are heavily weighted, this will exert a strong influence on the instructional ecology – with aspects that are cognitive-affective, social & cultural – of most teachers when they make decisions about what to teach, and how. }    { For some activities in some school-contexts, avoiding controversy is a rational response to concerns about perceived quality of teaching. }

• Preparation Time:  Even if a teacher is interested in “going for it” with a new way to teach skills, learning how to be effective in using the new method – which changes their instructional ecology, so it then has to be re-optimized – may require more preparation time than they are willing to invest.  But this factor can be unconscious, when a teacher wants to reduce their cognitive dissonance AND they consider themself to be a dedicated teacher, so instead of consciously thinking “I'm not willing to invest the time” they persuade themself that “more emphasis on skills won't be effective.”     {more about quality and time}

• Measuring Higher-Level Thinking Skills:  Accurate assessment of thinking skills is difficult and it requires more time, with subjective judgments that many teachers don't enjoy.

• Classroom Management:  Teachers may worry that during inquiry activities (to help students learn thinking skills) their classroom discipline will be more complex and difficult, with possibilities for problems.    {in reality, IF inquiry activities lead to more motivation for “problem students” this can make it easier to improve classroom discipline}

The full page ends with strategies for coping with these rational concerns so more teachers will decide to "increase their emphasis on thinking skills."  These strategies include evaluations of teachers that place less emphasis on exams, and instead use other criteria, including Conceptual Evaluations of Instruction Quality*;  and, more specifically for teaching Design Process, we can develop teacher-friendly computer-based activities.

* Conceptual Evaluations assume that most teachers are competent and conscientious, dedicated to teaching well, and that, as proposed by David Perkins, students will “learn much of what they have a reasonable opportunity and motivation to learn.”  In a Conceptual Evaluation we ask “do teachers have plans that offer opportunities to learn, and motivations?” and (for Quality Control) “what is the quality of performance when these plans are actualized in the classroom?”   /   This kind of evaluation is not new, it's “traditional” and is commonly used by most teachers when they evaluate their own teaching, and that of colleagues.  I'm merely calling attention to its potential value for minimizing...

Distortions of Curriculum (by high-stakes exams)

Why is it important to design curriculum-and-instruction that includes a wiser use of better exams?  or that reduces the importance of exams, for students, teachers, and schools?

Earlier (in Ideas versus Skills) I say that "high-stakes ‘standardized exams’ often produce undesirable distortions of curriculum-and-instruction" away from an idealized context in which educators simply ask “what kind of education is best for students? how can we help them develop their full potentials? what will be most effective in motivating students so they're excited about learning, and improving the ideas-and-skills that will be most useful for them in life, now and in their futures?”  Instead of focusing on these worthy goals, teachers in public schools (often pressured by their school, district, or state)* ask “how can we get more points on the standardized exams?” and this leads to distortions of educational priorities, moving our curriculum-and-instruction away from what is best for students.

* These distorting pressures are much less intense in public schools and home schools, so educators in these contexts are more free to focus on worthy educational goals.   {IOU - I'll check the accuracy of this claim soon, maybe in June.}

Here are some comments about exams & standards, written in mid-January 2013:

The Next Generation Science Standards (NGSS) include "Practices" for Science & Engineering.  I like this emphasis on thinking skills, but I'm hoping NGSS won't become the basis for standardized exams that continue to de-emphasize thinking skills.

Also, I hope their view of Practices will stimulate our national creativity in developing-and-adopting effective curriculum & instruction to teach these skills.  More specifically and personally, Design Process is compatible with NGSS yet offers "special added value" because it's distinctive, but...   Perhaps differing in any way from "The Standards" will be a disadvantage for adoption, if (through exams, or choices made while our education community is developing & adopting standards-based curriculum and instruction) there is an overly narrow focusing on NGSS's conceptualization of the processes of thinking used in Science and Engineering, and the terms they use to describe it.

(full page)

Five pages about important questions — those in this yellow box ( Strategies for Teaching - Developing Instruction for Teaching Design Process - Experience plus Principles ) plus Decisions about What-and-How to Teach and Why we should Teach Design Process — are closely related.

▓ Experience plus Principles   (into left frame`)

I recommend first reading the revised/condensed version of "Experience + Principles" and then (optional, for “more”) the original longer version below.

▓ Strategies for Teaching Inquiry-Process   (into left frame`)

Instruction using Design Process and Other Models:

Compared with other models for a process of inquiry, my model of Design Process is similar in most ways, but is distinctive in some ways, so it's old-and-new and it offers many potential benefits for helping students improve their understanding of inquiry-process, and their skill in doing design-inquiry and/or science-inquiry. (are understanding & skill related?)

Similarities occur in goals and being not-rigid & not-uniform and using long-term phases and learning from experience.  I recommend reading about these in an examination of Other Models-for-Process (including an in-depth look at d.school of Stanford) where I explain why one of my goals for Design Process has been achieved, but another has not, and how creatively combining models (using the short-term sequences in Design Process and long-term phases in other models) could be educationally useful.

But some differences occur in strategies for teaching inquiry-process.  For example, there is a wide range of views about whether it's best to use...

No Model, Semi-Model, and/or Model

During inquiry activities, students can learn principles of inquiry-process by using a model and/or semi-model, and/or no model.   {Design Process is a Model that contains a Semi-Model}

• no model — During inquiry activities the simplest teaching strategy is just letting students try to answer questions (in science-inquiry) or solve problems (in design-inquiry).  A skillful teacher will guide students and encourage reflection but if there is "no model" the guiding won't be goal-directed, it won't lead students toward learning the principles in any "model" for a process of science and/or design;  usually, fewer principles will be learned.*  But this is acceptable for proponents of using no model who answer “no” (or “probably not”) when we ask an important question: Will a combination of experience-plus-principles be more educationally effective than only experience? *

• semi-model — Similar to no models, major approaches to instruction in the past (e.g., Science: A Process Approach, SAPA) and in the present/future (with the new K-12 Next Generation Science Standards, NGSS) do describe thinking skills, individually and in functional combinations;  but there is no attempt to integrate these thinking skills into a coherently organized framework that could be called a model, so I call this approach a semi-model.   {more about SAPA and NGSS}

• other models — The full page compares other models with Design Process, looking for similarities & differences in their frameworks and supplementations, and their uses for instruction.  One difference is that most other models more strongly emphasize the action of Evaluation that is based on new Observations (as in learning from experience in Stage 2b of a five-stage progression of learning) by using it to define one part of a long-term sequence of actions.   {more about other models}

Do these 3 categories exist?  In reality there is a multi-dimensional continuum of modeling, so splitting the continuum into distinct categories is an oversimplification.  But I think these categories, although not rigorous, can be useful when we're asking “should we use a model never, always, or sometimes?” and are thinking about the effectiveness of different strategies for teaching inquiry, as in "Experience plus Principles" below.

* Flexibility in Thinking and Instruction:  Some educators with a strong preference for no model or a semi-model seem to imply that “a model should never be used.”  They may want to examine their assumptions and consider the potential benefits of sometimes using models.

To avoid another misconception, we also should recognize that discovery & explanation are compatible, so instead of thinking they must fight against each other in either-or competition, we should think about how to creatively combine them into an eclectic blend that will more effectively help students improve their ideas-and-skills.   { In my opinion discovery instruction, with guided inquiry, is more effective for teaching skills than for teaching ideas. }

Also, when asking “should we supplement inquiry-experiences by teaching inquiry-principles?”, the obvious answer is “yes” so instead we should ask “what principles?” and “how? using which teaching strategies, and with how much emphasis?”  The answer is “yes” because...

* Even though I think it's true that "usually fewer principles will be learned" when using no model, compared with using a model-for-process, I do recognize that this comparison of extremes (no model versus model) oversimplifies a complex situation in three ways.  First, re: categories, "there is a multi-dimensional continuum of modeling" so we cannot rigorously distinguish between no model, semi-model, and model.  Second, even teachers with "no model" have their own mental model for a process of inquiry;  in their mental model (which is somewhere between between the extremes of literally "no model" and what I consider to be a "model"), some principles will be explicit (i.e. known consciously by the teacher) and others will be implicit.  Therefore (third), even when a teacher has no explicit model (or semi-model), they will be teaching some process-principles, both explicitly and implicitly.  Since principles are always being taught, we can just ask “what principles should we teach?” and “how?”

What is Design Process?

Design Process is a model, but for instruction it can be used as both a semi-model and a model.  How?

10 Modes of Thinking-and-Action are used in Design Process, and I consider this interactive system of functionally-related modes to be a semi-model.  In 5 Stages of a Learning Progression these 10 modes are "integrated... into a coherently organized framework" that is a model (actually it's a family of models) for Design Process.

A sequence of instruction typically begins with no model, to give students experience before principles.  Then it can move on to either models (in the stages) or semi-models (the modes),* and eventually it should help students understand their process of designing in both ways, as modes of thinking-and-action that interact in ways we can describe using a logically organized model-for-process.

