Improving Students’ Performance through the Use of Simulations
and Modeling: The Case of Population Growth
Kathy Lea Malone
1
and Anita Schuchardt
2
1
Graduate School of Education, Nazarbayev University, Kabanbay Batyr 53, Astana, 01000, Kazakhstan
2
Department of Biology Education, University of Minnesota, Minneapolis, MN, 53455, U.S.A.
Keywords: Modelling Instruction, Science Modelling, Simulations, Population Growth, Scientific Reasoning.
Abstract: Internationally, students have difficulty interpreting and drawing conclusions from data. These skills are
essential components of scientific reasoning, an ability that has been shown to correlate with conceptual
change. Providing greater opportunities for students to engage in scientific practices such as modelling in
order to collect and reason with data has the potential to improve scientific reasoning skills. However, in
some subdisciplines of biology, such as population growth, data collection needs to occur over time scales
that are unfeasible in a classroom setting. Computer-based simulations of biological phenomena are one
way to overcome this limitation, but their effect on scientific reasoning has been under investigated. This
study researched the effect on scientific reasoning of computer-based simulations in a context that employed
a specific type of model-based reasoning (Modelling Instruction). Students who used computer-based
simulations in a Modelling Instruction context showed increased scientific reasoning post-instruction
compared to a comparison group. Moreover, shifts were observed in the intervention group towards more
formal reasoning whereas no such change was observed with the comparison group. This result suggests
that computer-based simulations should be further explored as a way to improve student scientific
reasoning, particularly in contexts where laboratory investigations are not feasible.
1 INTRODUCTION
Internationally, research studies and educational
policy groups have affirmed the need to have
students learn higher order thinking skills in science
classrooms (Mullis et al. 2016, p. 8; Organisation of
Economic Co-operation and Development, 2016, pp.
103-109). The results from the Programme for
International Student Assessment (PISA) have
suggested that across nations, students not only have
difficulty interpreting scientific data but also
drawing conclusions from that data (Organisation of
Economic Co-operation and Development, 2016, pp.
37-46). This deficiency might be caused by a lack of
opportunity for students to practice new skills within
the context of scientific laboratories. One contributor
might be lack of facilities. However, a more
common barrier, especially in Biology (and
particularly in topic areas such as population growth
and evolution), is likely to be difficulties in data
collection because of the time needed to see multiple
generations of data (Heaps et al. 2016, p. 221;
Oswald and Kwiatkowski, 2011, pp. 469-471). One
possible way to allow for the collection of data in
these areas that require extended time is through the
use of specially designed simulations (Huppert et al,
2002, pp. 809-812). However, there are few studies
that attempt to use simulations in population growth
within the context of secondary biology classrooms
which emphasize scientific modelling.
Interpreting and drawing conclusions from data
are essential components of scientific reasoning, a
skill that has been shown to correlate with
conceptual change (e.g., Coletta et al, 2007, p. 237;
Moore and Rubbo, 2012, pp. 4-5). Thus, it is
important to make sure that interventions improve
students’ scientific reasoning skills as these skills
can affect students’ lifelong learning. However, few
studies have focused on assessing shifts in students’
scientific reasoning when using simulations in the
context of a modelling instruction class in biology.
This paper attempts to fill this gap by describing
a quasi-experimental study in the USA that tests the
effectiveness of a simulation developed to introduce
population growth within the context of a specific
type of scientific modelling curriculum, Modelling
222
Malone, K. and Schuchardt, A.
Improving Students’ Performance through the Use of Simulations and Modeling: The Case of Population Growth.
DOI: 10.5220/0007612802220230
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 222-230
ISBN: 978-989-758-367-4
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Instruction. This study was guided by the following
research goal:
1) Will Modelling Instruction students experiencing
a population growth simulation outperform a
comparison group in terms of scientific
reasoning?
2 LITERATURE REVIEW
Within this section we will investigate past
instructional interventions in population growth as
well as the use of scientific modelling simulations
and Modelling Instruction (MI) within science
classrooms. In addition, we will specifically discuss
the use of conceptual modelling and modelling
simulations within the context of population growth.
The end of this section will include a discussion of
past studies focused on scientific reasoning and
simulations.
2.1 Learning Challenges in Population
Growth
Numerous studies show that at both the
undergraduate and secondary levels, student
conceptual knowledge about population growth is
sprinkled with misconceptions. Brody and Koch
(1990, p. 23) found that high school students
consider an ecosystem’s resources to be limitless.
