ArticlePDF Available

Abstract and Figures

The word modeling is becoming more and more common in physics, chemistry, and general science instruction. In physics, students learn models of the solar system, light, and atom. In biology courses they encounter models of joints, the circulatory system, and metabolic processes. The benefits of engaging students in model building are described in the literature.1-5 ``Modeling instruction'' is an example of a whole curriculum based on the idea of modeling.6 However, in a traditional physics class students do not have a clear understanding of what the word model means, and thus do not appreciate the role of this notion in physics.7-9 Physics teachers also have difficulties defining this word.10,11 The purposes of this paper are (a) to reexamine the word model as it is used in science, and (b) to suggest several types of tasks that engage students in the construction of models in a regular-format introductory physics course.
Content may be subject to copyright.
The Role of Models in
Physics Instruction
Eugenia Etkina, Aaron Warren, and Michael Gentile, Rutgers University, New Brunswick, NJ
he word modeling is becoming more and more
common in physics, chemistry, and general
science instruction. In physics, students learn
models of the solar system, light, and atom. In biology
courses they encounter models of joints, the circula-
tory system, and metabolic processes. The benefits of
engaging students in model building are described in
the literature.
“Modeling instruction” is an example
of a whole curriculum based on the idea of model-
However, in a traditional physics class students
do not have a clear understanding of what the word
model means, and thus do not appreciate the role of
this notion in physics.
Physics teachers also have
difficulties defining this word.
The purposes of
this paper are (a) to reexamine the word model as it is
used in science, and (b) to suggest several types of tasks
that engage students in the construction of models in a
regular-format introductory physics course.
What Is a Model?
The modeling approach in physics began with Rene
Descartes. He was the first to propose that the mental
constructs of a scientist about the world were not to be
considered “postulates representing his own beliefs but
as useful models from which one could deduce conse-
quences in agreement with observations.
In physics education research the word model is
associated with David Hestenes and his colleagues,
who advocated the use of models in physics instruc-
tion more than 20 years ago. He defined a model in
the following way: “A model is a surrogate object, a
conceptual representation of a real thing. The models
in physics are mathematical models, which is to say
that physical properties are represented by quantitative
variables in the models.
In general, physicists share several common ideas
about models:
a) a model is a simplified version of an object or pro-
cess under study; a scientist creating the model
decides what features to neglect;
b) a model can be descriptive or explanatory; explana-
tory models are based on analogies—relating the
object or process to a more familiar object or pro-
c) a model needs to have predictive power;
d) a model’s predictive power has limitations.
Mastering these ideas is difficult. How does one
know what to neglect while simplifying an object or
a process? Can the same object or process be modeled
differently in different situations? How does one make
a decision whether a particular model is appropriate?
How can one use models to make predictions?
In the following section we explore the meaning of
the word model at a deeper level, which will ultimately
allow us to devise tasks for students that address the
questions raised in the previous paragraph.
Classifying Models
As discussed above, scientists use models or simplifi-
cations to describe and explain observed physical phe-
nomena and to predict the outcomes of new phenom-
ena. We suggest that when simplifying a phenomenon
to make a model, we simplify 1) objects, 2) interac-
tions between objects, 3) systems of objects together
with their interactions, and/or 4) processes (Fig. 1).
This classification gives us four types of models.
1. Models of objects: When we choose to investi-
gate a physical phenomenon, we first identify the
objects involved. We then decide how we will sim-
plify these objects. For example, we can model the
same car as a point particle, or as an extended rigid
body, or as multiple extended rigid bodies.
2. Models of interactions: When there are multiple
objects involved, we need to consider interac-
tions between those objects. We make decisions
to neglect some interactions and take others into
account. We can model interactions quantitatively
in terms of the strength and the direction of a force
or a field, or the magnitude and the sign of a po-
tential energy. When we quantify this picture we
get some mathematical expressions that we call
interaction equations. An example of an interac-
tion equation is Coulombs law.
