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Terms in NGSS – a Glossary with Clarifications

This page, written in February 2013, is an “open letter” for the developers of NGSS`.

At that time, NGSS wanted people to provide detailed written feedback, submitted using a form on their website.  My feedback to them emphasized these ideas, with a link to this page, and my email address.  But nobody from NGSS ever responded in any way.

To give you a quick overview of ideas that are examined more thoroughly later, it begins with an Executive Summary:

Introduction - Writing a glossary will help to avoid problems that could occur if the new science standards are interpreted and used in ways that are "too loose or too rigid."


Part 1 describes the current absence of a glossary, and why writing one (to clarify meanings-of-terms and intentions-for-terms) would be relatively easy, and would help curriculum developers, teachers, and students.


I think Part 1 would be useful for everyone, and a glossary should be written.  Part 2 is more personal, explaining ideas that I'm hoping (but not really expecting) will be considered for the final version of NGSS and (more likely) when writing a glossary.

Part 2A begins with minimally restrictive definitions: "when you predict (in any way) the result is predictions, when you observe (in any way) the result is observations, and an experiment is any situation that lets you make predictions and/or observations."  Then it explains why these broad definitions "can help students improve their skillfully coordinated uses of mental-and-physical experimenting in a process of design, for science and engineering."   Diagrams 3a & 3b shows the logic of “parallel relationships” between mental experiments (to make predictions) and physical experiments (to make observations).   Stages 1-4` of a teaching progression explain, verbally & visually, how the process of thinking used for both science & engineering is (quoting from NGSS) "iterative and systematic" but is not a rigid step-by-step sequence.

Part 2B examines definitions-and-uses of observations and data in NGSS, where these terms are approximate synonyms.  A glossary can describe their major similarities & subtle differences, and one major difference.  In a major benefit, the term observations "is self-explanatory and is thus easy to understand," so it can be used as "a conceptual bridge... to help students understand the meaning of data ... in a deeper, more personal way, to make it a more intuitive part of their thinking."

Part 2C describes the benefits of two ways to define experiments* and shows why both ways are educationally equivalent because both can be starting points that, using similar instruction to help students learn the logical principles of experimental design, will produce the same ending points of knowledge-and-skill.  A glossary for NGSS could explain the benefits (and "functional equivalence for education") of both definitions, and encourage a flexible interpretation of intentions-for-terms.   /  * We can define an experiment broadly and then distinguish between controlled experiments & uncontrolled experiments, or these two categories can be called lab experiments and field observations (or field studies).



When defining standards for education, two dangers are being too loose or too rigid.

One of the many ways to be "too loose" is described in my PhD dissertation about Science Process where a section with strategies for Coping with Confusion in Terminology observed that "there is no consistent terminology; instead there are important terms... with many conflicting meanings, and meanings known by many names."   { My comments about confusion include a plea for Precise Definitions and a strategy of Gracious Interpretation. }

But being "too rigid" would stifle creativity if any perceived deviation from the standards, in any way, is viewed as unacceptable, as an excuse to immediately reject any slightly different approach before careful study, without seriously considering logical reasons to reject or adopt.


During the process of developing standards, I'm sure that avoiding both ways of “falling off the tightrope” has been a high priority, and I think you have succeeded in achieving a good balance.  But regarding the terms in NGSS, how accurately will your meanings-of-terms be understood by users? and how will your intentions-for-terms be interpreted?  These questions have motivated me to write this page.


Part 1 is brief and simple.  It describes why a glossary would help decrease confusion by encouraging a consistent use of terms in science education.  Writing a glossary would be very helpful (for curriculum developers, teachers, and students) and therefore greatly appreciated.

Part 2 is longer and more complex.  It's a request to think about two terms, and to supplement basic glossary-definitions with guidelines for interpretation, to clearly explain what is and isn't intended by your defining of terms, to encourage a reasonable reading of the standards with interpretations that are not too loosey goosey, or too blindly rigid.  It begins (in Part 2A) by describing an educational benefit, and ends (in 2B and 2C) with some ideas to think about and options to consider, regarding two terms in NGSS.