* Instruction can use a process of inquiry (to let students discover principles of inquiry-process) that is supplemented with explanation-based learning or with whole-part-whole instruction for parts of models or semi-models.

{ full page - Using Models to Teach Inquiry-Process }

▓ Developing Instruction for Teaching Design Process   (into left frame`)

Goals for Collaboration

I want to work cooperatively with other educators to develop instruction for teaching Design Process, using hands-on activities and computer-based activities.

I'll be happy to talk informally with you about my ideas and your ideas, and possibilities for developing them, separately or together.  We can look for areas of overlapping interests, comparing our ideas to find similarities & differences, creatively imagining how combinations of ideas might be mutually supportive.  Or if you see reasons for thinking “no, this probably won't happen,” I'll want to hear why, and whether you think I should be doing anything differently.

Later, if these discussions lead us to discover how we can use my ideas in your current work, or develop new projects, this could be interesting and productive.  And if not, if we just "talk informally," that also will be fine.

If you want to discuss anything in the website please contact me, Craig Rusbult (craigru-att-yahoo-daut-caum).

iou – This section was written in 2012, and recently (early 2023) I've written a new version that – when combined with the ideas below (to be revised in May 2023) – is a better description of possibilities for collaborative co-creation of improved education.

The rest of this page-summary describes some ideas to consider when designing instruction.  Both versions (2012, 2023) are preliminary outlines of possibilities, goals, and strategies, rather than a polished proposal for a project.

Options for Instruction

A Variety of Contexts:  A minds-on activity can be hands-on (with physical & mental activity) or hands-off (with mental activity), done with or without a computer, inside or outside a classroom, individually or in groups.  Most strategies for instruction are similar in most of these contexts (or combinations)*, and the similarities allow flexibility when we are designing instruction, and when a teacher is deciding how to use it for their own situation(s).   /   * 3 variables (with/without, inside/outside, individual/group) form 8 combinations-of-3 that can be mixed during instruction.  For example, teachers use 2 of the 8 combinations when their students do some parts of an activity with a computer, outside a classroom, alone, while other parts are done without a computer in classroom groups.

A Variety of Activities:  Currently, teachers are using many kinds of activities to provide students with experiences that are first-hand (using design-inquiry to solve problems, and science-inquiry to answer questions) or second-hand (by reading case studies) or both combined.

Instruction to teach principles-for-process using Design Process (together with other models for process) can be designed in three ways:

Help Teachers Adapt Old Activities:  We can design illustrations of “how to modify an activity” to show teachers how they can integrate a teaching of Design Process into familiar inquiry activities they already are using, or can find.*

Adapt Old Activities:  To design activities that are ready-to-use (so teachers don't have to adapt), we can modify familiar inquiry activities by adding options for teaching Design Process.  For example, students can learn more when activities using POE (= Predict + Observe + Explain which is a simple framework for science inquiry) or CER (= Claim + Evidence + Reasoning) are supplemented by Design Process.   { Sometimes working with activity-producers would be useful.  And “getting permission” would be required for adapting some activities. }

Invent New Activities:  We can design new Inquiry Activities that include mini-activities — with guiding, requests for reflection, discussions, direct explanations,... — to teach (i.e., to help students learn) principles of Design Process. 

Generation of Options can occur by selection (of old) and invention (of new).  Overlaps between old and new occur because a "new" activity often is invented by revising old activities.

Teacher-Oriented Goals for Instruction

Finding Effective Activities:  * This finding can be difficult and time-consuming for a teacher.  Therefore it's useful when a community of educators (teachers & others) develops creative ways to help teachers find resources – for example, by generating (finding or inventing) options for activities, evaluating these options, and then communicating their conclusions in ways that are "well organized and easy to use, to avoid overwhelming busy teachers with too much information and not enough guidance in how to use it, since one goal is to decrease their prep-time." Ideally, each activity will be...

Easy-to-Use and Flexible:  Generally, an instruction activity should be mostly self-contained (so it's easy to use as-is , and teachers are not forced to invest lots of extra preparation time)* yet flexible with options for customizing (so teachers, if they want, are able to do their own adaptive pesonalizing in the context of their overall strategies for teaching).

* For example, providing ready-to-use computer activities could be one part of a strategy — along with other changes in the overall culture of education — for helping more teachers decide to place a higher emphasis on “thinking skills education” despite rational reasons to avoid doing this.  Computer activities can help teachers in three ways:  by minimizing their extra prep-time, and reducing their concerns about quality of teaching (should this be evaluated by using exam scores of their students?), and reducing the competition of ideas-versus-skills for limited classroom time.

How can we design instruction activities that help students learn more effectively, and match how teachers like to teach (above) and (below) how students like to learn?  We'll examine this question later after looking at...

Student-Oriented Goals for Instruction

If one of our objectives is to design instruction (for a variety of contexts) that integrates principles for process-of-inquiry into inquiry activities, then two pairs of related goals are to develop instruction methods that...

Maintain Flow:  We want to help students regulate their metacognition so they can maintain their flow of creative-and-critical productive thinking during an Inquiry Activity.

Maintain Fun:  Our activities should be fun to use and appealing in many ways — visually, emotionally, and intellectually, challenging (in a good way) and intrinsically interesting with fascinating stories and/or stimulating actions — so students (who are busy with traditional activities plus modern social media, smartphones, video games,...) will be more motivated to use-and-enjoy these activities.  As one part of the overall appeal, when principles of design-thinking are integrated into an activity this should be done skillfully, so students will have minimal feelings that “this is boring” compared with actually doing the Inquiry Activity.   {more about maintaining flow-and-fun}

Allow Immediate Satisfaction:  We should maintain flow-and-fun, and adjust the level of difficulty so students will feel challenged but they will succeed, and will be motivated by the satisfaction from success that improves their self-perception and their desire to...

Increase Long-Term Satisfactions:  We can motivate students to pursue long-term satisfactions by showing them how the ideas-and-skills they are learning will help them achieve their goals for life, so they will want to learn for life by investing in their own personal education.  We can inspire students to think carefully about how they want to optimize their performing-enjoying and/or learning.  Often they will want to "shift their balance from mainly Short-Term Fun/Satisfaction toward also Long-Term Satisfactions," to supplement their satisfactions of performing-enjoying now with the learning that will help them increase their performing-enjoying later.  By using motivational persuasion we can help students appreciate the personal usefulness of improving their thinking skills for doing inquiry, by learning principles-for-inquiry.  But we'll want to do this in ways that are not boring (so the flow-and-fun is maintained) or condescending, so students won't feel “pushed” by us, so instead we design activities to let them be “pulled” by motivations they have self-generated.    { Some ideas about motivations that combine time-perspectives — that acknowledge the value of both now & later, of pursuing short-term & long-term satisfactions — are explored in Tennis and Other Games. }

General Humility:  So far these are just goals, not characteristics of current instruction using Design Process. 

Website and Instruction:  As explained in its homepage, "this website is designed for educators," not students, so "as-is now it isn't suitable for instruction."  My website is not intended to directly achieve the goals of fun-with-flow for students, motivating them to pursue long-term satisfactions, or easy designing of instruction by teachers.  But when we're trying to achieve these goals (fun/flow, long-term motivations, easy designing), some of the website-ideas could, with suitable modifications, be useful for instruction with inquiry activities in the classroom and/or using computers.

Personal Humility, and Collaboration:  With or without computers, it's difficult to design educationally effective activities.  And, especially for computer-based activities, I'm not an expert (compared with others who have more experience & knowledge) so designing effective instruction will require significant collaboration.

Strategies for Instruction

The most important educational function of Design Process is to stimulate-and-guide the metacognitive reflection that will help students improve their Strategies for Thinking – especially their Strategies for Coordinating a process of inquiry in design or science.

A General Strategy for Instruction:  We can use any Inquiry Activity in a sequence of experience, reflection, principles.  The activity provides experience, and we promote reflection to help students learn principles-for-inquiry.

Learning by Discovery:  Students can "use a process of inquiry to learn principles of inquiry-process."  {process-discoveries by students & teachers}

How can we stimulate productive metacognitive reflection while maintaining flow-and-fun?  Useful strategies for helping students “learn more from their experience” during any minds-on activity (done with or without a computer) are similar to a teacher's guiding by asking questions, using Reflection Requests (that direct attention to “what can be learned” from an experience"), providing supportive encouragement and useful formative feedback.

How?  An effective teacher uses externally-oriented empathetic metacognition (to think about the thinking of students) when deciding how to wisely choose the types, amounts, and timings of guidance.*

What?  One goal is helping students learn how to more effectively use on-and-off metacognition in which they sometimes stimulate productivity by using metacognition (when this can improve their performing and/or learning) and at other times allow productivity by avoiding metacognition (when it could be a distracting interference).

Why?  A broader goal is to help students optimize the total value (in performing + enjoying + learning) of their educational experiences.