Munson (1994, p. 32) found that undergraduate
students think that populations exist in two constant
states: one of growth or one of decline. However,
Munson (1994, p. 32) also discovered that students
can also believe that populations increase till limits
are reached then the population crashes thus going
extinct. The effect of predator-prey relationships has
also been shown to cause student difficulties since
many think that two organisms can only affect each
other if they share this specific relationship
(Griffiths and Grant, 1985, pp. 430-431). Stammen
(2018, pp. 149-151) found that middle school
students believe that competition within an
ecosystem always involves aggressive interactions.
These alternative conceptions do not portend well
for students learning a strong correct conception of
population growth that would support learning in
other biological concepts such as evolution and
genetics.
2.2 Models, Modelling and Modelling
Instruction in Biology
Science modelling is the process by which students
are guided in the construction of science models or
empirically testing the effectiveness of science
models. A scientific model is not only a 2D or 3D
representation of a science phenomenon but can
include other representations such as graphical,
pictorial, mathematical or verbal.
Little work has been done towards incorporating
the use of models and modelling into biology either
at the secondary or the undergraduate level.
Passmore and Stewart (2002, pp. 186-194) designed
the MUSE pedagogy for secondary school students
that focused on students’ comparison of previously
determined models against empirical data. At the
college level Dauer et al (2013, pp. 240-241) had
undergraduates develop models in multiple
biological areas (one of them population growth).
While both determined that students understood
models better neither study had a control group, nor
did they test for conceptual understanding.
Modelling Instruction is a scientific modelling
pedagogy that makes use of student generated
models to develop multiple representations. See
Figure 1 for an example of model representations in
population ecology.
Figure 1: Scientific Model representations for Population
Growth Model (adapted from Dukerich, 2015, p. 1315).
Modelling Instruction makes use of a Modelling
cycle (see Figure 2) that focus on student
development of scientific models from empirical
data. The students use the data to produce a model
that consists of multiple representations. The initial
science model is used to make predictions about
behaviour which can be checked against the initial
empirical data. If predictions are not in line with the
empirical data, then revisions to the model and its
representations are produced. The revised model is
then tested in other contexts. The cycle is continuous
so that at any time predictions to not match data
Improving Students’ Performance through the Use of Simulations and Modeling: The Case of Population Growth
223
revisions of the model representations are
considered. This allows students to develop a robust
understanding of the concepts being developed.
The use of multiple representations alone has
been shown in a number of studies to produce
improved conceptual learning (e.g., Dori and
Belcher, 2005, pp. 211-212; Won et al, 2014, pp.
863-864).
Figure 2: The Modelling Cycle (adapted from Malone et
al, in press).
Modelling Instruction has been shown to be
effective at increasing student conceptual
understanding in other disciplines such as physics
(Jackson et al, 2008, pp. 15-16; Malone, 2008, pp. 4-
12, Malone and Reiland, 1995, p. 411), and
chemistry (Malone and Schuchardt, 2016, pp. 4-5).
However, only a single study has been published on
the effect of Modelling Instruction in Biology
(Malone et al, in press). Malone et al, in press)
demonstrated that the use of Modelling Instruction
and physical simulations can be effective in student
conceptual understanding in evolution. They showed
that not only did students show a decline in
alternative conceptions but also there was an
increase in their use of multiple representations to
explain evolutionary concepts over that of the
comparison group.
2.3 Simulations, and Modelling in
Population Growth
A meta-analysis showed that simulations have a
beneficial effect over that of units with no
simulations in the secondary school (D’Angelo et al.
2014, p. 42).
In a review Smetana and Bell (2012, pp. 1362-
1364) found that the use of computer simulations
might depend upon how they are used in the
classroom. They suggested that in order to be the
most effective simulations should be used when
incorporated into pedagogy and encourage reflection
on the part of students. One study that tested the
ability of a single secondary school student to
produce a model about predation using a simulation
produced with Net Logo (Wilensky and Reisman,
2006, pp. 203-205) had mixed results. While the
student could produce a model of predation that was
predictive of observed lab outcomes in the
simulation, it was not consistent with real-life
observations. Another study used a simulation in the
seventh grade called WISE (Donnelly et al. 2016,
pp. 1344-1359). The students did show a gain in
conceptual understanding, but there was no
comparison group. However, few studies focus on
the effect of simulations which are embedded in a
modelling-based curricula unit, especially in the area
of population growth.