3. Models of systems: By combining the models of
objects and interactions for a physical system, we
get a model of the system. For example, if we sim-
plify a gas as many point particles that interact with
the walls of their container via elastic collisions, we
have a model of a system known as an ideal gas.
4(a). Models of processes (qualitative): Due to the
interactions between the objects in a system or
with objects outside a system, the system may
change in some manner. We will refer to a model
that describes the changes in a system as a process
model. For example, we can explain qualitatively
a thermodynamic process involving a gas in a con-
tainer with a movable piston using the model of an
ideal gas and considering its interactions with the
4(b). Models of processes (quantitative): When we
quantify our models of systems and processes, we
get mathematical expressions that we call state
equations and causal equations. A state equation
describes how one or more properties of a system
vary in relation to each other, but the cause of the
change is unspecified. A causal equation, however,
describes how the properties of a system are af-
fected by its interactions with the environment.
A state equation is a mathematical expression in
which each quantity corresponds to various properties
of a single system. For instance, x = x
+ v
t is a state
equation, a model of a process involving a point par-
ticle. Another example is the ideal gas law, a model of
a process involving an ideal gas. Each quantity in the
equation corresponds to a property of a gas (which is a
system of point particles). On the other hand, a causal
equation is any mathematical expression that includes
quantities that correspond to physical interactions
between a system and its environment. For example,
in the first law of thermodynamics heating and work
cause changes in the internal energy of a system.
Other examples of causal equations are the impulse-
momentum equation, the work-energy equation, and
Schrödingers equation. The most fundamental causal
equations are based on symmetries.
Each of these models can be represented in many
ways, including words, mathematical functions,
graphs, pictures, and model-specific representations
such as motion diagrams, free-body diagrams, energy
bar charts, ray diagrams, and so forth. Students need
to learn how to use these representations to solve spe-
cific problems. Much has been written about the im-
portance of representations in physics instruction and
Fig. 1. We can model nature by focusing on an object, an
interaction, a system, or a process. Quantifiable models
include mathematical expressions such as interaction equa-
tions, state equations, and causal equations.
successful instructional strategies.
Students often engage in modeling during our
classes but are unaware of it. In the section below we
suggest several types of tasks that make this process
explicit and encourage students to consciously engage
in and reflect on modeling.
Engaging Students in Making and
Testing Models
In physics education, modeling of phenomena for
investigations and problem solving has been done
mostly by Hestenes and his colleagues.
1-3, 6
Their ap-
proach assumes consistent use of special vernacular,
representations, and problem-solving strategies during
instruction. We suggest that tasks engaging students
in deliberate modeling of real situations can be used
in any physics course, while students are engaged in
problem solving or laboratory exercises. We describe
examples of activities to help students practice model
construction, evaluation, and revision of models. The
tasks are grouped under three categories: the types of
models identified in the “Classifying models” section
(models of objects, interactions, systems, and pro-
cesses), the purposes of modeling as identified in the
section “What Is a Model” (describing, explaining,
and predicting), and the limitations of the models. The
wording of the tasks follows the recommendations of
Heller and colleagues.
Different Types of Models
1) Choosing a model of an object
(a) A 70-m long train leaves a station accelerating at
2 m/s
. You are at the platform entrance 30 m
from the tracks. To determine if you can catch the
train, would you model the train as a point particle
or as a rigid object with a definite length? Explain.
(b) The same train travels for 10 hours and covers
630 miles. To determine the trains average speed,
would you model the train as a point particle or as
a rigid object with a definite length? Explain
2) Choosing a model of an interaction
You have been hired as a consultant for NASA and
the following task is given to you: You are in charge
of a group whose job is to design a computer pro-
gram that can quickly calculate the energy of an
Earth-rocket system. The rocket will travel from
the Earths surface to an orbit high above the Earth.