How?  We can ask “will a well-designed combination of experience + principles be more effective than just experience?” and “should we teach by using a model or semi-model or no model?” and “using what combination of models?”

* Effective teaching requires a combination of planning + improvising.  Doing this well is a challenge for teachers who can improvise (to adjust their guiding) during their real-time interactions with students, and it's even tougher to design into a computer program.

Two Ways to Guide:  A computer program can "improvise" so it's interactive, by adjusting “what it does” based on feedback from what a user does.  It can adjust the main activity, and also its teaching of process-principles with reflection, guiding, and explanations.  And it can offer options (selectable by teacher or students) for “just the basics” or also deeper explorations-of-process in various ways.  /  A teacher can make similar adjustments when selecting a hands-on activity (and maybe customizing it) and then, during it, with differential guiding of students.

Using Both:  Effective guiding — to promote reflection and teach principles — can come from a skillful teacher, or a skillfully designed computer program.  Both kinds of benefits can be combined in ways that are mutually supportive, with eclectic instruction.

Variety of Experiences:  Designing instruction with a variety of activities & contexts provides many benefits, including a wider range of opportunities for success and satisfactions.  Inquiry activities can be used in many subject areas, in many ways, when designing a wide spiral curriculum to help students improve a wide range of ideas-and-skills (and more) that include learning principles for problem-solving process in the context of problem-solving experiences and reflections.

[ to be continued ]

I.O.U. - The rest of this page-summary about "Developing Instruction" is being revised, and might be ready for viewing soon, in late October.

What?  We can design goal-directed Aesop's Activities to teach specific ideas and/or skills, including big ideas (nature of Science-and-Design) and smaller ideas, plus skills (like using critical-and-creative guided generation and other strategies for thinking).

How?  In schools (for K-12 or college), instruction to help students improve their Thinking Strategies can be integrated into regular “content” courses, or taught in separate “skills for learning” courses.  Each approach has benefits, as discussed in Developing Instruction for Metacognition.  During content-courses, a disruption of flow-and-fun can be minimized, which is appealing to the course instructors, by using “metacognitive wrappers” around a main content-learning activity to promote metacognitive reflection.

MISC -- { some ideas for computer-based instruction }  /  in #hwimvar —

{ full page for Designing Instruction to Teach Design Process }

▓ Experience plus Principles   (into left frame`)

I recommend reading the revised version of this page-summary.

In why we should teach Design Process I describe the "many logical reasons to conclude that Design Process might be very useful in education, so its possibilities are worth exploring and developing."  Based on what we know about thinking, learning, and performing, we should expect a well-designed combination of "experience plus principles" to be more educationally effective than experience by itself, to help students improve their thinking skills and whole-process skills in solving problems (for design-inquiry) and answering questions (for science-inquiry).

Why?  What are the potential benefits of supplementing inquiry-experience with inquiry-principles?  When thinking about these questions, here are some ideas to consider:

• Discovery and Explanation:  principles of inquiry can be (and I think should be) taught mainly by discovery learning — based on experience plus metacognitive reflection — supplemented by explanation-based instruction in an eclectic blend.

• Flexibility in Thinking and Instruction:  The previous page-summary compares three kinds of teaching strategies for teaching inquiry-process, using no model, a semi-model, or model.  Some educators with a strong preference for no model or a semi-model seem to imply that “a model should never be used.”  They may want to examine their assumptions and consider the potential benefits of sometimes using models.

• 3 Goals:  When evaluating effectiveness, we must ask “effective for achieving what goals?”  In the context of our overall educational goals for ideas-and-skills and more, my three main goals for Design Process are to accurately describe design-and-science,* and to help students understand it, and improve their skills in doing it.  These goals are closely related, due to mutually supportive relationships between cognition (for descriptions & understanding) and cognition/metacognition (for skills), but are not the same.  The similarities and differences between these goals are important when we compare the educational effects of experience-only and experience-plus-principles, because the effects of instruction will differ in some ways for these three goals, and for other goals.   /   Here is a central question, asked in two ways:  Will a thorough-and-accurate understanding of design-and-science help you improve your skills in doing it?  What are the relationships between metacognitive knowledge & reflection and quality of performing-learning-enjoying?   /   * Instead of “design and science” I'm calling it "design-and-science" to emphasize their close connections, because science is a special type of design.

• 4 Types of Evidence:  For evaluations we can use logical “if... then...” predictions (as in 1,2,3) and experimental observations (in 4).   1) Based on observations in research-experiments, How People Learn recommends that to increase transfers of learning (it's "the ultimate goal of learning") we should teach knowledge in multiple contexts, in a form that can be easily generalized.  We can do both of these with Design Process, so our logical expectation is that IF we use Design Process, THEN transfer will increase.  Using similar if-then predicting,...   2,3) Most educators acknowledge the effectiveness of using metacognition (2) and organizing knowledge (3) so we should ask “can either of these, or both, be improved by using Design Process?” and (if yes) “in what ways would this improve our educational effectiveness?”   4) Does empirical evidence (observations in research-experiments) show that instruction is more effective, or less effective, when inquiry-experience is supplemented with inquiry-principles?  in what ways?  how does the effectiveness change when inquiry-principles are taught with instruction that includes a model, instead of a semi-model or no model?  what will change if the model is Design Process?   /   1+2+3 plus 4:  These "four types of evidence" overlap, because the first three claims (re: transfer, metacognition, organization) are related, and are based on observations.

Analysis of Multiple Factors:  For all kinds of evidence (using Observations or Predictions), useful evaluations require complex analyses of multiple factors.  We try to understand relationships among factors by using analogical reasoning that carefully considers similarities and differences in:  the situations (including uses of instruction) observed in past research, and planned for the future;  the models-for-process used in past research, and how these models differ from Design Process;  the assessments used for research, re: kinds of ideas & skills observed, how they were observed, and how closely these ideas & skills match the educational goals we think are most important.

Conclusions and Claims

This page asks a question — in what ways does scientific evidence-and-logic support my claim that inquiry-experience should be supplemented by teaching inquiry-principles using Design Process? — but doesn't try to “prove” my claims are correct.  Instead I describe, with humble confidence,* logical reasons to decide that Design Process might be very useful (with many benefits for education) so the possibilities are worth exploring and developing.

* My confidence is balanced with humility, specifically because Design Process has not been used in classrooms to allow observation of results, and generally because:  we need more evidence from research by observing the effectiveness of instruction (of various types) in helping students learn ideas-and-skills (of various types);  and the logic will be challenging (because "accurate evaluations require complex analyses of multiple factors");  and our questions (about the principles-for-process to teach, and how to teach them) also involve many interactive factors.

{ full page for Experience plus Principles }

 ▓  Modes of Thinking-and-Action in Design    (into left frame`)

This page supplements Five Stages in a Learning Progression for models of Design Process by offering a different perspective.  It describes a semi-model system of functionally related thinking-and-action modes that — when the modes are logically organized to show how flexible sequences of thinking-and-actions are skillfully coordinated during a process of design — form an overall model of Design Process.   {model, semi-model, or no model}

So you can get a quick overview, here are the 10 modes (in 4 categories) used in a process of design:

1. DEFINITION  (at top of Diagram 1`)

1A. Choose an Objective (what you want to design) for a Design Project

1B. Define Goals (for the desired properties of a problem-Solution)

2. GENERATION  (in Design Cycle of Diagram 1)

2B. Invent Options (by modifying old Options, or with innovative new types of options)

2C. Design Experiments (to use for Mental Experiments, Physical Experiments, or both)

2D. Predict (by doing a Mental Experiment, make Predictions to use in 3A & 3B)

2E. Observe (by doing a Physical Experiment, make Observations to use in 3A & 3B)

3. EVALUATION  (in Design Cycle of Diagram 1)

3A. Evaluate Options using Quality Checks (compare Goals with Predictions or Observations)

3B. Evaluate Models using Reality Checks (compare Predictions with Observations)

4. COORDINATION

4A. Evaluate the Process and Make Action-Decisions (for what to do & when in Modes 1-4)

These 10 modes are not 10 steps, because Design Process is not a rigid sequence of steps`, so thinking-and-action in different modes often overlap with productive interactions between modes.

Design Process shows the coherent integration of creative-and-critical thinking skills (in these modes) to form a productive thinking process.  I think teaching Design Process can be most useful for the final mode, to help students improve their Strategies for Coordinating a Process of Design.

I.O.U. — Maybe the related “community skills” of Collaboration and Communication will be added to these modes of thinking-and-action.  Later, maybe in January, I'll think about this more deeply.

This page-summary describes the thinking-and-actions in each mode, beginning with Mode 1A.

1A — Choose an Objective for a Design Project

Grounded in your knowledge of what is (due to your Preparation) and inspired by thinking about what could be, you Choose an Objective by recognizing a problem (it's an opportunity to make things better) and then deciding to pursue a solution (by designing a better product, activity, strategy, or explanation) because — after you have considered the potential benefits and probability of success, comparing these with the alternatives (other ways you could use your limited resources of time, people, and money, plus available knowledge & technology) — you make a strategy-decision that this Design Project will be a wise investment of your resources.

