Khine M.S., Saleh I.M. Models and Modeling: Cognitive Tools for Scientific Enquiry
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9 Engaging Elementary Students in Scientific Modeling 211
some variation in the extent of increase depending on the context and wording of
each item. We note, however, that the pattern of increasingly using invisible objects
and their movement was not found in responses to pre–post assessment items about
model revision and use. This is likely due to the limitations of the assessment items
that asked students about their general reflections about model revision rather than
their own practice.
Analysis of the interview data indicates that four out of six students utilized
invisible particles and their movement in using their models of evaporation and
condensation to explain or predict related phenomena. For example, this interview
excerpt shows how a student used these ideas in his reasoning.
Interviewer Could you use your final model, either [of] the condensation or evap-
oration, to predict other phenomena? For example, a mirror fogging
up when you’re taking a hot shower?
Student Yeah, because the water is hot so the condensation—the mirror would
be colder than the water and the water would evaporate because warm
water evaporates faster than cold water. So then the water molecules
would spread out into the bathroom and usually you have the door
closed so it wouldn’t be able to get out so then it would just attach
themselves to the mirror, which is colder.
The presence of “water molecules” and their “spreading out” and then “attaching
themselves” allowed this student to explain the process and outcome of this related
phenomenon. Analysis of students’ models in their workbooks enabled us to trace
the extent to which students included invisible objects in their models over time
during the unit implementation. The result is shown in Fig. 9.2b. Outcomes indicate
that for each topic (evaporation and then condensation) more students included this
explanatory feature in t heir revised models. The results also indicate that when the
topic shifted from evaporation to condensation, some students brought these ideas
forward with them to the new context while the others did not.
What influenced students to increase this feature in their models? Analysis of
student workbooks provides some indication. Figure 9.2b illustrates the change
between students’ initial and second models of evaporation (model 1 and model 2)
as well as the change between their initial and second models of condensation
(model 4 and model 5). In both of these contexts, students largely made their own
decisions about what to include in these models unlike in teacher-guided consensus
models (model 3 and model 6). Between their initial and second models, students
engaged in empirical investigations and experienced computer simulations. Given
that empirical investigations did not directly provide this feature, one can argue that
many of these changes came from students’ experience with the computer simu-
lations. Figure 9.3 is an illustrative example of the change between one student’s
model 1 and model 2. Note that this student explained evaporation more effectively
by utilizing invisible particles in his second model of evaporation. Additionally,
students explicitly pointed out in their interviews that computer simulations had
impacted their models. Two students’ responses below illustrate this point.
212 H. Baek et al.
Fig. 9.3 A student’s initial and second models of evaporation
Interviewer ... [Were the computer simulations] helpful for you?
Student 1 Yeah.
Interviewer How?
Student 1 Because of the movement, like if there was a gas they would go fast
and like...the molecules would do that. If it was a liquid it would
be slow and it would spread apart. And a solid they would be stuck
together and going slow in different directions.
Interviewer Did you think those simulations were helpful? Did you learn anything
from them?
Student 2 Yeah, that there were molecules everywhere in the air. I just thought
it was just air.
In summary, elementary students’ increasing inclusion of invisible objects or,
more specifically, invisible particles and their movement in modeling indicates the
potential impact of the computer simulations in the MIS. Although there are many
explanatory components of computer simulations, these features enabled students
to appreciate and access the scientific explanation. In other words, students began to
see the utility and power of explaining macroscopic phenomena using the theoreti-
cally constructed entities of invisible objects and their movement, rather than telling
a narrative that involves objects and processes observable in everyday life.
The Influence of the Social Interactions in MIS: Students’
Attention to Audience and Communicative Features of Models
Finally, we were interested in finding out how the social interactions included in
MIS affected students’ modeling practices. Analysis of the pre- and posttest data,
9 Engaging Elementary Students in Scientific Modeling 213
students’ models, and the interview data shows some evidence that students increas-
ingly considered audience and communicative features of models in their modeling
practices.
Analysis of the written assessment suggests that students generally became more
attentive to communicative features of models such as labels in model construction
and evaluation (Table 9.4C). Analysis of data from three items asking respondents
to construct a model indicates that students increasingly included labels in their
later models to make them easier to understand from pretest to posttest. Figure 9.4
illustrates this change. Note that this student labeled “water,” “water in the air,”
and “evaporation” distinctively in the posttest whereas he did not include any labels
in the pretest. Analysis of four items about model evaluation also shows that stu-
dents increasingly attended to the extent to which models are easy for others to
understand. While the manner in which each student attended to this communicative
aspect varied—including labeling, detailed description, and symbols—they all rec-
ognized that models should communicate what they contain effectively to audience.