You know that the gravitational potential energy of
a system consisting of two objects of masses M and
m (when the smaller object is outside the more mas-
sive object) is U = –GMm/r. To make your computer
program as fast as possible, though, you want to
know when it is OK to treat the gravitational poten-
tial energy of the system as U = mgh.
a) Where do we set U = 0 when we use U = mgh?
b) Using your answer to part (a), show that we can
use U = –GMm/r to derive U = mgh. [Hint: 1/(R +
(1/R) – (h/R
) if h << R].
c) Based on the approximation used in part (b),
when do you think it is reasonable to use U = mgh?
3) Choosing a model of a system
You are an assistant for the physics labs. You just
found a resistor in your desk drawer and are curi-
ous about its resistance. You have a battery, some
connecting wires, an ammeter, and a voltmeter. You
decide to measure the voltage across the resistor and
divide it by the current through the resistor. You
build a circuit as shown below (Fig. 2a) and then
realize that there is another way to do it (Fig. 2b).
What modeling assumptions about objects and
processes in the circuit do you need to make to go
with the first circuit or with the second? What infor-
mation about the elements of the circuit do you need
to have in order to decide which method (a or b) is
applicable to the circuit? (Hint: Remember that both
measuring devices have internal resistance.)
Fig. 2. Using the reading of the ammeter and voltmeter
to calculate the resistance of the resistor leads to differ-
ent results for different circuit arrangements.
4) Choosing a model of a process
State equations
Given a rigid 0.50 m
container with 4.46 mol of
air inside, and an initial temperature of 500 K, you
measure a pressure of 37.06 kPa. You then cool it to
a temperature of 133 K while compressing the gas to
a volume of 0.10 m
. The pressure of the gas is now
measured to be 49.10 kPa. Two possible models of
the gas are the ideal gas model and Van der Waal’s
model. Determine how consistent each of these
models are with the reported measurements (a =
0.1358 J·m
, b = 3.64 x 10
/mol for air).
If one model is more accurate for a certain measure-
ment, propose an explanation for why this is the
Causal equations
You are analyzing a video of a falling beach ball (m =
500g, R = 20 cm) by viewing it frame by frame. You
find that the acceleration of the ball is constant and
equal to 8.8 m/s
. You decide to analyze the situa-
tion by modeling the interactions of the ball with the
Earth and air. What modeling assumptions about the
interactions and processes do you need to make to
explain the acceleration of the ball?
Purposes/Uses of Models
5) Using models to describe phenomena
You bought a motorized toy car for your little sister.
How can you find out which model describes the
motion of the car best: the model of motion with
constant speed, constant acceleration, or changing
6) Using models to explain phenomena
You have a cart on an air track attached by a string
that passes over a pulley down to a hanging object
[see Fig. 3(a)].
You push it abruptly toward the left. The cart
moves to the left, slows down, stops, and starts mov-
ing to the right with increasing speed. The graph for
the cart’s acceleration versus time is shown in Fig 3(b).
When you repeat the same experiment with a cart on
a regular track, the acceleration-versus-time graph
looks different [see Fig. 3(c)].
Identify models of objects, interactions, systems,
and processes that can help you to explain each graph
and discrepancies between them.
7) Using models to predict new phenomena
A helium-filled balloon is attached to a light string
and placed inside a box made of transparent plastic.
The box has wheels on the bottom that allow it to
roll. Explain why the balloon and string are verti-
What models of objects and interactions did
you use? Predict what will happen to the thread
and the balloon if you abruptly push the box to the
left. To make the prediction, explain what models
of objects and interactions you will include in your
system and how you will model any processes that
occur. Then observe the experiment. If your predic-
tion does not match the result, revise your model in
order to get a new prediction that does match the
Model Limitations
8) Limitations of models objects,
interactions and processes
Your friend’s lab group has to figure out the specific
heat of a 0.50-kg rock. They plan to heat the rock by
letting it sit in a 200°C oven for five minutes. Then
they will put the rock in a thermos filled with 200 g
of ice (measured with a dietary scale), close the ther-
mos, and wait another five minutes. After doing all
Fig. 3. (a) A cart on the air track connected by a string
that passes over a pulley to a hanging object was
abruptly pushed in the negative direction and let go.