{the full-length page has more about 1A/1B and 2A}

For 1A & 1B, above & below,

Perspective:  An essential part of skillful designing is doing what is needed to define the stakeholders — everyone who will be involved in or affected by your project — and think with empathy in an effort to understand their perspectives, how they think & feel, what they need and want.

Quality Control:  After a problem-solution is chosen and it's actualized by converting it from an idea into reality, what Quality Controls — to observe and improve the Quality of Actualizing — will be useful?

Communicating:  During most design projects, skillful communication is useful in many ways, internally (for collaborative teamwork) and externally (for marketing,...).

1B — Define Goals for a Solution

Define your Goals for the desired Properties of a problem-solution by asking “What do I want?”  Properties can be:

• specifications for desired characteristics of a product (for “what it is” composition, “what it does” functions, and “how well it does the functions” performances), or activity (ask “what, when, where, how, and who” in the context of “why”), or strategy (ask “what results do we want?”), or explanation (for Predictive Accuracy and other characteristics-criteria).

• practical or legal constraints on a permissible solution (for its production cost, selling price, safety,...) and on the process of design for budgets (in its use of resources: people, time & wages, capital investments,...) and for timings (with a final deadline for completion, and maybe sub-deadlines during the project) and maybe for other criteria.

{more about 1B}

GENERATION of Information:

Old (in 2A) and New (in 2B, 2C-2D-2E)

2A — PREPARE by Finding Old Information

As shown in Diagram 1` (and 2a-2e, 3b), you "Choose an Objective" (in Mode 1A) and "Define your Goals" (in 1B) and — both before & after 1A/1B — you "Prepare by finding information" (in 2A).  Why?  In an effort to understand a problem-situation more accurately and thoroughly, you FIND useful old information.  (here, "old" means already known by someone, not outdated)

Two Kinds of Memory:  When you search for old information, you can REMEMBER it in your personal memory, or LOCATE it in our collective memory, in what is recorded (culturally remembered) in books, journals, web-pages, audio & video recordings,...

You can find a wide variety of old information (known information) about problem-Situations, and Options (candidates for a Problem-Solution), Option-Properties (predicted or observed), Experiments (mental or physical), and Explanatory Theories & Models.  In addition to these ideas, in some situations you'll want to learn more about skills that might help you during a process of design, or that are the objective of design.

{more about 2A}

GENERATION - Old and New, by Selection and Invention

All of this old information (found in Mode 2A) is analogous to new information (made in 2B-2E) because GENERATION (of Options, Experiments, Predictions, Observations) includes both old and new.  What someone already did, to allow your finding in 2A, you now can do to get new Options (2B), Experiments (2C), Predictions (2D), and Observations (2E).

In Stage 2a (using Diagram 2a) when you "Choose an Option to evaluate" you can find-and-select an old Option, or invent a new Option, or improve an old Option.

{more about Old-and-New with a table}

Mixing of Modes – with Productive Interactions

Because the modes are not steps there are frequent overlaps between thinking-and-actions in all of the 10 modes, with mutually supportive interactions.  Here are some examples of flexibly improvised timings:

Beginning:  1A is numerically first in a sequence of mode-numbers (1A, 1B,... 4A), but Mode 1A is not the beginning of thinking-and-action in a process of design.  When you are Choosing an Objective (in Mode 1A) you already should be "grounded in a knowledge of what is" due to Preparation (in 2A).  And when you "think about what could be" (2B), knowing more about "what is" (2A) helps you Predict (2D) whether another Option might "make it better" compared with existing Options, and decide (using Evaluations in 3A & 3B) if trying to achieve desirable Goal-Properties (1B) is worth investing your valuable resources in a Design Project.  You can see why making wise choices about a Design Project (in 1A) requires using all modes of thinking-and-action, including 4A when you decide "what to do next" in a mixing of modes.

Continuing:  Later in a process of design you can return to “earlier” modes — with flexibility of timing shown by two arrows, ↓↑ , connecting the top of Diagram 2A` (Modes 2A+1A+1B) with Design Cycles of Generate-and-Evaluate — because:  you want to learn more with more Preparing (2A);  or you re-Define Goals (1B) because you “recognize what you want when you see it” while you're Generating-and-Evaluating Options (2B, 3A);  or you may be inspired to see a new Objective (1A).   /   * As indicated by the large down-arrow, usually these actions are done early in a project.  But they also can be done later, with flexible improvisation during Design Cycles, thus the smaller up-arrow.

Guided by Goals:  Your desired Goal-Properties (from 1B) become a focus for action, providing “aiming points” to guide a creative Generation of Options (2A+2B) and a critical Evaluation of Options with Quality Checks (3A) in which quality is defined by the Goals (1B) that are your evaluation criteria.

Other interactions include the central role of experimenting (2C-2D-2E) in design, and how Evaluation (3A or 3B) stimulates responses in all modes (which can be mixed into Design Cycles of Generation-and-Evaluation), and an action-coordinating mode (4A) when you decide "what to do next," and more.

Diagram 3b includes two more design-actions – Collaboration & Communication – that are part of the process (typically from start to finish) during all group projects and some individual projects, so "usually" you do these actions.

2B — INVENT Options for a Solution

You can Generate Options by selecting an old Option (in 2A) or inventing a new Option (in 2B).  A thorough searching-and-finding in 2A can help you in two ways, when it provides an old Option you may want to use as-it-is or with revision.  But knowing about old options can hinder your Free Generation of Ideas if a knowledge of “the way things have been” becomes a mental rut, an assumed certainty about “the way things must be” that restricts your freedom of thinking.

You can creatively invent a new Option by combining any of these four strategies:  Free Generation, with creative thinking liberated by "reducing restrictive assumptions";  Guided Generation, when "critical Evaluation stimulates-and-guides your creative Generation";  Revision of an old Option, perhaps by using Analysis in which you "consciously analyze an old Option into its features, and think about ways to revise each feature."

Revision and/or Innovation:  You can invent by beginning with an old Option and revising it, or by free-inventing an option that is less connected with the old so it's “more new” and, by definition, is more innovative.

2C-2D-2E — EXPERIMENTING in Design Process

Design, Do, Use:  In any process of design, whether the objective is to design a Solution (in General Design) or an Explanation (in Science), experiments are a focus of action:  you design Experiments (here in Mode 2C) and do them to make Predictions (in 2D) and Observations (2E) that you use for Evaluation in Quality Checks (3A) and Reality Checks (3B).     {more – a deeper examination of these ideas}

2C — DESIGN Experiments (Mental & Physical)

This section builds on the foundation of a visual-and-verbal outline of Experimental Design — that shows why an Experimental System is an Option-in-a-Situation for General Design, and is a Model-Situation for Science, and how you imagine (to do a Mental Experiment) or actualize (to do a Physical Experiment) so you can make Predictions or Observations — and a condensed summary of this section (2C) about Designing Experiments.

also:  For deeper explorations, in the context of my PhD work about Scientific Methods of Thinking-and-Action, An Overview of Experimental Design and Details about Creative Experimental Design – about goal-directed design, anomaly resolution, crucial experiments, heuristic experiments, vicarious experimentation, thought-experiments, and more.

As with all design, in Experimental Design you use iterative cycles of design to creatively Generate Options for Experimental Systems, and critically Evaluate these Options by asking “which E-Systems can produce information (Predictions or Observations) that will be useful for our Design Project?” because it will help us design a Solution (in General Design) or Model (in Science-Design).  Then you choose which E-System(s) to use.

Old and New:  As with all Generation of Options for a Solution, you can Generate Options for an Experiment (which is a special kind of activity) by selecting old ideas and inventing new ideas.  The old information you find while Preparing in Mode 2A includes old Solution-Options, and their Properties that were observed & predicted in Physical Experiments & Mental Experiments.  And you can ask “how were the old Predictions and old Observations produced?”  Maybe you can do what they did by using these old Experiments — as-is or revised, mentally or physically — to produce new Predictions (in 2D) or new Observations (in 2E).  And you can invent innovative new types of experiments.

Similar ideas are in this paragraph from my revised page-summaries:*

Old plus New:  All of this OLD information (searched for and found in Mode 2A) is equivalent to, or analogous to, NEW information (produced in Modes 2B-2C-2D-2E) because GENERATION of Information (of Options, Experiments, Predictions, Observations) includes both OLD (existing) and NEW (produced).  The thinking-and-actions that someone did earlier to produce the OLD information you find in 2A, you can do now to get NEW Options (in 2B) for Solutions or Models, and NEW Experiments (2C) that allow NEW Predictions (2D) and NEW Observations (2E).    {a table - Generating Information (old & new) and Using It}     {* But "Old and New" is located here in Mode 2C, while "Old plus New" is between Modes 2A and 2B. }

comments:  Currently this section (2C) is the longest in this page-summary for Modes of Design-Thinking because these ideas are complex, are useful for Design Thinking (to Design Experiments that are useful for Science-Design and/or General Design) and for education.  One theme that is repeated and is emphasized, because it's important, is...

