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CHEMISTRY EDUCATION:
RESEARCH AND PRACTICE IN EUROPE
2000, Vol. 1, No. 1, pp. 109-120
RESEARCH REPORT
New educational technologies (NET)
Nitza BARNEA and Yehudit J. DORI*
Department of Education in Technology and Science, Technion, Haifa
COMPUTERIZED MOLECULAR MODELING -
THE NEW TECHNOLOGY FOR ENHANCING MODEL PERCEPTION AMONG
CHEMISTRY EDUCATORS AND LEARNERS
Received: 21 September 1999; revised: 29 November 1999; accepted: 1 December 1999
ABSTRACT:
Insufficient emphasis is put in science teaching on the fact that models are simulations of
reality based on a certain theory and that molecules are not miniatures of the models that represent them.
We investigated how chemistry teachers and high school students who enrolled in a special program
perceive the nature and functions of models by using a model perception questionnaire. In the research 34
pre- and in-service teachers attended a 14 hours workshop on models and their model perception was
investigated with the model questionnaire. This questionnaire was also administered to two groups of
high-school chemistry students – experimental and control – which studied chemical bonding and
structure. The teachers of the experimental group participated in the training and emphasized the model
concept via using various models including computerized molecular modeling, whereas the control group
teachers taught the topic in the traditional way, without the aid of computer and without emphasizing the
model concept. Overall, the in-service training on models has improved several aspects of the teachers'
model perception in both stages. This finding is confirmed by the significant difference found between
the experimental and control groups of the high school students. Students’ results indicate the
effectiveness of the treatment on students’ conceptualizing the meanings of models, especially in the
domain of chemistry. [Chem. Educ. Res. Pract. Eur.: 2000, 1, 109-120]
KEY WORDS:
computerized molecular modeling; model perception; high-school chemistry; in-service
training
_______________
* Currently on sabbatical at the Center for Educational Computing Initiatives, Massachusetts Institute of
Technology
INTRODUCTION
Modeling and simulation are used in research and education to describe, explain and
explore phenomena, processes and abstract ideas. A great virtue of a good model is that by
suggesting further questions it takes us beyond the phenomenon we began with to formulate
hypotheses that can be experimentally examined (Toulmin, 1953; Bagdonis & Salisbury, 1994).
It is recommended that science educators should become less concerned with the presentation of
facts and concentrate on showing the centrality of models in research and education (Raghavan &
BARNEA & DORI
110
Glaser, 1995). However, most educators use a limited number of static models, and do not
emphasize the way in which models are created, their essential role in science learning, or their
advantages and limitations (Gilbert, 1997; Bagdonis & Salisbury, 1994; Oversby, 1995).
According to Gilbert Boulter (1998) models are differentiated between target systems, mental
models, expressed models, consensus models and teaching models. One way of performing a
simulation with different model types quickly and efficiently is by a computerized environment.
The use of computers in science and technology teaching has various advantages. Among these
are the options of providing for individual learning, simulation, graphics, and the demonstration
of models of the micro and macro world (Dori, 1995; Dori & Hameiri, 1998; Lazarowitz &
Huppert, 1993). Computers enable students to solve a variety of problems while carrying out
their own research at their own pace.
The use of molecular models to enable visualization of complex ideas, processes and
systems in chemistry teaching has been widespread for a long time (Peterson, 1970). The choice
of the type of model has an impact on the mental image that the student creates. One problem
that arises while using models is that insufficient emphasis is placed on the fact that models are
theory-based simulations of reality. Applied to chemistry, ball and stick models derived from
polystyrene spheres and plastic straws are not merely enlargements of the molecules they are
intended to represent. These are analogue models that are used to explain new and abstract
concepts. Some of the analog properties are similar to aspects of the target they are representing.
For example, the relative diameter of the spheres represents the size of the different atoms, but
other aspects are not reflected in the model; all straws are equal, while bond length are not.
Other analog models focus on different properties of the molecule, thereby creating multiple
ways of representing the same molecule. Teachers frequently use just one type of model, limiting
students' experience with models and causing their model perceptions to be partially or
completely inadequate. The use of computerized models places more emphasis on the creation of
mental models by students and their use to make prediction (Gilbert & Boulter, 1998).
The aim of this study is to investigate how chemistry teachers and students perceive the
nature and functions of models. Teachers’ perceptions are important, since if teachers do not
have the necessary understanding of the nature and role of models in the development of a
discipline, they probably will not be able to incorporate them properly in their teaching (Gilbert,
1991; Barnea & Dori, 1996).