(b) The acceleration-versus-time graph for the cart.
(c) The acceleration-versus-time graph for the same cart
when the experiment was repeated on a regular track.
this, they open the thermos and find that the ice has
completely melted. They measure the final tempera-
ture of the rock by measuring the final temperature
of the melted ice water, which is 20°C. They calcu-
late the specific heat of the rock as follows:
+ m
ice water
+ m
= 0
= –[(0.2 kg)*(33.5 x 10
J/kg) + (0.2 kg) *
4186 J/(kg*K) * 20ºC)] / [0.50 kg * (–180°C)] =
930 J/(kg K) .
Identify all the modeling assumptions your friend’s
group has made about the objects, interactions, sys-
tems, and processes, and evaluate whether or not each
assumption should be accepted.
Choosing a productive model to describe or explain
a phenomenon under study is a routine part of the
work of scientists but a rare exercise for our students.
Students have difficulties understanding the mean-
ing of the word model and using it to analyze physical
phenomena and solve problems. We hope that by cre-
ating and using tasks similar to the ones shown here
students can become more proficient at modeling. To
help students you can engage them in “meta-model-
ing”—reflection on the purposes and outcomes of the
modeling process. We encourage instructors to de-
velop their own tasks like those shown above, and to
incorporate them into their curricula. Solutions to the
problems presented in the paper and more tasks are
available at
We thank Michael Lawrence and Suzanne Brahmia
for providing suggestions for the tasks, and David
Brookes and Alan Van Heuvelen for helping with
the preparation of the manuscript. We also thank an
anonymous reviewer for helpful comments and sug-
gestions. The project was supported in part by NSF
grant DUE 024178.
1. D. Hestenes, “Toward a modeling theory of physics
instruction,” Am. J. Phys. 55, 440–454 (May 1987).
2. I. Halloun, “Schematic modeling for meaningful un-
derstanding of physics,” J. Res. Sci. Teach. 33, 1019–
1041 (1996).
3. I. Halloun, “Schematic concepts for schematic models
of the real world: The Newtonian concept of force,” Sci.
Educ. 82, 239 (April 1998).
4. I.M. Greca and M.A. Moreira, “Mental, physical, and
mathematical models in the teaching and learning of
physics,” Sci. Educ. 86, 106 (Jan. 2002).
5. A.G. Harrison and D.F. Treagust, “Learning about
atoms, molecules, and chemical bonds: A case study of
multiple-model use in grade 11 chemistry,” Sci. Educ.
84, 352–381 (May 2000).
6. M. Wells, D. Hestenes, and G. Swackhamer,”A model-
ing method for high school physics instruction,” Am. J.
Phys. 63, 606-619 (July 1995).
7. L. Grosslight, C. Unger, and E. Jay, “Understanding
models and their use in science: Conceptions of middle
and high school students and experts,” J. Res. Sci. Teach.
28, 799–822 (1991).
8. D.F. Treagust, G. Chittleborough, and T.L. Mamiala,
“Students’ understanding of the role of scientific mod-
els in learning science,” Int. J. Sci. Educ. 24, 357 (April
9. J. Ryder and J. Leach, “Interpreting experimental data:
The views of upper secondary school and university sci-
ence students,” Int. J. Sci. Educ. 22, 1069 (Oct. 2000).
10. R.S. Justi and J.K. Gilbert, “Modelling, teachers views
on the nature of modelling, and implications for the
education of modellers,” Int. J. Sci. Educ. 24, 369 (April
11. Private communication with Jane Jackson.
12. G. Holton and S.G. Brush, Physics, the Human Adven-
ture: From Copernicus to Einstein and Beyond, 3rd ed.
(Rutgers Univ. Press, New Brunswick, NJ, 2001), p.