The Educational Benefits of Using Broad Definitions by "defining three major terms – predictions & observations, experiment – in ways that are simple and logical, broad and minimally restrictive."

Creative Thinking – to Generate a Wide Variety of Options

A Wide Variety of Options:  Creative designers, in Science and General Design, use a wide variety of experimental situations — inside and outside a laboratory, with control-of-situation ranging from total to none, with observations (made by humans and instruments) that are qualitative & quantitative — in their investigations of nature and technology.

We can stimulate the creative thinking of students — encouraging them to explore this wide variety of Options for Experimental Systems — by using a simple, broad, minimally restrictive definition:  an Experiment is any situation that provides an opportunity to get information by making Predictions (in a Mental Experiment) or making Observations (in a Physical Experiment), so an Experimental System is any Prediction-Situation or Observation-Situation. 

This broad definition — with experiments including all "opportunities to get information" mentally and/or physically — is relatively non-restrictive, which can make it easier for students to use fluent non-restricted Free Generation by "freely imagining ... to consider the full range of options" for experiments.*   An improved fluency is especially useful for mental experimenting, to let students experience this valuable mode of design-thinking more often, to help them understand the process and improve their skills in designing, doing, and using Mental Experiments, and also Physical Experiments.    { significant educational benefits also arise from a broad view of Observations }

* Students can expand their "full range" so it includes "all opportunities" by creatively thinking about ways to ask “what can we observe, and how?” with detectors (using human senses and external instruments) for a particular Experimental System, or a kind of system (a domain of systems), or in a wide range of diverse systems.  They can imagine how Mental Experiments (including simulations) can let them explore experimental systems that would be difficult or expensive with Physical Experiments, by imagining a skyscraper in an earthquake, or an observer moving at the speed of light.  Or imagine ways to do quick-and-cheap Physical Experiments with prototypes that can take many forms, including physical scale models.

Critical Thinking – Using Logic to Design Experiments

We should combine Creative Designing of Experiments with Critical Thinking about Experiments.

Creative-and-Critical Thinking:  For a Designing of Experiments, as in other areas, thinking that is productive requires using ideas-and-skills by combining a knowledge-of-ideas with creative thinking and critical thinking."  In one type of creative-and-critical thinking, a creative Generation of Ideas [for Experiment-Options] is stimulated-and-guided by a critical Evaluation of Ideas" in retroductive Guided Generation when you imagine running many kinds of experiments, and for each you ask “what might happen, what would we learn, and how could this be useful?”  Your generation-of-ideas using retroductive reasoning — guided by evaluative Quality Checks or Reality Checks that require mental experimenting — can be used to generate Experiment-Options, Solution-Options, and Model-Options.

Goal-Directed Design:  Sometimes experiments are done just to see what will happen, to increase our knowledge about some interesting aspect of the world.  But we also can design experiments to accomplish a specific function during a process of design, to get information (predictions or observations) "that could be useful for our Design Project," near its beginning (to help in defining goal-criteria for the desired properties of a problem-solution) or later (to generate information about options).  In a coordination strategy you ask “where are we, in our process of design?” and — perhaps by finding “gaps in knowledge” to fill with predictions or observations — you search for experiments that will provide information to help you answer a question, support an argument, or design a solution.

Functions in General Design and Science-Design:  Sometimes these functions overlap so "an engineer sometimes does Science."  This occurs when an experiment lets you evaluate the Quality of a Solution-Option (for General Design) and also understand (with Science-Design) the factors affecting its Quality, which helps you find ways to improve the Option's Quality (for General Design).  Engineers want to increase their knowledge about nature in the context of technology so this understanding can help them design better problem-Solutions.   {examples of science within engineering}

Creative Analysis:  In General Design, usually an experimental system is "a Solution-Option operating in a Situation" so you can keep one constant while varying the other:  in the same Situation, test various Options so their properties (mentally predicted or physically observed) can be compared;  or test an Option in various Situations, to learn more about this Option.  Or you can use other strategies for controlling experimental variables, as discussed above.   {more about Creative Analysis}

Understanding Cause-and-Effect:  Here are a few of the many questions we can ask about variables in Experimental Systems:  is a potential causal factor (C) necessary to cause an effect (E)? is it sufficient? by itself, or combined with other factors?  what can we conclude if a set of experiments shows that “if C, then E”, or “if not-C, then not-E”, or both, or neither?   {You can design a series of E-Systems so it will help you identify causes, or analyze a series of known Systems-and-Observations, by using Mill's Methods of Logic.}

Recognizing-and-Reducing Errors:  For all systems, including those with a population where sampling is important, scientists (and students) use imagination-and-logic to find systematic errors & random errors and reduce them, to increase accuracy & precision.  Sometimes they can do cross-checking by designing sets of experiments that measure “the same thing” in different ways.  Why?  Thinking about sources of error can help you estimate the credibility of observations from an experiment, or revise-and-improve it, or generate other new options for experiments.

Considering Constraints:  Another aspect of critical thinking, useful in the classroom and beyond, is being practical by asking “what experiments can be done using the available resources of time, equipment, money, people, and knowledge.”

Teaching and Learning:  Students can learn principles of experimental logic from an eclectic blend of instruction that includes their own first-hand experience, plus case studies for second-hand experience that lets them "learn problem-solving principles... in a wide variety of situations, in an interesting, time-efficient way."

Learning for Life:  When students learn the logical principles of Experimental Design, this improves their own design-thinking (for Science & General Design) and also their abilities to critically analyze the experiment-based claims made by others, which is essential for scientific literacy and using the skills of science in everyday life.

Control of Variables in an Experimental System

When you critically evaluate an experiment-option (a potential experiment) — to determine how useful it could be in Science (in a process of trying to answer a question) or General Design (trying to solve a problem) — an important question is the control it allows.  For each experimental system (each observation-situation) you can ask “what kinds & amounts of control are available?”

In a broad spectrum of experiments "with control-of-situation ranging from total to none," at one end is totally-controlled experiments, and the opposite end is uncontrolled experiments with no control.  These correspond roughly to lab experiments that usually allow significant control (maybe total control for all variables that matter)* and field studies in which there is much less control (and perhaps no control).   /   * An experiment can never be totally controlled.  Why?

* What is an experiment?  Sometimes an experiment is defined narrowly to include only a situation where some control is available.  With this requirement, uncontrolled experiment is an oxymoron.  By contrast, I use broad definitions of 3 related terms — predictions are the result of predicting (in any way), observations are the result of observing (in any way), and an experiment is any situation that allows making predictions and/or observations — because this seems educationally beneficial for helping students improve their skillful coordination of mental and physical experimenting`.   {examples of Non-Control and Control}

When control is available, students ask “what variables can be controlled?” and “what variables should be controlled?”  They can learn logical principles that include Mills Methods of Experimental Reasoning for designing a set of related experiments with Agreements or Differences,... as in a “fair test” by changing only one variable at a time.  In addition to these basic principles, we can help them learn the reasons for blind (or double-blind) studies, and much more.

But even when you have no control over a situation, you have choices.  An example of “no control but many choices” is astronomers who study the life cycles of stars.  They cannot control what is happening in "situations" billions of light-years away, billions of years in the past, but they can decide what to observe and how.*  By choosing which direction to look (into the center or edge of a galaxy that is old or new, or into intergalactic space) and how far away (near or far, to observe relatively recent action or events in the distant past) and what to observe (electromagnetic radiation that is IR, visible, UV, x-ray,...), they control their observations and the data they collect.  Then, with a creative analysis of their data they “control variables” by choosing from among the vast number of experimental systems produced in nature, which span a wide range across many variables (e.g. timing in a star-cycle, mass of a star, location inside or outside a galaxy,...) to help them develop descriptions and explanations for the life of a star, from birth to death, even though the lifetime is billions of years for most stars.  And occasionally scientists can observe an entire supernova death-event (and its main aftermath) that occurs more quickly, during the lifetime of an astronomer.

* Observation-Detectors (humans and/or instruments) are part of an Experimental System that is used to develop a System-Model and make Predictions.

With other types of field studies in historical sciences (astronomy, geology,...) and in biology, nutrition,* medicine, meteorology,..., scientists can do similarly creative Experimental Design in their choices about making observations, and then in analyzing observations-data.   /   * For example, to develop knowledge about Mediterranean Diets we first noticed that people with an “unhealthy diet” (according to some conventional theories of nutrition) were healthy in important ways, then in our field studies of these people we asked “what is different about their diet, and why might this help them be healthy?”

Regarding a control of variables, many Experimental Systems are near the ends of a spectrum-of-control (i.e. they are mainly controlled or uncontrolled, as in a typical lab experiment or field study), but between these extremes are many semi-controlled Systems.