Students need more experience with models as intellectual tools that provide contrasting
conceptual views of phenomena, and more discussion of the roles of models in the service of
scientific inquiry (Gabel and Sherwood, 1980; Grosslight, Unger, Jay and Smith, 1991). Harrison
and Treagust (1996) found out that most 8-10 graders prefer models of atoms and molecules that
depict these entities as discrete, concrete structures and therefore, prefer space-filling molecular
models. To enhance understanding a multiple range of models should be used, as students prefer
the use of computerized molecular models instead of the plastic ones (Barnea, 1997; Dori and
Barnea, 1997).
RESEARCH GOAL AND POPULATION
The goal of our study was to develop a computer molecular modeling (CMM) learning
environment via implementing a constructivist approach in high-school chemistry, and to
COMPUTERIZED MOLECULAR MODELING
111
examine its effect. Teachers’ and students’ perception of the nature and functions of models were
investigated by using a model perception questionnaire.
The research included two populations and was administered in two stages. The first
stage included 34 teachers who participated in an in-service training.The second stage was
implemented in an urban high school in Israel, involving five heterogeneous classes (N=169) of
tenth graders (age 15) who studied chemistry for the first year. The experimental group – three
classes (N=97) – worked on the subject of geometric shapes of molecules with the molecular
modeling software and a dedicated working booklet. Two other classes, which served as a control
group (N=72), studied the subject in the traditional way.
Four teachers were involved in the second stage, two taught the experimental group and
two the control group. All the teachers had academic degrees in chemistry and at least 15 years of
teaching experience.
RESEARCH METHODOLOGY
The CMM software and working booklet
The software tool used in the study was the “Desktop Molecular Modeler
(DTMM)”
(Crabbe & Appleyard, 1994 - this package is published by Oxford University Press, Walton St.
Oxford OX26DP, UK), a PC-compatible package which enables three- dimensional molecule
visualization. Several representation styles – colored lines, space filling, quick filling and ball
and stick – are available. The software is controlled through pull-down menus, which are easy to
master. The most powerful features of the software are molecular synthesis and energy
minimization (Gulinska et al., 1991). The energy minimization routine optimizes the geometry
of the newly created molecule by using an algorithm that creates the optimal three-dimensional
conformation.
As part of the research we have designed and written a self-study booklet, which fosters a
constructivist learning approach of various geometric shapes of molecules (Barnea & Dori,
1999).
Teachers’ training – First stage
The pre and in-service teachers (N=34) attended a 14 hour workshop on models and
modeling. During the training teachers learned the different meanings of models and experienced
various types of models. They discussed in small groups the difference between mental models,
expressed models, consensus models and teaching models, and tried to categorize models they
use into these classifications.
Teachers spent 6 hours familiarizing with the molecular modeling software. They worked
with a database that was especially designed by the researchers for the high-school curriculum.
Teachers chose molecules from the data-base, measured their bond length and angles, rotated
them and watched them in various styles: bonds only, stereo lines, ball and stick and space
filling.
BARNEA & DORI
112
After the information gathering stage, teachers checked whether the geometric shapes
they viewed for those molecules, were consistent with their prior knowledge. Where mismatches
were found they discussed the advantages and disadvantages of the models, thus getting more
acquainted with the abilities and limitations molecular modeling offers as a teaching tool.
Experimental group – Second stage
The teachers of the experimental group participated in the training and emphasized their
new insight of the model concept while using various models including computerized molecular
modeling. The experimental group—three classes of tenth graders—participated in three
computer laboratory sessions of two hours each, and used the database described above. Since
the computer laboratory had only ten workstations, students worked in pairs and had the
opportunity for interactions and group work. Students chose molecules from the database,
measured their bond length and angles, rotated them and watched them in various styles: bonds
only, stereo lines, ball and stick and space filling. Students also used stereo view glasses to get a
3D perception of molecules.
After the information gathering stage, students decided what geometric shape those
molecules had. Later, they discovered the similar properties of molecules that shared the same
structure by building their Lewis formula. These students could identify the graphic
representation easily, and this, in turn, helped them decide whether the molecule is polar or non-
polar. Sometimes, the teacher used rigid models in order to clarify some misunderstandings,
stressing the differences among the various models. Students discussed in group the advantages
or disadvantages of each model, enhancing their understanding of models and modeling. During
the sessions students were very enthusiastic and concentrated on their work, discussed the results
and conclusions with their peers, and called for assistance or approval of the teacher only when
they disagreed or could not find a proper answer.