The use of models and modelling in the teaching
of evolution and population ecology within the
context of an engineering themed unit has been
shown to be effective at the secondary level in terms
of student conceptual understanding (Malone et al,
2018, pp. 42-44). This unit also incorporated a series
of excel based simulations that looked at not only
population growth but also natural selection. This
quasi experimental study showed that there was a
significant gain in student understanding of
population growth and natural selection over that of
a comparison group as well as an increase in student
use of multiple representations. In addition, students
demonstrated a greater fascination with science.
However, this study did not test for shifts in student
scientific reasoning skills.
The studies showing conceptual gains used
simulations that were incorporated into specific
pedagogical units. However, none focused on
scientific reasoning skills of students.
2.4 Scientific Reasoning and
Simulations
As mentioned in the introduction the link between
scientific reasoning and science has been studied.
However, fewer studies have focused on the link
between simulation use in science and scientific
reasoning.
The studies showing a link between scientific
reasoning and science have been mostly conducted
at the college level. For example, Coletta, Phillips
and Steinert (2007, p. 236) showed that when active
learning methods are used with STEM college
majors, student scientific reasoning skill as
measured by the Lawson’s Classroom Test for
Scientific Reasoning (LCTSR) was highly correlated
CSEDU 2019 - 11th International Conference on Computer Supported Education
224
to gains in physics knowledge. This study also
demonstrated a similar correlation effect in one
secondary school using Modelling Instruction.
However, pre to post gains in scientific reasoning
when using simulations in the context of a
Modelling Instruction unit have not been assessed.
Therefore, it is not known how modelling based
simulation activities affect students’ shifts in
scientific reasoning pre to post implementation.
This study attempts to fill these gaps by testing
the effects on students’ scientific reasoning skills of
a science modelling simulation embedded in a
Modelling Instruction population growth curricular
unit.
3 METHODS
This study is a quantitative evaluation study looking
at the differences in scientific reasoning skills pre to
post instruction between a treatment and a
comparison group of students.
3.1 Research Questions
The study was guided by the following research
questions.
1) Will Modelling Instruction students experiencing
population growth simulations display a greater
increase in scientific reasoning skills than
comparison students?
2) Will Modelling Instruction students demonstrate
a greater shift towards formal reasoning in terms
of Piagetian reasoning stages?
3.2 Participants and Settings
The participants in this study were high school
students located in the Midwestern region of the
United States enrolled in regular level biology
courses. All of the students attended a suburban
school district but were from a mix of high schools
within the district. The implementing cohort
consisted of 205 students and were taught by a
teacher in their first year of implementing modelling
instruction. The comparison cohort of 141 students
was taught by two different teachers. All three
teachers had similar backgrounds and total years of
teaching experience.
3.3 Population Growth Modelling
Instruction Unit
The constructed unit started with a pre-assessment
activity that asked students to consider what would
happen if all the plants in the world died. This was
used to draw out student preconceptions. No
“correct” answers were given to the question.
Students shared their initial thoughts and ideas. In
addition, the implementation teachers requested that
students supply their reasoning for any of the claims
they were making.
The students were then introduced to two species
of paramecium (P. caudatum and P. aurelia) using
microscopes. The students were asked what they
thought would happen to the size of each population
after 100 years if they were in a place with no
predators? The students were broken into groups to
develop a prediction and to develop methods to
represent or “show” their predictions to the rest of
the class. This was the first Modelling Instruction
modelling cycle for the implementation students.
Thus, most drew pictures about what would happen
over time and very few represented their predictions
in a graphical form. During group sharing, if some
students developed diagrams, storyboards and
graphical representations of their predictions these
were shared with the class during class discussion.
Thus, after the consensus depending on student
backgrounds the initial representations were quite
diverse. In this case most of the graphical
representations were in the form of bar graphs and
pie charts. As students were sharing their
representations, they were asked to describe the
biological meaning behind them.
After the prediction phase the teacher asked the
groups to consider how they might investigate this
question using paramecium. The students had to go
to the internet to find the life cycle of paramecium
and consider the number of offspring. In addition,
they were asked to consider which variables would
be independent, dependent and which would be held
constant. The students discovered that if they
actually used live paramecium the time to collect
data would be much too long to for their classes.
Consequently, the teachers introduced the use of
google sheet simulations to the students. The lab was
conducted by either dividing the class in half so that
each half worked with one or the other organism.
This allowed groups to see what happened to the
output due to the difference in growth rate between
these two organisms.
The google sheet simulation consisted of an
input sheet where students could decide upon a
Improving Students’ Performance through the Use of Simulations and Modeling: The Case of Population Growth
225
number of conditions, a graph and data sheet as well
as an equations sheet. The equations sheet was there
to show students the mathematical growth formula if
the teacher desired to do so.