MORE – This section (and its links) is a “sampler” for the fascinating activity of Experimental Design, and — using the perspective of a model developed during my PhD work, as described in — so is Designing Experiments.

2D — PREDICT by doing a Mental Experiment

You make predictions in a two-step sequence:

1. Construct a System-Model:  If you want to know “what is happening and why” in an Experimental System (aka E-System, or System), you try to describe-and-explain by constructing a System-Model to describe the System's composition (what its parts are) and operation (what the parts do, and why).    { Models & Theories, Hypotheses, Explanations describes how a Model often is constructed by applying a general Theory to a specific System. }

2. Use If-Then Logic:  You do a Mental Experiment with a System-Model, making Predictions using if-then logic by reasoning that “IF the system behaves as expected (according to my System-Model), THEN my Prediction (my logical expectation) is that       will happen and       will be observed.”  You can predict using if-then logic in several ways, with model-based deduction (using deductive logic, assuming the System-Model) or model-based simulation (e.g., by running a computerized System-Model)* or experience-based induction (by assuming that what happened before, in similar situations, will happen again), or in other ways.

Interpolation and Extrapolation:  Any if-then prediction can be an interpolation — for a System within a domain of Experimental Systems in which observations are known, or a model is assumed to be valid — or an extrapolation outside this domain.   /   In a simulation you also can use a Domain-Model (assumed valid for a domain of related E-Systems, not just for one System) to make predictions for a collection of E-Systems.

Timing:  Although prediction implies that both steps (for System-Model and If-Then Logic) are done before Observations are known, it makes no logical difference if these are done after Observations are known, as in retroductive Guided Generation of Model-Options to use for an Explanation that is an Argument.  But when Retroduction, which is guided by known Observations, is used to creatively generate Model-Options, there should be more concern about the possibility of using ad hoc adjustments to achieve a misleading match between Model-based Predictions and the known Observations.  {you can search for "ad hoc" here to find 4 sections about concerns}  {Predictive Deduction is reversed when you do creative Retroduction}

MORE about Using Models-and-Logic to Predict

If theory is defined broadly (as I recommend for education) and we use a deduction theory or simulation theory (by assuming the system-model or simulation is approximately accurate) or an inductive theory (assuming "what happened before... will happen again"), then all 3 types of if-then predicting (by deduction, simulation, induction) use theory-based models.

By using induction, you can predict without constructing a System-Model.  But educators usually want students to ask "why?" because this will help them transfer their scientific understanding into new situations, so we should encourage students to supplement their inductive predictions by also constructing model-based predictions.

This process of logic (using a system-model to make predictions) is sometimes called "if... then... because", but "because... if... then..." describes the process-timing (of Steps 1 & 2) more accurately.

2E — OBSERVE by doing a Physical Experiment

Choose an Experimental System, then physically actualize this E-System — which in General Design requires actualizing the Option by "making (or obtaining) a product, doing an activity, or applying a strategy" — so you can do a Physical Experiment and make Observations in the ways planned in your Experimental Design or by improvising.  Usually we want observations to be both precise and accurate;  in most experiments this requires careful attention to details when designing the Observation-Situation, the Experimental System.

What is an Observation?  As with defining experiments, many benefits — for creative problem solving and effective education — arise from a definition that is broad and non-restrictive:  an Observation is the result of observing in any way, by using human senses or technological instruments, to get information that is qualitative or quantitative.  We can observe by using human senses (to see, hear, touch, taste, or smell) plus the instruments we have developed (e.g. a ruler, scale, pipet, watch, thermometer, microscope, telescope, spectrometer, chromatograph,...) that help us measure more precisely and observe more widely.  Usually, scientists (or engineers, teachers, students,...) consider observations to be the result we get after raw information (from senses or instruments) has been translated into observations that we record using symbolic representations that are verbal (with words,...), visual (pictures,...), and mathematical (numbers,...).

Observations are Data:  In science and other areas, including education, observations are also called data.   /   Although the meanings of observations and data are almost the same, in most ways in most contexts, there are minor differences, especially in connotations due to the differing ways that each term (especially data) is used in science & engineering & other fields & education, and in everyday language.  For example, sometimes data means observations that have been organized in some way, to achieve an objective such as easier analysis or (in a later stage) better communication.

3A — EVALUATE Options by using Quality Checks

Evaluations using Quality Checks — with the Quality of an Option defined by our Goal-criteria for a satisfactory Solution (in General Design) or Explanation (in Science) — are very important in Design Process.

Evaluation is used for Argumentation:  The results of Evaluation are used in a process of Argumentation* that can occur within a person or in a group.*   /   terms:  An argument can refer to the process of arguing, or the resulting logical statements, the individual arguments that combine to form an overall argument.

* During a process of Argumentation, you combine Evaluative Thinking (based on evidence-and-logic) with a Strategy for Persuading and Skills in Communicating.

* Attitudes while Arguing:  When two or more people argue — with each person making claims (based on evidence-and-logic, values & priorities) about the quality status of different options, in an effort to influence evaluations, decisions, and actions — they often have multiple motives.  For everyone the main motivation should be a desire to choose the best option and do the best action, because everyone cares about the outcome.  But often egos become involved, so the arguers also want to influence how other people view the arguers (themself & others), or to exert personal power over the group's thinking & actions.  Whatever the motives are, attitudes of antagonism (with angry confrontation) should be minimized in a classroom, and in most other contexts.

Arguments in Education Standards:  Skills of Argumentation are featured in the new Common Core State Standards (CCss) and Next Generation Science Standards (NGSs) the Scientific & Engineering Practices include "Engaging in Argument from Evidence" because students should "understand the culture in which scientists [and engineers] live, and how to apply science and engineering for the benefit of society."  Students should improve their abilities in "using argumentation to listen to, compare, and evaluate competing ideas,... using evidence [+ logic] to identify strengths and weaknesses of claims" in "the process of argument" that designers use to decide what is "the best explanation for a natural phenomenon [in science]... or the best solution to a design problem [in engineering]."

Making Decisions about Options:  In a design project, solving a problem (in General Design) or answering a question (in Science) requires decisions.  Usually this occurs by assigning Quality Status and Optimizing.  You assign a Quality Status for each option by "combining evaluations from multiple Quality Checks [in which you compare many Predictions & Observations with many Goals, for many Options]... so all things are considered."  If the overall Quality Status of one Option is much higher than for all other options — and if you are confident that your generation of options has been thorough, so generating a better option is unlikely — the decision is easy.  But usually "you will have a tough competition because several options offer different benefits... so you must set priorities... and use trade-offs... to design an optimal Solution" for achieving your prioritized Goals.   {more about using multiple Quality Checks}

Making Decisions about Actions:  After you do an Evaluation with a Quality Check or Reality Check, you have a wide of options-for-action, because your next action can occur in any of the 10 modes of thinking-and-action.    You can:  continue evaluating (in Mode 3A or 3B);  recognize a knowledge-gap, so you Prepare more thoroughly (2A) to fill the gap with old information, or to generate new information you design an Experiment (2C) and do an Experiment (2D or 2E) to produce new Predictions or Observations;  generate more options (2B), perhaps by combining the best features of several options (each "offering different benefits") into a hybrid option;  revise some Goal-Properties (1B);  think about the overall project (in 1A) and decide whether to continue working on it, or delay work for awhile, or abandon it, or convert it into a related project by revising its objectives, or supplement it with a new spinoff project;  decide that one Option is a satisfactory Solution (3A) so you then begin working on sub-projects (1A) to manufacture, market, distribute, and sell it;  think about "what to do next" (4A) so you can Coordinate your Process of Design.

3B — EVALUATE Models by using Reality Checks

This section supplements "Goals for an Explanation" in An Overview of Science Process` with details about three topics:  The Logical Foundations of Science;  Evaluating Plausibility-and-Utility;  Making Decisions about Models & Explanations.

Theory, Model, Hypothesis, Explanation

These four terms are similar because all are human efforts to describe-and-explain “what is happening and why,” and all are used in a process of designing Explanations.  We'll examine their similarities, differences, and relationships, to supplement the basic definition of a System-Model`.

Theories:  For scientists, the usual meaning of Theory is an Explanation that has wide scope (a large domain that includes many experimental systems) and strong support (high Quality Status) based on their evaluations using evidence-and-logic.  More generally, a Theory is any attempt to understand “how the world works”, and we use all of our theories (our scientific theories, personal theories, business theories,...) to make predictions that help us make wise decisions in life.  In this everyday definition, a Theory's scope can vary from narrow to wide, and its support from low to high.