13. E. Etkina, T. Matilsky, and M. Lawrence, “What can we
learn from pushing to the edge? Rutgers Astrophysics
Institute motivates talented high school students,” J.
Res. Sci. Teach. 40, 958–985 (2003).
14. C.T. Hill and L.M. Lederman, “Teaching symmetry in
the introductory physics curriculum,” Phys. Teach. 38,
348 (Sept. 2000).
15. A. Van Heuvelen, “Learning to think like a physicist: A
review of research-based instructional strategies,” Am. J.
of Phys. 59, 891–897 (Oct.1991).
16. J.R. Frederiksen, B.Y. White, and J. Gutwill, “Dynamic
mental models in learning science: The importance of
constructing derivational linkages among models,” J.
Res. Sci. Teach. 36, 809–836 (1999).
17. J.L. Lemke, “Teaching all the languages of science:
Words, symbols, images, and actions,” http://www-
18. Students engage in similar activities while solving con-
text-rich problems as in P. Heller, R. Keith, and S. An-
derson, “Teaching problem solving through coopera-
tive grouping Part 1,” Am. J. Phys. 60, 627–636 (July
19. Videos for modeling tasks and other videotaped experi-
ments can be found at The
video of this experiment can be found at http://paer.
PACS codes: 01.40Gb, 01.55
Eugenia Etkina is an associate professor of science
education at Rutgers University. She works with pre- and
in-service physics teachers and with colleagues in physics
who are reforming undergraduate physics courses.
Graduate School of Education, Rutgers University,
10 Seminary Place, New Brunswick, NJ 08901-1183;
Aaron Warren is a graduate student in the Department
of Physics and Astronomy working on his Ph.D in physics
education research.
Department of Physics and Astronomy, Rutgers
University, 136 Frelinghuysen Road, Piscataway, NJ
Michael Gentile is an instructor in the Department of
Physics and Astronomy.
Department of Physics and Astronomy, Rutgers
University, 136 Frelinghuysen Road, Piscataway, NJ
20 THE PHYSICS TEACHER Vol. 43, 2005
... Besides, the learning syntax of MI can be easily collaborated with the main principles in explaining natural phenomena based on the principle of energy, namely choosing a system and modeling the interaction between the system and the environment and between components in the system. The system model is understood as a way to make it easier to describe and explain observed physical phenomena, but it is also useful for predicting new phenomena that may arise (Etkina et al., 2006). ...
... All of these stages facilitate students in the development and development of conceptual understanding (Brewe et al., 2009;Hestenes, 1987;Jackson et al., 2008). The development of conceptual understanding can be through a graphical and diagrammatic representation of the model phenomena being studied (Etkina et al., 2006). ...
... It might occur because, in learning, there are still several groups of students who have difficulty choosing a productive system to describe the phenomenon and model it to solve problems. This was in line with the argument that to develop models for students was still difficult (Etkina et al., 2006). Therefore, it was necessary to know which concepts most students still have difficulties and how students think about the concept. ...
Full-text available
This study aimed to explore the effectiveness of modeling instruction based on a system for improving student's understanding of energy concepts on high school students. This Research was a mixed-method design with an embedded experimental model. The subject of this study was the 62 students of 11 th grade, at Senior High School in Nganjuk, Indonesia. Modeling instruction based on system learning could significantly improve students' understanding of concepts better than conventional learning. Based on the calculation of the effectiveness of learning using N-gain obtained for 0.33 (medium or low medium category) for the treatment class and 0.18 (low category) for the control class. It concluded that improved students' conceptual understanding of the treatment class was better than the control class. This research also was identified student's difficulties especially in differentiating forces and work. Students propensity to use p-prime in solving problems rather than using energy theorems.
... To address this unexplored aspect, our investigation presented in this paper builds on Ref. [12]. This is because we see the topic of orbital motion explored by Gregorcic et al. as particularly apt for highlighting the distinction between descriptive and explanatory models in physics [13]. Historically, Kepler's laws constitute a descriptive model for the motion of planets around the Sun, while Newton's laws of motion and his law of universal gravitation provide an explanatory model of the same phenomenon [14]. ...