The google sheet simulations were designed to
contain the variables that were requested by pilot
students for either P. caudatum or P. aurelia. The
input page asked students to select their initial
population size, whether they had limited or
unlimited resources, and the generation time. In
addition, students had to input the container size,
number of offspring produced per generation as well
as the average number of offspring that die each
generation. See Figure 3.
At this point, depending upon students’ abilities
with graphs, they either focused on just the data
charts (see Figure 4) or the simulation generated
graphs (see Figure 5). The ones that focused only on
data charts were asked to hand graph their output
data. This allowed for a comparison of graphing
techniques between groups and the ability to discuss
the reasons why a line graph was a better selection
for the output data rather than a bar graph.
Figure 3: Simulation Input Page.
Figure 4: Simulation's Data Output Page.
The output generated included the number of
“observed” as well as “predicted” paramecium
depending upon their input variables on the input
page. The students were tasked with changing their
input page in order to match the observed numbers
on the graph (see Figure 5). When they did this, they
were asked to discuss it with their teachers and
explain why and what part of their prediction they
were changing.
After the lab, students were asked to construct
large poster displays which detailed how their
predictions changed for both limited and unlimited
food supplies, large vs small containers, etc. Each
student group produced a number of representations
of their findings and then the whole class with the
guidance of the teacher developed a class consensus.
The consensus consisted of graphical representations
(see Figure 6), diagrammatic representations (see
Figure 7) as well as verbal representations. An
example of a verbal representation is as follows:
As the days go by, the number of
paramecium increase at a greater rate. The
relationship is not linear so that when you
double the days the number does not double.
Different organisms have different growth
rates.
At this point the class has not determined a
mathematical representation. The mathematical
representation was developed using the data from
the two types of paramecium.
The unit then has the students deploy or test their
new model for population growth in multiple
contexts. This allows students to refine their model
further after they discover what happens when two
paramecia are living together in the same container.
Figure 5: Simulation's Graphical Output Page - students’
predictions almost match the observed graph.
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Figure 6: Sample of Graphical Representations of the
Model.
Figure 7: Sample Diagrammatic Representation.
3.4 Research Instruments
This study made use of the 24 item two-tiered
Lawson’s Classroom Test for Scientific Reasoning
(LCTSR) as a pre and posttest (Lawson, 1978, pp.
12-15). The LCTSR has been used in multiple
studies across a number of contexts (e.g., Ding et al,
2016, p. 620; Lawson, 2000, pp. 11-12). This
assessment can be used to not only assess overall
scientific reasoning but also to look at specific
scientific sub skills. These sub-skills consist of
control of variables, correlational reasoning,
proportional reasoning and hypothetico-deductive
reasoning. In addition, the results can be used to
determine the number of students at different
Piagetian reasoning stages (i.e., Formal reasoner,
Late Transitional Reasoner, Early Transitional
Reasoner and Concrete Reasoner). Piagetian
reasoning stages are based on student abilities to
apply deductive reasoning skills to abstract
hypothetical problems. Lawson’s test allows one to
identify learners as Level 0 (Piagetian formal
operational reasoners), Level 1 and 2 (Piagetian
transitional reasoner) or Level 3 (Piagetian formal
operational reasoner). Thus, based on the student
scores obtained students can be categorized into
separate reasoning levels. Figure 8 compares the
LCTSR to the Piagetian three levels of formal
reasoning.
The pretest was given within the first 2 weeks of
the school year and the post test was given during
the last month of the school year.
Figure 8: Comparison of LCTSR with Piagetian
Reasoning Levels (from Stammen et al, 2018, p. 3).
3.5 Data Analysis and Results
Since this study is focused on population ecology
only 20 of the 24 items on the LCTSR were
analysed. The 4 items not analysed focused on
conservation of mass and volume which were not
considered pertinent to this study.
3.5.1 Single-tiered Question Analysis
In order to determine overall differences between the
two cohorts a single-tiered analysis was completed
whereby all 20 items on the LCTSR were treated as
independent from one another.
Figure 9 shows the overall average pretest and
postest scores for the two cohorts. The t-test results
for a paired pretest to posttest comparison of
scientific reasoning scores were significant for the
treatment cohort (t (410) = 3.29, p < 0.001) but not
for the comparison cohort (t (280) = 1.52, p < 0.13).