Timing:  Of course, writing this three years ago would have been better.  But... until recently I didn't study the terms carefully (this isn't easy, as described below) and I don't have a time machine, so... in late February 2013, writing this letter-page seems to be the best I can do.



1. Writing a Glossary for Important Terms

During a recent revision of this website to make my terms more consistent with NGSS, I asked “how are terms defined in NGSS?”  Discovering this wasn't easy, and the searching took a long time (what I found is summarized in an appendix) because there is no single place in NGSS or its Framework-book where all major terms are clearly defined, and some are never defined.   That is why I think a glossary would be useful, to define the most important terms clearly and briefly;  these clarifications for meanings-of-terms would be a glossary's main function, but you also could clarify intentions-for-terms.

A glossary could be included in the final version (early-April 2013) or shortly afterward in a supplement, in Appendix F for Science and Engineering Practices or elsewhere.  Writing it would be relatively easy because it would not require any changes in the Standards, unless (while thinking about terms before the final NGSS is released) you decide that some changes would be beneficial.


Four related terms (explanation, theory, model, hypothesis) are defined well in NGSS, in logical ways that are consistent with their common uses — by scientists and by those who study science (from the perspectives of history, philosophy, education,...) and teach it — and with my definitions of these terms` for use with my model(s) of Design Process.  Four other terms (predictions, observations & data, experiments) are examined below.



2. Clarifications for Two Terms

Part 2A explains how using broad definitions for three terms can help students improve their thinking skills by productively coordinating their mental & physical experimenting.

Then 2B & 2C examine the uses of two terms in NGSS, observations & experiments, to ask if clarifications (for meanings and intentions) might be educationally useful.


2A. Educational Benefits from Broad Definitions

We can define three major terms – predictions & observations, experiment – in ways that are simple and logical, broad and minimally restrictive:

    when you predict (in any way) the result is predictions,
    when you observe (in any way) the result is observations,
    and an experiment is any situation that lets you make predictions and/or make observations.

With more detail, "an Experiment` is any situation that provides an opportunity to get experimental 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."  In similarly non-restrictive definitions, we can make Predictions by "using if-then logic ... with model-based deduction... or model-based simulation... or experience-based induction... or in other ways," and "an Observation is the result of observing in any way, by using human senses or technological measuring-instruments, to get information that is qualitative or quantitative."


What are the educational benefits?  Broad definitions of these 3 terms can help students improve their skillfully coordinated uses of mental-and-physical experimenting in a process of design that is used for science and/or engineering.

Mental and Physical Experiments:  These definitions emphasize the importance of two ways to investigate, mentally and physically, that during an effective process of design (for science or engineering) are deeply related, mutually supportive, and creatively interwoven.  An experiment is any "opportunity to get information" mentally and/or physically;  the analogous “parallel definitions” for predictions and observations will help students understand the “parallel relationships” between Mental Experiments (to make Predictions) and Physical Experiments (to make Observations) that you see on the left and right sides of Diagrams 2e and 3 which show three kinds of comparisons (for Quality Checks & Reality Checks) used in design-thinking for engineering & science, respectively.

Mental and Physical Experiments in a Context of Design:  Seeing these parallel relationships in a logically organized visual structure will help students understand how the practices of engineering & science are iterative and systematic, as described by NGSS: "the process of developing a design is iterative and systematic, as is the process of developing an explanation or a theory in science. (p 11 of Appendix F, quoting p 68 of the Framework)"  My model of Design Process shows how a process of design (for engineering or science) uses iterative cycles of Generation-and-Evaluation, as explained verbally & visually (in the left & right frames) in Stages 1 and 2a` of a five-stage progression for learning.  A central principle of systematic design-thinking* is the creative coordination of mental and physical experimenting;  Stages 2a and 3` explain how we evaluate using Quality Checks for Solution-Options (in Stage 2a) and Reality Checks for Explanation-Options (in Stage 3) plus a Guided Generation of Options when creative Generation is stimulated-and-guided by critical Evaluation.  It's easier for students to see & understand this coordination of mental-and-physical when we use analogous definitions (with verb-actions producing noun-results in each) for predictions and observations.