... Before the researcher can finish the question, Adam answers. 13 Adam: I mean, they are directed toward each other [holds hands up to the IWB and follows both stars as they orbit, pointing his pinky fingers toward each other, Fig. 9, left] all the time. ...
... I, mechanistic reasoning entails the development of explanatory models. Etkina et al. [13] suggest that "explanatory models are based on analogiesrelating the object or process to a more familiar object or process." This is precisely what Adam and Beth do as they mechanistically reason via nondisciplinary semiotic resources: they generate for themselves an enacted analogy for the orbits of binary stars in the form of an embodied dance. ...
Full-text available
In this paper, we present a case study of a pair of students as they use nondisciplinary communicative practices to mechanistically reason about binary star dynamics. To do so, we first review and bring together the theoretical perspectives of social semiotics and embodied cognition, therein developing a new methodological approach for analyzing student interactions during the learning of physics (particularly for those interactions involving students’ bodies). Through the use of our new approach, we are able to show how students combine a diverse range of meaning-making resources into complex, enacted analogies, thus forming explanatory models that are grounded in embodied intuition. We reflect on how meaning-making resources—even when not physically persistent—can act as coordinating hubs for other resources as well as how we might further nuance the academic conversation around the role of the body in physics learning.
... A specific case of coordinating theory and experiments is the construction and evaluation of models. Models are simplified, and often abstracted quantitative representations of the system under investigation, constructed to describe, explain or predict the system's behavior (Etkina et al. 2006b). In introductory physics courses, models are usually instantiations of general, theoretical principles such as Newton's laws, in generic systems such as orbital motion under the influence of a centripetal force (Halloun and Hestenes 1987). ...
This study focuses on science teachers’ first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers’ self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers’ attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers’ self-efficacy in teaching physics in 9th grade. Also, teachers’ prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group’s self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers’ predispositions towards the integration of computational modeling into the physics teaching.
... For the physics teacher, testing and contrasting activities can be productive in that they can be leveraged to highlight the role of modeling in physics [17,18]. For example, in the case shown in figure 2 involving the 'glass' rectangle, a physics teacher could interject to ask students about whether the Algodoo software is still a valid environment for exploring physics phenomena. ...
Full-text available
In this paper, we present three types of activity that we have observed during students' free exploration of a software called Algodoo, which allows students to explore a range of physics phenomena within the same digital learning environment. We discuss how, by responding to any of the three activity types we identify in the students' use of Algodoo, physics teachers can springboard into a range of relevant physics discussions while supporting and valuing student agency and divergent thinking. Thus, while one might not expect students' undirected use of a digital tool such as Algodoo to be particularly worthwhile for the physics classroom, we highlight how students are never 'far from the shore' of a productive physics discussion.
Conference Paper
The purpose of this study was to examine the effectiveness of modelling instruction to enhance students’ conceptual understanding, compared to conventional learning. Modeling Instruction focused on the constructing model to explain the relationship among state quantities of the ideal gas (pressure, volume, and temperature), kinetic energy, internal energy, and average speed of the ideal gas. The participants were 121 high school students in Batu city. Data was collected using a test consisting of 9 multiple-choices items. We also asked the students to explain their reason for their choice. The students’ responses on the multiple-choice test were analyzed quantitatively, whereas the students’ explanations were analyzed qualitatively using the constant comparative method. The results showed that the students in modelling class gained higher conceptual understanding than that the students in the conventional class, with the d-effect size of 1.26. The N-gain on modelling class and the conventional class was 0.64 and 0.26, respectively. The students in modelling class showed a better understanding of the relationship between the quantities of gas state, kinetic energy, and average speed. However, most students still have difficulty in determining internal energy in the context when the number of gas particles may change.