Control group – Second stage
The two teachers of the control group participated in the training too, and were familiar
with the computerized molecular modeling package. They chose to teach in the traditional way
without the aid of the computer out of their free will, and (possibly) anxiety of computers. The
control group—two classes of tenth graders—studied the geometric shapes of molecules in the
traditional way in the classroom, and used only one type of plastic models.
Model questionnaire
We examined the perception of the model concept among pre- and in-service chemistry
teachers in the first stage of the study. In the second stage of the research we examined model
perception among high-school students that were taught by these teachers. The research tool was
a questionnaire on models in general and on models in chemical bonding and structure in
particular. It is a revised version of the first part of the questionnaire of Barnea et al (1995). In
this part, responders were asked to mark and explain if they agree, partially agree or disagree
with 16 statements.
COMPUTERIZED MOLECULAR MODELING
113
The second and the third parts of the questionnaire were developed especially for this
research (Barnea, 1996). The second part of the questionnaire related to the use of models in
chemistry and contained open questions related to bonding and structure. The third part dealt
specifically with models used in chemistry. The responders were asked to specify model type(s)
which are used to explain the following examples of visible phenomena.
1. Copper in the solid state conducts electricity.
2. Gaseous chlorine does not dissolve in water, whereas, hydrogen chloride gas dissolves well in
water.
3. Solid graphite conducts electricity, while diamond does not.
4. Sodium chloride does not conduct electricity in solid state, but it conducts electricity in an
aqueous solution.
As a result of the first stage of the study analysis (in-service training) the following
change was incorporated in third part of the questionnaire. Graphic representations were added to
represent models, and responders were asked to write the name of model/s or to describe the
chemical phenomena these models represent. When items were given textually, responders were
asked to draw the appropriate model which explains this phenomena. Exemplary items of the
third part (after modification) are presented in Table 1.
The 16 statements in the first part were divided into four categories by the researchers.
Four experts in chemistry education were independently asked to read the questionnaire and
TABLE 1
. Exemplary items from the third part of the model perception questionnaire.
The phenomenon The model which explains the phenomenon
1. The model which describes the energy levels of the
electron in the atom. Electrons fill these levels in a
regular pattern: 2, 8, 18, etc.
2. Solid Copper conducts electricity
3.
4.
Gaseous chlorine does not dissolve in water
5. The structure of C– Graphite.
BARNEA & DORI
114
TABLE 2
. Categorized items from the first part of the model perception questionnaire.
Category 1
Models describe and
explain phenomena
Category 2
The model as a mental
or a representative
structure
Category 3
Ways to create models
Category 4
The model as a tool for
research and prediction
* The only function of
models in science is in
teaching.
* The terms 'model' and
'theory' are
synonymous.
* All models are
creations of the human
intellect.
* Models are aids that
are used to obtain
knowledge of nature
* A model always
provides a complete
description of the
object, structure or
process in nature that it
models.
* Any representation of
a structure or of a
process is called a
model.
* Models exist in
nature.
* Models can be used to
predict phenomena
structures or processes
that have not been
observed.
* Models play an
important role in the
explanation of
phenomena.
* A scientist has more
knowledge of an object,
process or structure that
is represented by the
model.
* A model is formulated
using facts obtained by
experiment and/or
observation.
* Models play an
important role in
scientific research, in
medicine and the drug
industry.
* An important function
of any model is to
describe an object, a
process or a structure in
nature.
* All models are
representations (some
are purely visual, some
can be touched).
* All models are mental
images i.e. exist only in
the human mind.
* Models are temporary.
With the increase of
knowledge a model
becomes obsolete or
useless and is either
adapted or replaced by
another model.
divide the statements into categories to check the validity of the decision. The categories thus
obtained are the following: models as describing and explaining phenomena; models as mental
representations; model generation and models as prediction and research tools. Table 2 represents
the 16 questionnaire items divided into the four categories.
RESEARCH RESULTS
The model perception questionnaire was administered to pre- and in-service chemistry
teachers, as well as to high-school students. The score consisted of four sub-scores: a score for
each part of the questionnaire and a score on the categories in the first part.
·
Part I
is the sum of the scores gained on the 16 statements using the following scale:
completely agree - 2, partially agree - 1, oppose - 0. Opposing statements got the opposite
score, i.e., fully agree got the score 0 whereas oppose scored 2. Since each statement/item
scores between 0 and 2, the total score ranged between 0 to 32.