Defining "theory" for Education:  Teachers can begin with these two definitions, scientific (narrow) and general (broad) — by explaining them, and comparing them to show similarities & differences — to help students understand the wide range of ways that theory is used, inside and outside science.  Because scientific reasoning is used (and should be used) in everyday life, I describe transfers of ideas-and-skills from school into life: "in all of life, not just in science, we use our theories about ‘how the world works’ to... make predictions that... can help us make wise decisions."  This is a direct strategy to minimize linguistic confusion, not an indirect strategy that simply ignores everyday definitions.  Does a direct strategy have any disadvantages?   /   Here in Mode 3B, I'm mainly using the scientific definition, partly for consistency with the new Science Standards that define the main objective of science as the designing of explanations rather than designing theories or models, so they encourage using explanations (not theories) "in all of life."  But elsewhere I also use the general definition because it offers motivational benefits by encouraging students to use scientific reasoning in "science" and also to transfer it into their everyday lives.   I think it's easy to describe how students can use their personal theories about how the world works;  by contrast, it seems more awkward to describe their personal explanations of how the world works, or their personal models about how the world works.

Models:  In science, Theories and Models are very similar.  Both describe an experimental system's "composition (what its parts are) and operation (what the parts do, and why)."  But in science the status of a Theory is usually high, while the status for Models can range from low to high.  And a Model is usually less general than a theory, with a smaller domain, because most Models are constructed for one Experimental System (to make a System-Model) or one type of system (to make a Domain-Model for this domain of related systems);  often a Model is made by applying one or more wider-scope Theories to a specific System or type of system.  During a process of constructing Theory-based Models, usually Models become more simplified compared with the Theory(s) and with reality;  for any System, many different models can be made by deciding which aspects of a “complete model” to include, what approximations to make, and representations to use.   /   Or models can be formed in other ways, intentionally or unconsciously.  This occurs, for example, in experience-based inductive reasoning when you use if-then logic to predict by "assuming that what happened before, in similar situations [for similar systems], will happen again," which requires comparing your models-of-systems (past and current) to recognize that it's a "similar system."

Model-Representations:  Our external representations of a Model — the Conceptual Models (visible, audible, mathematical,...) that we use for communicating ideas, and to support internal thinking — can take many forms (verbal, visual, mathematical, and/or physical*), abstract or concrete, expressed in words (written or spoken), pictures, diagrams, graphs, tables, equations, simulations, analogies, or in other ways, to represent parts of a model or all of it.  Also, each of us personally constructs our own Mental Models that are internal representations of a Model.   /   * A physical representation can be:  a Conceptual Model (like the cardboard scale model of DNA made by Watson, or a classroom's lamp-and-balls model of the Solar System) used for mental experiments or mental/physical simulations;  or a Prototype Model (a physical actualization of a Solution-Option, such as a scale model that “works” in some ways) used for physical experiments and/or mental experiments.

Hypotheses:  A Hypothesis claims that a System-Model is similar to an Experimental System.  We can form different Hypotheses, using the same System-Model, if we answer the questions “how similar, and in what ways?” by making different claims for which aspects of the Model's composition-and-operation are similar to the System (and how similar are they?), and for our expectations of Predictive Accuracy.  A “strong” Hypothesis might claim an exact correspondence in all ways between the System-Model and System, with perfect Predictions, while a “weak” Hypothesis might claim only a rough approximation, or a correspondence (exact or approximate) for only some aspects but not for all.

a tip for teachers:  When we're aiming for accurate communication, a practical problem is that the terms in this section, and their relationships, are complex and subtle.  For example, the term hypothesis is often used incorrectly, as when hypothesis and prediction are treated as synonyms;  yes, a hypothesis is used when predicting, but it isn't the prediction.  A possible solution:  during instruction, abstract descriptions of complex concepts should be illustrated with concrete examples, and combined with student activities. {I.O.U. - later, there will be commentary and examples: 1 2 3 4 5 }

Explanations:  Because a Hypothesis is a potential Explanation — it's an Explanation-Option, a hypothetical Explanation being proposed as a possibility that (as-is or revised) could become an Explanation — we also "can form different [Explanations] using the same System-Model" for one system.  Or, for a wider domain of systems an Explanation can be based on a Domain-Model or a Theory, or a combination of several Models and/or Theories, so we have Explanatory Models (used in a Model-based Explanation) and Explanatory Theories (in a Theory-based Explanation).  Due to the interactive relationships between Hypothesis, Model, Theory, and/or Explanation — which "are similar because all are human efforts to describe-and-explain" — all can be evaluated using Quality Checks.  Due to overlaps in what is being evaluated, this website often will describe the generating-and-evaluating of explanatory Models (or Theories or Hypotheses) that occurs during our designing of Model-based Explanations.

Designing an Explanation:  The usual main objectives of science are to design Experiments (in Mode 2C), and design Explanations in a 3-step sequence:  First you construct a System-Model for the Experimental System.  Second, you form a Hypothesis (an Explanation-Option) by proposing that the System-Model is similar to the System (in some aspects of its composition-and-operation, to some degree) and thus it might explain “what is happening and why” in the System.  You can evaluate the Hypothesis in a third step, which is using...

Hypothetico-Deductive Logic:  Diagram 3c` (with “context” shown above it, by the unshaded part of Diagram 3b) shows how to test a Hypothesis — which proposes that "System-Model ≈ System" (that they are “approximately equal” because they are "similar... in some aspects")* — with a Reality Check by comparing Predictions (made in a Mental Experiment by "using if-then logic" assuming the System-Model)* with Observations (made in a Physical Experiment in "a physical running of the System").  This helps you estimate its Predictive Accuracy, which usually is the most important goal-criterion for an Explanation, although other goal-criteria (i.e., quality-criteria) also can be considered during evaluative Quality Checks, as explained below.

* We can make predictions using deductive "if-then logic" (thus the name Hypothetico-Deductive), but we also can predict by using other kinds of If-Then Logic – as in simulations that may be partially non-deductive, or inductive generalizations.

* This definition of hypothesis is used by Ronald Giere, a prominent philosopher of science;  unfortunately, the term hypothesis is commonly used in many different ways, which can cause confusion for teachers and their students.  Also from Giere (but with major changes) is the dual-parallel structure of my diagrams, including Diagram 3c, with Mental & Physical (left & right) plus Hypothesis & Reality Check (top & bottom).

Quality-Criteria for evaluative Quality Checks:  We want an explanation to be plausible-and-useful.  Usually we care most about plausibility, so during evaluation of a Model-Option we focus on Quality Checks that use Reality Checks` to estimate its empirical Predictive Accuracy, which is the main evaluation-criterion for plausibility.  But for plausibility* and for utility, we also consider other goal-criteria to define quality for a Model (or Theory, Hypothesis, Explanation):

    the empirical Predictive Contrast (between its predictions and those of competitive models) by asking “how much do their predictions differ?” which is useful because often two or more models can make accurate predictions for an experimental system;
    its domain (type & size) of application, internal characteristics (types of entities-and-actions, logical structure, internal consistency,...) and external relationships with other models & theories (logical consistencies, overlap-sharing of domains,...) and with the thought styles of a community;
    its cognitive utility (to stimulate creative-and-critical productive thinking that includes accurate understanding & predicting, creative generating of useful ideas, and more) when you actualize a model by constructing useful representations of it internally (in the mental models you use for thinking) and externally (in words, pictures, equations,... to support the thinking of individuals, and for communication to promote learning-and-thinking in a community), plus its closely related educational utility (when people learn how to use a model in these ways, whether this occurs with or without help from others, in a scientific community, in school systems for any age & level, or elsewhere) and research utility (to stimulate productive experimental & theoretical research);
    its personal utility for helping people achieve personal and professional goals (by getting scientific grants, publications, profits, positions, respect,...).

* Although "the main evaluation-criterion for plausibility" is Predictive Accuracy (tested in Reality Checks), we also can use other criteria.  For example, plausibility will be lowered (below what is warranted by Predictive Accuracy) if a Model:  has undesirable internal characteristics or external relationships because it proposes entities-and-actions that seem implausible, or is not logically consistent with principles from accepted models or theories, or in other ways;  or it has low Predictive Contrast, so you ask “which of the models (making similar Predictions, and thus having similar Predictive Accuracy) seems most plausible?” and maybe you “split the credit” for Predictive Accuracy, thus lowering the plausibility of all these competing models;  or for other reasons.

MORE about these Quality Criteria for Evaluation

The Best Explanation:  We use this complex blend of Goal-Criteria (for defining Quality) to evaluate Explanations (and Explanatory Models, Explanatory Theories, Hypotheses) in multiple Quality Checks so we can estimate a Quality Status for all competitive Explanations.  When we consider everything and ask "Which explanation is best?" a conclusion may be obvious.  But a decision is difficult when different explanations are favored by different criteria;  often the answer is that "it depends," as illustrated by 5 Explanatory Models (from Newton & Einstein) that are compatible (they're “variations on the same basic theme”) and are useful for different situations, for describing-and-explaining the behavior of a bowling ball, tennis ball, moon rocket, cosmic muon, or universe.  These 5 models show why a hypothesis might claim that a model "is approximately correct" (or is correct in some ways but not others), and also "limit the domain of theory-application (re: types of experimental systems) for which this claim is made."