The mystery tube is a fairly well known activity among science teachers for illustrating the nature of science. A variety of procedures have been presented for carrying out this activity, such as Scott Miller’s method based on the BCSE 5E Instructional Model. Mystery tubes and other “black box” activities allow the students to engage in the scientific process without requiring any specific knowledge ahead of time. The traditional mystery tubes are designed simply enough that it is a viable activity even for elementary and middle school age children. That simplicity though means that high school and especially college science and engineering students might figure out too quickly how to replicate the behavior, and become more confident than we want them to be in their model. In this paper, I present a new version of the mystery tube that is complex enough to give strong high school and college science and engineering students a reasonable challenge, so they can better experience the ongoing process of science and the inherent uncertainty.
Conference Paper
Full-text available
New English language version. Previously only available in Spanish. Discusses the nature of scientific concepts as multimodal integrations of linguistic, visual-graphical, mathematical, and operational-actional aspects. Implications for the teaching of science, connections between science and mathematics education, and multimodal approaches to science learning, tools, and analysis.
Full-text available
High school and college students often carry out of traditional physics courses loose bundles of vague and undifferentiated concepts about physical objects and their properties. Within the framework of schematic modeling, a scientific concept can be defined explicitly with five schematic dimensions: domain, organization, quantification, expression, and employment. Based on the level of commensurability between scientific concepts and individual students' own concepts, students' cognitive evolution into the scientific realm can take different directions ranging from reinforcing existing concepts to constructing novel ones on completely new foundations. Such evolution is promoted in a student-centered, model-based instruction. The newtonian concept of force is discussed for illustration, along with the results of tutoring two groups of Lebanese students to develop this concept in a schematic modeling approach. © 1998 John Wiley & Sons, Inc. Sci Ed82:239–263, 1998.
Full-text available
This work is the third edition of the classic text "Introduction to Concepts and Theories in Physical Science". It has been reworked to further clarify the physics concepts and to incorporate physical advances and research. The book shows the unifying power of science by bringing in connections to chemistry, astronomy and geoscience. In short, the aim of this edition is to teach good physics while presenting physical science as a human adventure that has become a major force in our civilization. New chapters discuss theories of the origin of the solar system and the expanding universe - fission, fusion, and the Big Bang-Steady State Conservatory, and thematic elements and styles in scientific thought.
Full-text available
Schematic modeling is presented as an epistemologic al framework for physics instruction. According to schematic modeling, models comprise the content core of scientific knowledge, and modeling is a major process for constructing and employing this knowledge. A model is defined by its composition and structure, and situated in a theory by its domain and organization. Modeling involves model selection, construction, validation, analysis and deployment. Two groups of Lebanese high school and college students participated in problem solving tutorials that followed a schematic modeling approach. Both groups improved significantly in problem solving performance, and course achievement of students in the college group was significantly better than that of their control peers.
Full-text available
Scientific models are used routinely in science not only as learning tools, but also as representations of abstract concepts and as consensus models of scientific theories. Students' experiences with scientific models help them to develop their own mental models of scientific concepts. This paper discusses the development and evaluation of an instrument to measure secondary students' understanding of scientific models. The results of a study with 228 secondary science students identify five themes about students' understanding of scientific models: scientific models as multiple representations; models as exact replicas; models as explanatory tools; how scientific models are used; and the changing nature of scientific models. The results highlight the need for greater emphasis on the teaching of the role and purpose of the concept of scientific models in science.