·
Category
sums the knowledge of the different categories and ranges from 0 to 4. Each
category consists of 4 items. Each item has a maximal score of 2 therefore the score on a
category can range from 0 – 8. In order to get a score 1 on a category, one has to receive at
COMPUTERIZED MOLECULAR MODELING
115
least 5 out of 8 possible points given on a category. If one gets less than 5 points, one’s score
will be 0 in that category. The sum of these scores results in the Category score. If a responder
knows all four categories, his/her score will be 4.
·
Part II
summarizes the second part of the questionnaire, which includes three open questions.
In this part the responder has to verbally define a model (item no. 17), give three examples for
various models that are used in chemistry (item no. 18), and explain what is the role of models
in chemistry (item no. 19). The answers to these statements were categorized into levels.
Statements 17 and 18 got scores ranging from 0 to 3 and statement 19 could score from 0 to 4,
overall the third score could range from 0 to 10. In this way item 19 gets more weight than the
other two, but this question deals with the highest level of understanding. While item 17
requires knowledge, and item 18 is a question of application, item 19 requires analysis of the
definitions and examples in order to generalize the role of a model, which is a much heavier
task. Some examples for representative answers and their scores are presented in Table 3.
·
Part III
, given to the third part of the model perception questionnaire, relates to eight items.
A score of 0 was given to a wrong answer, 1 was given to a partial or verbal explanation,
where as 2 was given only if a full explanation was accompanied with a graphical description.
The maximum score could thus be 16.
Teachers’ results
The teachers’ scores on the various parts of the questionnaire are represented in Table 4.
34 pre- and in-service teachers took part in the workshop and answered the questionnaire.
From
Table 4 we see that even after the intensive training, some of the scores are still not high in the
research population. We can relate this result to the difficulties that arise when a clear and
unequivocal definition of the model concept has to be formulated. This is a problematic issue, as
the model concept is known intuitively but there is usually no definite distinction among model,
TABLE 3.
Exemplary responses and their given score in Part II of the model perception questionnaire.
Item No. Student Response The Score
17.
*
A model is an exact copy of reality 1
Define a model
*
A model is developed in order to explore
a certain phenomenon by overlooking
or stressing some aspects 2
*
A model is developed in order to pursue
an idea or to perform a research 3
18.
*
Models of the atom, like ball and stick 1
Give three examples
*
Various models of lattices - more than one example 2
for various models
*
Models in chemistry which do not deal with
that are used in the atom structure - more than one 3
chemistry
19.
*
The models support understanding 1
Explain what is
*
A model describes and simplifies phenomena 2
the role of models
*
A model relates between micro to macro 3
in Chemistry
*
A model helps to predict events and phenomena 4
BARNEA & DORI
116
TABLE 4.
Mean scores and standard deviation of the different parts of the model perception
questionnaire for the pre- and in-service teachers (N=34).
Score Mean S.D. Minimum Maximum Range
Part I 23.30 2.73 17 29 0-32
Category 2.94 1.12 0 4 0-4
Part II 5.54 1.78 0 10 0-10
Part III 7.67 2.89 2 14 0-16
theory and reality. Moreover, the ways in which models are created are not clear.
In Table 5, the correlation coefficients among the various parts of the questionnaire are
represented. We can see high and significant correlation between part I of the questionnaire and
the category score, and between part II and the category score. In other words, the category score
is in high correlation with both the general part and with the chemical part of the questionnaire.
Analysis of the teachers’ responses to the Part II, the open part of the model
questionnaire, revealed that most of the participants thought of a model as a way to describe a
process or a phenomenon which could not be seen. A distinction between a mental image and a
concrete model that can be seen and touched was made. There was agreement among all
responders that models help explain and understand phenomena through simplification and
visualization.
The examples given for the use of models in chemistry were all in the domain of atomic
and molecular structure. Most teachers perceive models as a means to enlarge or reduce the real
process or phenomenon, or to illustrate some theory. Only few teachers thought of models as
mental images.
TABLE 5.
Correlation’s among the different parts of the model perception questionnaire for pre- and
in-service teachers (N=34).
Part I Category Part II Part III
Part I 0.743 ** 0.02
ns
0.306
ns
Category 0.380 * 0.118
ns
Part II 0.05
ns
ns
p > 0.05
* p < 0.05
** p< 0.0001
COMPUTERIZED MOLECULAR MODELING
117
TABLE 6.
Analysis of variance (ANOVA) of students' mean scores on total score for the model
perception questionnaire.