The Logical Foundations of Science:  Although "we want an explanation to be correct, to be true," logically we cannot know (with certainty) that an explanation is correct.  But we can develop a logically justifiable confidence about its plausibility, its estimated probability of being correct.  How?  We first determine the Predictive Accuracy of a System-Model by using Reality Checks in Hypothetico-Deductive Logic.  In a separate step, most scientists then conclude (based on reasonable assumptions about reality)* that if Predictive Accuracy is high, this probably occurs because the System-Model is correct, so they conclude that the System-Model is probably correct, that it's plausible.  But when evaluating a Model (or a Model-Based Explanation) for plausibility-and-utility, scientists also use other goal-criteria.

* This conclusion is accepted by most scientists (for most theories, in most situations) who are critical realists, but is questioned by some scientists (and some scholars who study science, and postmodernists) who propose instrumentalism or relativism.   {the pros & cons of critical realism versus relativism or instrumentalism}

4A — COORDINATE your Process of Design

This is a different kind of mode, because its function is executive control of cognitive processes by making decisions about thinking-and-action in the other 9 modes.

Why?  Decisions are necessary because, at each stage in a process of design, you have many options for Design-Actions.

How?  Part 1, an overview:  To coordinate a process of design with a Coordination Strategy based on Conditional Knowledge, you metacognitively observe your current process, then generate-and-evaluate options for actions so you can make an action-decision by asking “what is the best use of my time right now?” (considering both urgency & importance) and choosing an immediate action that occasionally is a planning of actions, either short-term or longer-term.

Why?  for Performing:  The immediate short-term benefits of effective cognitive-and-metacognitive Coordination Strategies` — which convert individual thinking skills into skills — are using time more effectively and/or designing a better solution.

Why?  for Learning:  The long-term benefits can be improvements in your Metacognitive Awareness, and your Conditional Knowledge of Design Process, and therefore in your Coordination Strategies.   {goals for Performing and/or Learning}

How?  Part 2, with more detail:  In a Coordination Strategy you "coordinate a process of design" by combining Metacognitive Awareness (of “where you are” in your process, and “where you want to go” so you can decide “WHAT you should do, to get there”) with Conditional Knowledge that lets you find a match between a recognized need (it's WHAT you want to do) and a capability (for doing WHAT you want):

    You decide “WHAT needs doing” with metacognition (observing where you are in your current situation, in the now-conditions) plus cognition (comparing where you are with where you want to go, in the goal-situation you defined with Modes 1A-1B).
    You'll know “WHAT you can do” by developing, for each of your skills {or ideas} and for productive combinations of skills {and/or ideas}, a Conditional Knowledge about its functional capabilities (WHAT it lets you do, and thus WHY it's useful) and its conditions-of-application (for WHEN it will be useful).
    When you find a match between your WHAT-need and a WHAT-capability, this indicates a productive design-action that you probably will want to choose and do.

Conditional Knowledge and Transfers of Learning:  If you improve your Conditional Knowledge — which is a way to know “WHAT plus the WHY-and-WHEN Conditions for Application” so you can remember-and-apply what you are learning — you will improve your Transfers of Learning.

How?  Part 3, a different perspective:  A "Strategy to Coordinate a Process of Design is a special type of Strategy for Performing-and-Learning" which is a way to intentionally learn (from the past or for the future) by learning from experience using a process of design with cycles of PLAN-and-MONITOR in which you PLAN (Generate-and-Evaluate so you can Choose) and MONITOR (Use-and-Observe).

Action-Sequences in Design Process

During a process of design, no step-by-step sequence of actions is rigidly followed and uniformly used, so we answer "no" when asking "is there a method?"   But we should say "no... and yes" because some combinations of actions do occur, in sequences ranging from short-term to long-term.

navigation tips:  Below, some links open in the right-side frame, but (so the diagrams will remain visible on the right side) some open in the left frame.  Either way, your browser's Back-Button will bring you back to this section.

Basic Sequences:  Designers often use two functionally integrated sequences that are featured in Diagrams 2e/3b and 4a/4b`:   Moving downward on the left side of Diagram 2e or 3b, individual thinking-actions can be combined into useful sequences when you design-and-do a Mental Experiment to make Predictions that are compared with your Goals-for-Quality in a Prediction-Based Quality Check.  And on the right side you can design-and-do a Physical Experiment to make Observations that are compared with Goals in an Observation-Based Quality Check.

Diagrams 4a & 4b show some of the branching-options for these two sequences:

    After you Design an Experiment you can decide to do a Mental Experiment, or a Physical Experiment, or (eventually) both. 
    Then the results of an experiments are used for Evaluation:  On the left side, your Predictions can be used in a Prediction-Based Quality Check, or a Prediction-and-Observation Reality Check, or both.  Similarly, your Observations can be used for an Observation-Based Quality Check and/or a Mental-and-Physical Reality Check.
    These three actions – when you Design, Do, and Use Experiments (often in sequence) – are why "experiments are a focus of action" in Design Process.

Cyclic Sequences:  These basic sequences, which are used to Evaluate, become part of a Design Cycle or Science Cycle when critical Evaluation is used creatively to Generate Solution-Options (guided by Quality Checks) in General Design, or to Generate Model-Options (guided mainly by Reality Checks) in Science-Design, with creative-and-critical Guided Generation.    { For some educational purposes, isolation diagrams can be useful. }

Sequences of Cycles:  In the wider context of a Design Project, the top line of Diagram 4a – "Make Decisions about Design-Actions and Design Project" – is a reminder that the purpose of our Generating-and-Evaluating actions is to achieve an Objective, to "make it better" by solving a Problem.  During an overall process of design, in an entire Design Project from beginning to end, we have iteration-options for using cycles of Generate-and-Evaluate that operate within broader cycles of Plan-and-Monitor in which we use Observations to learn from experience.

Structured Improvisation:  An effective coordination of design-actions "is analogous to an expert hockey player's process of goal-directed structured improvisation, guided by a strategic action-coordinating plan that is intentionally flexible, open to real-time adjustments in response to an awareness of what is happening."  An expert process of design is flexible, not like "the rigid choreography of a figure skater."  During design, flexibility is possible — despite the common use of sequences in which thinking-actions are combined in functionally useful ways — due to branching-options (that allow choices) and iteration-options (when actions & sequences are re-used).

Phases of Design:  Other models of design describe long-term phases of design that occur due to tendencies of timing, with some actions usually tending to happen early in a process of design, and others later.  For example, quick-and-cheap Mental Ideation using Mental Experiments (to generate ideas for options) tends to occur early, followed by Testing with Physical Experiments (to evaluate options) that is more costly in time and money.  This is long-term sequencing, if sequencing is defined broadly, and these phases of design are examined in Other Models for Design.

MORE – Diagrams 4a/4b are examined more thoroughly in Stage 4.

APPENDIX

Ideas-and-Skills Knowledge - Complexities & Interactions

This section supplements the page-summary for An Ideas-and-Skills Curriculum.

Sometimes it's difficult to distinguish between knowledge that is conceptual and procedural, because of dual characteristics.  For example, Conditional Knowledge is Conceptual Knowledge (about conditions & capabilities) that functions as Procedural Knowledge (to coordinate a process of design).  So is it Conceptual, or Procedural, or both?

Similarly, a Learning Strategy is based on Conceptual Knowlege about Metacognition that can increase when a Learning Strategy is developed-and-used, even though the main objective is improved skill (Procedural Knowledge) in learning, thinking, and performing.  The increase of Conceptual Knowledge is a byproduct (that accompanies the main objective) but it does occur.

Due to these limitations of a two-term system, psychologists & educators have constructed ways to describe knowledge in more detail.

Two related kinds of design-based metacognitive strategies — Coordination Strategies (used in #4 for a Design Project, to improve the results of a problem-solving process) and Learning Strategies (used in #1 for a project of Personal Education, to improve the quality of a person's ideas-and-skills) are similar uses of cognition/metacognition for analogous purposes.

Both strategies can be used to improve Conceptual Knowledge and/or Procedural Knowledge, and to improve performing now and/or (by learning) performing later.  But usually the main objective for a Coordination Strategy is improved problem-solving results in the current project, and for a Learning Strategy it's improved results in future projects.  The emphasis placed on Learning and/or Performing and/or Enjoying varies, depending on the person and the situation.

Conceptual Knowledge is learned for its own sake in #2a/2b, and in #3 it's learned (with motivation) so it can be used. 

The full page for Ideas-and-Skills Curriculum ends with an appendix about Mutual Interactions between components (Motivation, ... Communication) of the model being used throughout the page.

IGNORE THIS AREA, which has "notes to myself" about ideas I might use:

These variations can be understood and taught by using a strategy of Framework + Supplementations.

Diagram 4b shows actions in their typical order, which is useful for developing Coordination Strategies based on Conditional Knowledge of Sequences.

terms – In this website, design can mean both of these or only General Design, depending on context.)