* In the practices of science & engineering, a "systematic" process is not a rigid step-by-step sequence, as emphasized while explaining what Design Process is and is not.


Terms in NGSS - Definitions and Uses:  Some terms are not explicitly defined in NGSS or the Framework.  Therefore, below — in Parts 2B & 2C, which describe the terms (often illustrated using NGSS-quotations that may include italicizing added by me) and some options to consider — I've tried to determine the intended meaning of all terms by using definitions (when available) and by observing how each term is used in NGSS and also (because uses in NGSS seem to be consistent with common uses by scientists and by those who study & teach science) what I know about terms from other sources.  If any of my inferences about meanings are incorrect, please tell me so I can use your feedback to revise this page.


2B. Observations & Data — Clarifications of Meaning

In NGSS, Appendix F implicitly defines observations broadly, with the same basic meaning as data.  For example, students "record observations (p 13)" and (p 5) "ask questions based on observations of the natural and/or designed world" and (p 8) "use observations to describe patterns... in the natural and designed worlds," which is the same way they "collect data to test ideas about phenomena in natural or designed systems (p 6)" so they can "[use] data to support explanations or design solutions. (p 7)"

These two terms, observations & data, are defined-and-used by NGSS (and by others, including me) in ways that are very similar.  Basically, both are information that is the result of observing-and-recording, so we can call this information observations and also data, treating these terms as synonyms.

Clarifications in a Glossary:  In NGSS, data is a key term, used frequently;  although observations is used far less often, its meaning is very similar to data in most ways, in most contexts.  But these terms are not identical, so they are approximately synonymous, not exactly.  Later you'll see one major difference.  And there can be different connotations due to the ways each term (especially data) is used in education, science & engineering & other fields and in everyday language.  NGSS can use a glossary to describe relationships (major similarities & minor differences,...) between observations and data. 


When these terms are treated as approximate synonyms, with observations ≈ data, one educational benefit is using observations as a linguistic-and-conceptual bridge to help students intuitively understand the meaning of data.  This is possible because...

observation is one of three important terms that are self-explanatory, and are thus easy to understand, due to a cause-efffect relationship between a verb and noun that use the same root word.  In each of the three pairs, a verb-action causes a noun-result (i.e. verb-action → noun-result), for explaining → explanation, and predicting → predictions, and observing → observations.

The logical simplicity of these verb-and-noun pairs will promote clear communication and easier learning.  The first two results of verb-action, explanations & predictions, are key terms in NGSS;  the third result (physical observations, which are analogous to mental predictions)* also could be a key term – at least in the glossary, even if no changes are made in the standards – and this would help students understand the meaning of data.  How?  When these terms are viewed as synonyms and are used interchangeably (in most contexts), we can form a conceptual bridge — first we describe the linguistic self-explanation of observing → observations, and then define observations ≈ data — to help students understand the meaning of data (how do we get it? what is it?) in a deeper, more personal way, to make it a more intuitive part of their thinking.

* This analogy between mental predictions and physical observations is an important part of the "parallel relationships" that can help students understand-and-improve the fluency & coordination of their mental and physical experimenting.


Qualitative and Quantitative:  NGSS calls attention to an important distinction by asking students (on p 10) to decide "if qualitative or quantitative data is best" in a particular situation, and "when to use qualitative vs. quantitative data."  If data and observations are treated as synonyms, asking “which is best?” can be stated as quantitative data vs qualitative data, or as quantitative observations vs qualitative observations.

When students are asked to "make... observations and/or measurements to collect data (p 7)," I think this means that they "make... observations [i.e. qualitative observations, which are qualitative data] and/or measurements [i.e. quantitative observations, which are quantitative data] to collect data."  But it would be useful to clarify this meaning in a glossary, in case anyone might mistakenly interpret this to mean that observations cannot be quantitative.