Source of Variance SS df F p
Research group 2009.30 1 14.10 0.0002
Gender 455.25 1 3.20 0.076
Interaction* 142.62 1 1.00 0.319
* (Research group * gender)
High school students’ results
An overall score for the model questionnaire was calculated, by summing its four
components, such that each part had the same contribution to the total score. Analysis of
variance, shown in table 6, indicates that there is a significant difference between the research
groups. However, no significance was found for gender or interaction between gender and
research group factors.
Similar results were obtained when this analysis was done for each part of the
questionnaire independently. Significant differences in favor of the experimental group were
found regarding Part I, Part III and the score for Category. For Part II however the difference is
not significant. The results are presented in Table 7.
A thorough investigation into items 17-19, which compose the second part of the
questionnaire, revealed that the experimental group students scored higher on items 17 and 19,
but the control group students did better on item 18. The corresponding results for the students as
well as the teachers in the second phase are shown in the Figure. In item 18, responders were
requested to specify three different types of models, which are in use in chemistry. After the
experimental students had experienced intensively the computerized molecular modeling, most
of them gave examples from their practice in this area: ball and stick or space filling models,
diamond or graphite models, etc.
TABLE 7
. Mean scores, standard deviation and t-test procedures of different parts of the model
perception questionnaire by research group.
Score Group N Mean S.D. t p
Part I Experimental 86 21.20 3.35 3.41 0.0008
Control 64 19.22 3.71
Category Experimental 86 2.67 1.12 3.62 0.0004
Control 64 2.00 1.16
Part II Experimental 86 3.98 2.18 1.19 0.235
Control 64 3.55 2.14
Part III Experimental 86 3.79 3.08 4.45 0.0001
Control 64 1.88 2.20
BARNEA & DORI
118
0
0.5
1
1.5
2
2.5
mean score
item 17 item 18 item 19
N o . o f item
Teachers
Exp.
Conrol
FIGURE.
Mean scores for items 17-19 by research group.
Students from the control group did not have the same experience, therefore their
examples were from various domains, such as: accumulation states, energy levels, models of an
industrial plant, and of course models dealing with bonding and structure. Due to the diversity of
answers of the control group, it scored higher than the experimental group on this item. From
Figure 1 we see that teacher scores were higher than those of the students in both groups.
Nevertheless there is still room for improvement in both.
The rate of success for the various categories (1-4) for the experimental group was 70%,
66%, 70%, and 50% respectively. For the control group the corresponding figures were 78%,
46%, 34%, and 32%. These figures show that experimental group students perceived the model
concept as expressed by items of categories 2, 3, and 4 better than the control group students.
Items of category 1 – models as describing and explaining phenomena – which were relatively
straightforward, were correctly perceived by both groups regardless of the learning environment.
While the experimental group students scored better than the control group students in
categories 2, 3 and 4 (p < 0.05), both had difficulties with category 4, which deals with the ability
of a model to explore and predict occurrences. The low score this category got in both groups is
due to the fact that the aspects of the model as prediction and research tools were not emphasized
in the learning process. Students therefore did not agree with statements claiming that a model
does have these exploration and prediction abilities.
The corresponding results for the teachers were: 92%, 61%, 83% and 62%. We see that
for the teachers too category 4 was difficult, but also category 2 which dealt with mental models.
It points out that there is still work to prepare teachers and widen their model perception.
COMPUTERIZED MOLECULAR MODELING
119
CONCLUSION AND FUTURE RESEARCH
The results of this study indicate that overall the in-service training program on models
put emphasis on many aspects of the trainees' model perception. The importance of the training is
that it made teachers realize the role of models and their initial perception of models was
expanded. The most significant outcome of the training is reflected in the school implementation,
where a noticeable difference between experimental and control groups was found. Experimental
group students scored higher than those of the control group in all four score types of the model
perception questionnaire. The difference turned out to be significant in the category and the
chemistry models parts. This indicates the effectiveness of the treatment on high school students’
conceptualizing the meanings of models, especially in the domain of chemistry.
Teachers appreciate the value of models as well as their limitations. Hence, more time
should be invested to introduce pre- and in-service chemistry teachers to the model concept and
to their use in science in general and in chemistry in particular. More research is needed to
determine the long-term effect of such training on participant perceptions and the influence it had
on students of the trainee teachers.
ADDRESS FOR CORRESPONDENCE:
Nitza BARNEA, Department of Education in Technology and
Science, Technion, Haifa 32000, Israel; e-mail: nitza@techunix.technion.ac.il
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