Full-text available
The Rutgers Astrophysics Institute is a program in which gifted high school students learn about contemporary science and its methods, and conduct independent authentic research using real-time data. The students use the processes of science to acquire knowledge, and serve as cognitive apprentices to an expert astrophysicist. A variety of naturalistic and statistical methods were employed to gather data concerning various changes in the students as a result of their participation in the institute. Specifically, we concluded that students were able to (a) distinguish between observational data and models, devise testing experiments, and reflect on the analysis and the interpretation of X-ray data; (b) achieve results comparable to those of regular Advanced Placement (AP) students on individual AP exam problems (the students had not taken AP Physics), (c) engage in elements of meaningful authentic research, and (d) change their approaches toward learning science. © 2003 Wiley Periodicals, Inc. J Res Sci Teach 40: 958–985, 2003
We present,a theory,of learning in science based on students,deriving conceptual linkages among,multiple models,which represent physical phenomena,at different levels of abstraction. The mod- els vary in the primitive objects and interactions they incorporate and in the reasoning processes that are used in running them. Students derive linkages among,models,by running a model (embodied in an inter- active computer,simulation) and reflecting on its emergent,behaviors. The emergent,properties they iden- tify in turn become the primitive elements of the more abstract, derived model. Wedescribe and illustrate derivational links among three models for basic electricity: a particle model, an aggregate model, and an algebraic model. Wethen,present results of an instructional experiment in which we compared high school students who were exposed to these model derivations with those who were not. In all other respects, both groups of students received identical instruction. The results demonstrate the importance of enabling stu- dents to construct derivational linkages among models, both with respect to their understanding of circuit theory and their ability to solve qualitative and quantitative circuit problems. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 806‐836, 1999 This article is concerned,with how,students come,to understand,abstract scientific models
In this paper, the role of modelling in the teaching and learning of science is reviewed. In order to represent what is entailed in modelling, a 'model of modelling' framework is proposed. Five phases in moving towards a full capability in modelling are established by a review of the literature: learning models; learning to use models; learning how to revise models; learning to reconstruct models; learning to construct models de novo . In order to identify the knowledge and skills that science teachers think are needed to produce a model successfully, a semi-structured interview study was conducted with 39 Brazilian serving science teachers: 10 teaching at the 'fundamental' level (6-14 years); 10 teaching at the 'medium'-level (15-17 years); 10 undergraduate pre-service 'medium'-level teachers; 9 university teachers of chemistry. Their responses are used to establish what is entailed in implementing the 'model of modelling' framework. The implications for students, teachers, and for teacher education, of moving through the five phases of capability, are discussed.
In this paper we examine students' views about the interpretation of experimental data within a specified science context. A written survey was completed by 731 science students drawn from upper secondary schools and universities in five European countries. An additional 19 upper secondary science students were interviewed individually after they had completed the survey. A minority of students emphasized the role of models in the interpretation of the data, despite the emphasis placed on theoretical models within the survey text. A substantial proportion of respondents focused on the quantity and quality of the data to be interpreted. Post-survey interviews showed that many students had difficulty in articulating a view concerning the role of theoretical models. Our data support previous studies, typically involving younger respondents, which have found that science students tend not to recognize the role of theoretical models in the interpretation of data.
Analogical models are frequently used to explain science concepts at all levels of science teaching and learning. But models are more than communicative tools: they are important links in the methods and products of science. Different analogical models are regularly used to teach science in secondary schools even though little is known about how each student's mental models interact with the various models presented by teachers and in textbooks. Mounting evidence suggests that students do not interpret scientific analogical models in the way intended, nor do they find multiple and competing models easy to understand. The aim of this study is summarized in the research question: How can students' understanding of the multiple models used to explain upper secondary chemistry concepts be enhanced? This study qualitatively tracked ten students' modeling experiences, intellectual development, and conceptual status throughout grade 11 as they learned about atoms, molecules, and chemical bonds. This article reports in detail a year-long case study. The outcomes suggest that students who socially negotiated the shared and unshared attributes of common analogical models for atoms, molecules, and chemical bonds, used these models more consistently in their explanations. Also, students who were encouraged to use multiple particle models displayed more scientific understandings of particles and their interactions than did students who concentrated on a “correct” or best analogical model. The results suggest that, when analogical models are presented in a systematic way and capable students are given ample opportunity to explore model meaning and use, their understanding of abstract concepts is enhanced. © 2000 John Wiley & Sons, Inc. Sci Ed84:352–381, 2000.