In NGSS,* data comes from two sources, from observations made in physical experiments {"make... observations and/or measurements to collect data,” p 7} and also predictions made in mental experiments {"use models (including mathematical and computational) to generate data,” p 6}.  This major difference between data (= observations + predictions) and observations can be included in a glossary's description of "relationships... between observations and data."  Of course, students should understand the very important difference between mentally produced prediction-data and physically produced observation-data, so this difference should be clearly explained (and emphasized) in the glossary.

* This broad definition will let students use data from a wider variety of sources for NGSS-inspired instruction activities to improve their skills with Practices 4 (Analyzing and Interpreting Data) and 5 (Using Mathematics and Computational Thinking).   /   Although the traditional source of data is observations, getting data from simulation-predictions is becoming more common.  For example, Wikipedia's page on Computer Simulations describes "input sources... [for the] external data requirements of simulations" and also "the output data from a computer simulation" that uses observations (from other sources) as input data, and makes predictions in its output data.

How should we distinguish between data from these two sources, from observations and predictions?  Of course, we will remind students to “always consider the context.”  And we can use adjective prefixes, like observation-data and prediction-data.  Or a teacher can use data to mean observation-data (since this is the most common meaning of data, and its most common source) and use a prefix (at least once during an activity, as a reminder that they are using prediction-data) when data has been produced by predictions.  And other adjectives are possible;  for example, students are analyzing simulation-data (sim-data?) when they are analyzing predictions made with model-based computer simulations.


also:  To avoid confusion, maybe we should use the term "observations" only for the result of observing.  NGSS uses "field observations" (on p 7) for physical experiments done “in the field” instead of in a lab, so maybe these should be called field experiments, or field studies, or observational field studies, observational studies, or (as in the Framework, p 60) observational inquiries. 



2C. Experiments — Clarifying Intentions

Part 2C is different.  My requests in Parts 1 (to write a glossary) and 2B (to define observations and data as approximate synonyms) are to clarify the meanings-of-terms, and doing this offers obvious benefits that can be achieved in simple ways.  Here in 2C my requests are to clarify your intentions-for-terms, and for this the benefits and ways are not as easy to describe.  But I think Part 2C is very important, and you'll understand why after you read it.


Experiments in NGSS

• In my broad definition an experiment is "any situation that lets you make predictions and/or observations," but NGSS seems to split these situations into two categories:  in a laboratory experiment, students "decide which variables are to be... intentionally varied from trial to trial, and which are to be controlled," while in a field observation "not all conditions are under the direct control of the investigator. (p 7)"   I say "seems to" because although there is no explicit definition (in NGSS or the Framework), there seems to be an implication that an experiment requires active control of an observation-situation, or it will be just a passive observation (with this term used in a way I think we should not use) rather than an active experiment.   Instead, there are reasons (logical & educational) to define experiment broadly, and then split this broad category into uncontrolled experiments and controlled experiments, and help students understand how they can use logic to guide their creative Designing of Experiments.

• Broad definitions for three terms (predictions & observations, experiment) are useful for describing mental-and-physical experimenting.  But in NGSS this is impossible because no term corresponds to all situations in which we can "make predictions and/or observations."  Instead, an experiment seems to be only one kind of situation, so it's too narrow.  And although an investigation covers all situations, it also includes much more, so it's too broad.  (NGSS defines an investigation very broadly, as a cluster of related activities in which we design & do & use Experiments so "planning and carrying out investigations may include elements of all of the other practices (p 7)," which might encompass an entire design project.)   Thus, in NGSS there is no term to describe all situations that allow prediction/observation, no more and no less;  their definition for investigations is too wide (these are all situations and more), while for experiments it's too narrow (these are some situations but not others).


Two Ways to Categorize Experiments

Each definition of experiment, broad (by me) and narrow (by NGSS), cannot describe all observation-situations completely and accurately.  Why?  The rich variety of situations that let us observe, and predict, cannot fit into one category (as in my definition) or two categories (as in NGSS).  But some important educational benefits occur when we first define experiment broadly, and then explain how an experiment can be designed with “controls” and “choices” that make the experiment more useful.