The pretests of the treatment and comparison cohorts
(M = 37.23 and 34.5, respectively) were not
significantly different from each other (t (345) = 1.2,
p < 0.23). The post test scores between the treatment
and comparison cohorts (M = 43.25 and 37.34,
respectively) were significantly different (t (345) =
2.92, p < 0.004).
Figure 9: Single-tiered LCTSR Scores by Cohort.
The distribution of scientific reasoning subskills
on the pre and posttest by cohort can be seen in
Figures 10 and 11. Across all dimensions the
posttest sub-skills developed by the treatment cohort
Improving Students’ Performance through the Use of Simulations and Modeling: The Case of Population Growth
227
were larger than that of the comparison cohort. The
proportional reasoning subskill is a bit concerning
given that it is the lowest score of all the subskills
(see Figure 10). Given the simulations focus on the
effects of one variable upon others one might expect
this score to be much higher for the treatment
cohort.
Figure 10: Scientific Reasoning Subskill Scores (Control
of Variables, and Correlational and Proportional
Reasoning) by Cohort.
Figure 11: Scientific Reasoning Subskill Scores
(Hypothetic-deductive and Probabilistic Reasoning) by
Cohort.
3.5.2 Two-tiered Question Analysis
By using the two-tiered analysis (treating paired
items as a group), the LCTSR scores can be
categorized into Piagetian reasoning stages. In this
method the largest score is 13. Therefore, students
scoring from 11-13 are categorised as Formal
Operational Reasoners whereas those scoring 0-4
would be categorized as Concrete Reasoners. Figure
12a and b show the shift in number of students in
each reasoning stage per cohort. Figure 12
demonstrates that the treatment group showed a shift
Figure 12: Pre and Post Student Reasoning Levels by
Cohort.
towards more formal reasoners. Whereas, the
comparison group only shifted one student into this
category. In both cases there was not much change
from concrete reasoners to other categories.
However, the comparison cohort did not show much
shift between any categories.
4 DISCUSSION
The single-tier item analysis demonstrated that gains
in scientific reasoning were made by the treatment
group using the population ecology simulation and
modelling instruction between assessment
administrations. However, the comparison cohort
did not make any significant gains between
administrations of the assessment. Therefore, the use
of population growth spreadsheet simulations in
conjunction with Modelling Instruction in the
context of population ecology produced a shift in
scientific reasoning skills.
The subskill scores demonstrated that the
curriculum units used by the treatment cohort
showed post assessment reasoning abilities that
exceeded that of the comparison cohort. The two
subskills showing the lowest values between cohorts
was that of proportional reasoning and control of
variables. This demonstrates that both groups need
to be exposed to activities that allow them to master
these subskills to a greater extent.
The two-tiered analysis demonstrated that the use
of a simulation in conjunction with Modelling
Instruction demonstrated the ability to assist over
6% of the students into becoming formal reasoners.
Whereas, the comparison group did not demonstrate
any major shift between reasoning levels. However,
even though the treatment cohort showed much
more positive results in terms of reasoning levels pre
to post assessment there was still very little shift in
the total number of concrete reasoners.
5 CONCLUSIONS AND FUTURE
DIRECTIONS
Overall, the study demonstrated that the use of
simulations in conjunction with Modelling
Instruction pedagogy demonstrates positive results
in terms of scientific reasoning gains versus that of
the comparison cohort. In addition, the study
demonstrated the ability of the materials to allow for
student shifts in Piagetian reasoning levels towards
more formal reasoners. Therefore, students using
CSEDU 2019 - 11th International Conference on Computer Supported Education
228
these materials should be better prepared for
advanced science study.
However, the results also demonstrated that the
materials need to be improved in order to allow for a
more authentic ability to practice control of variables
and to develop proportional reasoning skills. In
addition, differences in the simulation use between
high and low ability students should be studied in
order to develop better simulation scaffolds. Better
simulation scaffolds could allow all students show
similar gains in reasoning levels across classrooms
that contain students of varying abilities.
In addition, this study did not include cohorts
that used just the simulation or Modelling
Instruction materials without population ecology
simulations in order to tease apart the effects of the
two in terms of scientific reasoning skills. Future
studies should also include an analysis of conceptual
gains as well as that of scientific reasoning.
ACKNOWLEDGEMENTS
This research was partially funded by a grant under
the federally funded Math Science Partnership State
Grants Program, under Grant number OH160505
and OH160511. Any opinions, findings, and
conclusions or recommendations expressed in this
material are those of the authors and do not
necessarily reflect the views of the funding
organizations.
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