In this diagram, experiments — broadly defined as any situation that lets us mentally Predict or physically Observe — are split into uncontrolled experiments (with control = 0) and controlled experiments (with control > 0):

splitting experiments into uncontrolled vs controlled, or field studies vs lab experiments

Here are some comments...

for the line in the middle:  DEGREE OF CONTROL is a simplified label for a complex combination of control-characteristics, re: types & amounts of control.

for the area above this line:  A totally-controlled experiment is impossible;  e.g., concentrations of chemicals in two containers cannot be exactly equal;  and even if two experiments were identical in other ways, they could not be done at the same time and same location, so time & location would be variables preventing them from being identical.  Therefore the control cannot be 100%, it must be less than 100%, symbolized < 100%.  But often we can get close to 100%, if we control all of the variables that we think are relevant, that are important factors in affecting the observed results.  Thus, a controlled experiment has control > 0 (by definition) but its control must be < 100%, and the ends of this range — {0 < control < 100} — are arbitrarily labeled .01 (slightly > 0) and 99.99 (slightly < 100%) on my diagram, to make the range more “concrete” and easier to intuitively understand.

for the area below this line:  field studies and lab experiments differ in two ways, in location (in "field" or "lab", however these are defined) and in Degree of Control, which typically is higher for a lab experiment so its line is further toward 100%.  But experiments in each location have a vaguely defined range (indicated by the dotted arrow-lines and absence of ending-arrows, showing that I make no claims about where each range ends), and their ranges overlap because some studies in the field have more control than some experiments in the lab;  NGSS says that in field studies* "not all conditions" (instead of “no conditions”) can be directly controlled, because a field study can be uncontrolled or semi-controlled, so field study ≠ uncontrolled study.   /   * Instead of the term field observations used by NGSS, in Design Process I'm using field studies because this avoids the confusion of having two major meanings for one word, observations.  It also avoids an implication, which might occur when contrasting field observations with lab experiments in a binary categorizing, that a field observation (= field study) cannot be an experiment with some control, or with some choices.

controls plus choices:  When they are doing Experimental Design, it's important for students to understand that even in a totally-uncontrolled field study, creative “choices” are available for what-and-how to observe during the experiment, and then how to process the observations-data.  These choices are illustrated by astronomers studying light coming from faraway stars when scientists cannot control the situation, but they can choose what-and-how to observe.  In fact, choices are useful (and necessary) for all experiments, throughout the range-of-control from 0% to 99.9%.


Using logical principles to control variables is called experimental design in NGSS (and by me & others) because the usual goal of scientists is a controlling of variables (and of what/how to observe) that helps us understand “what is happening, how, and why.”  Therefore, we try to design uncontrolled or semi-controlled field studies so they are more like logically controlled lab experiments.


Educational Equivalence

Benefits and Flexibility:  Each set of definitions — broad and narrow, as recommended by me (experiments, controlled [in various ways] & uncontrolled, choices) and NGSS (field studies, lab experiments) — offers distinctive benefits, and I'm hoping that both sets of benefits will be described in a terms-glossary along with a clarification-of-intentions.  A flexible statement of intentions could help minimize potential problems of rigidity (when the NGSS intentions are interpreted by those reading the standards) that "would stifle creativity if any perceived deviation from the standards, in any way, is viewed as unacceptable, as an excuse to immediately reject any slightly different approach before careful study."  This flexibility seems justifiable because, after beginning with either set of definitions, we can use similar instruction to help students learn principles for controlling variables, so there is a...


Functional Equivalence for Education

Both definitions, broad and narrow, can be educationally equivalent because both can be starting points in a process-of-teaching/learning that will produce very similar ending points of knowledge-and-skill in the logical process of creatively Designing Experiments.  Here is an overview of educational journeys from starting points to ending points:

  But both can be a starting point for effectively helping students learn logical principles for designing experiments, so both definitions can be educationally equivalent.

From any starting point, we try to show students the kinds of experiments that will help them reach logical conclusions when they have significant control over an observation-situation, and also when they have less control so they must use other kinds of adjustments by choosing what to observe, with creative analysis of data, and in other ways.   For example, our teaching strategies to promote Creative-and-Critical Thinking in Experimental Design will be very similar, whether the starting point is my broad definition of experiment or the narrower definition of NGSS.  After my introduction of NGSS terms in the second paragraph of Controlling Variables in an Experimental System, I describe the meanings that seem to be implied in the NGSS terms:

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 [using terms in NGSS] 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).
From this point onward all teaching strategies could be almost identical, independent of their starting-point definitions, with a "Functional Equivalence for Education."

[update: This paragraph was written in 2013 as if it would be read by anyone in NGSS, although it probably wasn't.]   Part 2C began with an explanation of why it's different because in Parts 1 and 2B the focus is meanings-of-terms, but in 2C it's intentions-for-terms.  You, in NGSS, can decide whether to describe the benefits of different sets of terms for observation-situations (in 2B), and how to clarify your intentions-for-terms for all terms, especially in 2C for experiments.  It will be my responsibility to respond by conforming (as much as possible) to NGSS terms, and then (if it's necessary, if there are any unavoidable differences) persuading others that my use of terms allows an "educational equivalence" for teaching principles of Experimental Design.


I've searched for a simple-and-logical term to replace experiment in Design Process, but haven't yet found it.  I'm hoping that this use of experiment won't be a personal difficulty for me.  Unfortunately, this could occur if the intentions of NGSS are interpreted too rigidly, so my use of experiment in Design Process becomes a "perceived deviation from the standards" that is "viewed as unacceptable" by other educators.  Maybe some negative results of rigidity, for me and others, can be avoided by writing a glossary with a flexible definition of experiment, and with a clarification of principles regarding your intentions-for-terms.

A general difficulty is that neither definition of experiment (broad or narrow) is perfect, as explained above.





Definitions from NGSS

Below are definitions-and-uses of terms, quoted mainly from NGSS - Appendix F (pages 1-23) but also from the Framework - Chapter 3 (pages 41-82) with added emphasis by me — bold is for terms used in NGSS and Design Process, bold purple is for terms used only in Design Process, and italic shows functions or characteristics of terms) — plus my comments, either [in brackets within quotations] or outside quotation marks.  Here are definitions of terms in NGSS, and/or the way terms are used.



There is no formal definition in NGSS because this isn't necessary, because the way explanation is used in NGSS matches its common uses in everyday life (and in Design Process), with a self-explanatory verb-and-noun pair in which the result of explaining is an explanation.



This also is fairly simple.  “The goal of science is to explain phenomena. ..... The goal of science is the construction of theories that provide explanatory accounts of the world.” (pp 11, 20)    The second sentence describes how theories can be used "to explain" with explanatory theories in theory-based explanations.   /   note: The final two terms are purple because, although both are in the Framework (along with explanatory models & model-based explanations), they are not in Appendix F of NGSS.


Theories and Models

This is more complicated.  We’ll begin with comparisons.

Similarities:  Theories and Models are similar in their characteristics (what they are) and functions (how we use them).  Therefore, NGSS has "model or theory" statements, with scientific questions being "inspired by the predictions of a model or theory" (p 5);  "scientific investigations may... test a theory or model for how the world works" (p 7);  scientists & students can "frame a hypothesis (a possible explanation...) based on a model or theory" (p 5, 60).

Differences:  My description of theories & models explains that a theory usually has "wide scope... and strong support," while models can have a range of scopes (narrow to wide) and support (weak to strong), which agrees with statements in the Framework.  But I also describe an everyday meaning in which "the scope of a theory can vary from narrow to wide, and its support from low to high."  I encourage teachers to use both definitions, "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."  An appendix describes the benefits of using this direct strategy, instead of an indirect strategy that simply ignores everyday definitions.

Relationships:  the Framework says, "scientific explanations are explicit applications of theory to a specific situation or phenomenon, perhaps with the intermediary of a theory-based model for the system under study" (p 52);  "very often the theory is first represented by a specific model for the situation in question, and then a model-based explanation* [using an explanatory model*] is developed" (p 67);  students "can develop theory-based models" (p 48);  "scientists achieve their own understanding by building theories and theory-based explanations* [using explanatory theories*] with the aid of models and representations and by drawing on data and evidence" (p 68).   /   These four terms are purple because I use them but they're not in Appendix F of NGSS, although they are in the Framework, on pages 67, 59, 68, and 67 & 78, respectively.



NGSS describes a wide variety of models, and ways to use them:

"A practice of both science and engineering is to use and construct models as helpful tools for representing ideas and explanations.  These tools include diagrams, drawings, physical replicas, mathematical representations, analogies, and computer simulations.  Modeling tools are used to develop questions, predictions and explanations; analyze and identify flaws in systems; and communicate ideas.  Models are used to build and revise scientific explanations and proposed engineered systems." (p 16)

Models are important in science & engineering, and thus in schools where "modeling can begin in the earliest grades, with students' models progressing from concrete 'pictures' and/or physical scale models (e.g., a toy car) to more abstract representations of relevant relationships in later grades, such as a diagram representing forces on a particular object in a system. (p 6)"

Students can make predictions — when "models are used to represent a system (or parts of a system) under study (p 6)" — that can be used to "test mathematical expressions, computer programs, algorithms, or simulations of a process or system to see if a model 'makes sense' by comparing the outcomes with what is known about the real world. (p 10)"   One way to make predictions is when "simple computational simulations are created and used based on mathematical models of basic assumptions. (p 10)"

For a more complete view of models in NGSS, read pages 6 & 16 for Practice 2.

My definitions-and-uses of models are very similar to NGSS, although I use sub-terms for different types of models.  When a model is "used to represent a system... under study" it's a system-model (constructed for one experimental system as described above and in Relationships) that is used with if-then logic to make predictions.  A domain-model is similar, but is assumed to be valid for a wider domain of related experimental systems.  As in NGSS, model-representations take a wide variety of forms in external conceptual models (a term I borrowed from the Framework, p 56, although it's not used in NGSS) that can include physical prototype models, plus internal mental models (also used in the Framework but not NGSS).  These models can be used in mental experiments (to make predictions) or physical experiments (to make observations).   /   In a similar way, Ronald Giere categorizes a wide variety of models into theoretical models (as in my system-models & domain-models), analog models, and scale models.



A definition in NGSS (p 5) and its Framework (p 60) states that students should be able to "frame a hypothesis (a possible explanation [i.e., an explanation-option] that predicts a particular and stable outcome) based on a model or theory."  This definition of hypothesis, and all of its uses in NGSS, are also the definition & uses in Design Process.

And with more detail on p 67:  "A scientific hypothesis is neither a scientific theory nor a guess;  it is a plausible explanation for an observed phenomenon that can predict what will happen in a given situation [i.e., in an experimental system].  A hypothesis is made based on existing theoretical understanding relevant to the situation and often also on a specific model for the system [i.e. a system-model] in question."   And (p 50), "models enable predictions of the form ‘if... then... therefore’ to be made in order to test hypothetical explanations" because we can use a system-model plus if-then logic to make predictions that can be tested with Hypothetico-Deductive Reasoning.

The definition in NGSS is clear, but is incomplete because it doesn't explain the difference between a Model and a Hypothesis which "claims that a System-Model is similar to an Experimental System" in some ways, and "we can form different Hypotheses using the same System-Model, 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."    { My description is based on a definition from Ronald Giere who defines a hypothesis as "a statement (claim, assertion, conjecture)... that the model is indeed similar to [some aspect of] the world in indicated respects and to an implied degree of accuracy."  This seems consistent with hypothesis in NGSS, you can clarify your more-complete meaning in a glossary. }



Quantitative Observations

Although NGSS seems to define observations as being either qualitative or quantitative, phrases like "observations and measurements" leave room for interpretations, and a glossary could clarify.

And we should not imply that measurements (= quantitative observations or quantitative data) can be made only with instruments.  Here are some examples of measurements made by humans:  we can use our senses to estimate sizes (length, weight, volume,...)* as in "hefting" an object to estimate its weight, or "walking off" the length of a building;  estimating length to the nearest .1 mm, using a ruler with mm-markings;  deciding what weight (or mass) to record when observing a scale with non-digital indicators, or whose digital read-out is fluctuating, or seems to be drifting in one direction; 

* In one activity, students estimate quantitative properties (length, weight, volume) for various objects, in American units and metric units, using their senses instead of instruments.  (density also can be estimated, as in asking "will it float?" in fresh water or salt water)

Also, during sports activities we continually make estimates of distance (and other properties) when shooting a basket, catching a fly ball or grounder, throwing or catching a pass, and so on.


Observations that are qualitative or quantitative can be made with human senses or by instruments:

  with senses  
  by instruments  
QUALItative Observations?
QUANTItative Observations?   



Changing Terms in Design Process:  To make this website more consistent with NGSS, recently I decided to change theory (my original term for the main long-term objective of science) to explanation (a term used by NGSS for the basic objective of science) or model (similar to theory), or (with no change) theory.  So far, these term-revisions have been changed in the most important page (an Executive Summary of the Website and a Longer Summary) and in parts of a few other pages.  Later, terms throughout the website will be changed, one at a time, making decisions whether each "theory" should remain as-is (theory) be changed to explanation or model.  But these modifications of terms are minor because my definitions (based on those of Ronald Giere, below) already were consistent with NGSS, so only a “change of emphasis” was needed. 

Why can the terms of Design Process be changed?  The terms used in this website are part of "my own non-essential elaborations" so these non-essential terms can be changed without changing the essential characteristics of Design Process as a logical framework for describing the process-practices used in science and engineering.  But... [I.O.U. - this will continue by explaining why, for practical reasons of style and clarity and conformity with common uses of terms, it would be extremely difficult or unwise to change some terms.]


The Many Meanings of "Theory" - Strategies for Coping with Confusion

For education to "help students understand the wide range of ways that theory is used, inside and outside science" we can use an indirect strategy (by just ignoring everyday definitions of theory) or a direct strategy (by explaining & comparing definitions often used inside & outside science), as described above and in Defining "theory" for Education.

Originally I thought this appendix would examine the pros & cons of teaching strategies that use both definitions, or don't.  But I cannot think of any good reason to "just ignore everyday definitions," because confronting the linguistic misconceptions of students (if they think everyday meanings & scientific meanings are the same) should improve their understanding, for reasons that are similar to strategies for reducing their scientific misconceptions.  But a teacher might want to eventually use only the typical science-meaning in a classroom, after explaining & comparing the different definitions.

What is the Main Objective of Science? - Theory vs Explanation

Initially, I defined the possible objectives of design as a product, strategy, activity, or (in science) a theory.  But NGSS defines the objective of science as explanation or an explanation.


Ideas from Ronald Giere

While I was developing my model for Science Process two important ideas came from Ronald Giere.

Hypothesis:  "A theoretical hypothesis is a statement (claim, assertion, or conjecture) about a relationship between a theoretical model and some aspect of the world.  It asserts that the model is, indeed, similar to the world in indicated respects and to an implied degree of accuracy." (Understanding Scientific Reasoning, p 27 – 3rd Edn, 1991)   { Giere's main concepts are in my definition of hypothesis. }

The Giere-Box:  The basic "box shape" in my visual representations of mental-and-physical parallels are borrowed from Giere's diagram in Understanding Scientific Reasoning.   {note: I first saw this in the 1991 edition;  it's also in 1997 & 2005, although "with Experimental Setup" was not added until this diagram from 2005.}

Hypothetico-Deductive "parallel box" by Ronald Giere
You can see the many ways I changed this "basic box" in a comparison-page showing two diagrams (his, mine) for Hypothetico-Deductive Reasoning in a Reality Check, plus one of my diagrams that includes this reasoning (and more) in a more generalized Design Process.