The following excerpts illustrate some typical responses.
– “A strength in model B is that is very detailed because detail usually can help you
understand better.” (item 7 in the pretest)
– “Limitation: confusing. No words to explain anything. And the striped pattern
doesn’t help either; Strengths = labeling.” (item 7 in the posttest)
– “The strengths are shows water bits going up with arrows shows water on the lid
and in the container. Limitations is [sic] cannot tell necessary if arrows are water
bits.” (item 5 in the posttest)
The written assessment analysis indicates that few students displayed attention to
communicative features in their response to the items of model revision and model
use. The two items about model revision involving scientific and scientists’ models
did not indicate that students considered communicative effectiveness in revising
models possibly because students seemed to think that scientific and scientists’ mod-
els already hold enough communicative clarity. Further, the items of model use did
not offer a context that required students to think of communicative features.
Analysis of student models in their workbooks produced the same pattern as
the findings from the pre–post assessment analysis. Figure 9.2c shows that students
Fig. 9.4 A student’s model in pre- and posttest (item 2)
214 H. Baek et al.
increasingly included labels when they constructed and revised models. Note that
a considerable number (26 out of 34 or 76%) of students transferred this feature
as they entered a new topic (condensation). This evidence indicates that students
became more aware of audience as they went through the unit.
The result of the interview analysis supports this analysis. Five out of six noted
the communicative aspects of models for multiple questions about model evaluation.
For example, one student mentioned helping others’ better understand his model as
one rationale for his model revision.
Interviewer Okay. Is there any other reason for you to make the change?
Student Yeah, because it explains more. I didn’t really have any labels on this
one (initial model of evaporation). So if this was in a museum, people
who saw it wouldn’t really understand it because it didn’t have any
labels or explain what happened to the water.
These results suggest that students increasingly attended to audience and the
communicative features of models in constructing, revising, and evaluating mod-
els. Such evidence suggests the potential impact of the social interactions in MIS
on student modeling. At the same time, we notice that few students adopted more
advanced social features such as evidence-based argumentation from their experi-
ences of peer evaluation and consensus model construction in MIS. Most s tudents
instead focused on basic communicative features of models such as labeling perhaps
because this evaluation criterion was both accessible and acceptable within students’
social norms. The results reported here may indicate a potentially important first step
toward more sophisticated modeling practices.
Discussion and Concluding Remarks
In conclusion, the MIS seemed to have impacted students’ modeling practices in
several ways: First, empirical investigations allowed students to note the impor-
tance of empirical evidence and implicitly use this evidence in scientific modeling,
although very few students explicitly illustrated evidence in their models. Second,
the computer simulations enabled students to increasingly use invisible objects
(particles and their movement) as an explanatory feature in their models. Lastly,
students’ engagement in social interactions such as peer evaluation and con-
sensus model construction helped them become more aware of audience and
communicative features of their models.
What do these findings indicate about approaches that can support rich model-
ing practices and the effectiveness of MIS? The success of the MIS-embedded unit
depends on the objectives for students’ modeling practices. On the one hand, few
elementary students came to note and utilize the sophisticated epistemic relationship
between empirical evidence and models, the power of mechanism as an explanatory
feature, or the value of criteria-based peer evaluation and argumentation as a result
of the implementation of the model-centered unit. On the other hand, the evidence
9 Engaging Elementary Students in Scientific Modeling 215
indicates that the various features of MIS within this 6-week unit may have sup-
ported students in moving in that direction. These successes and opportunities for
improvement provide ideas for further development and implementation of MIS in
our design experiment work.
One aspect to consider further is how to most effectively weave together mod-
eling practices and reflective MMK activities in MIS. The balance and timing
of the practices and the discussion of reflective knowledge are critical. Engaging
students in modeling practices without discussing the nature and purpose of mod-
els and modeling in which they are engaged might lead to acquisition of skills
in a mechanistic sense and hinder students from generalizing and transferring
their practices and knowledge to other contexts. On the other hand, incorporating
multiple MMK discussion at the beginning of the unit can sometimes be over-
whelming and abstract—appearing as additional information to passively learn.
Some evidence from this research as well as those of others (Buckingham &
Reiser, 2010; Kenyon, Cotterman, Todd, Reese, & Reese, 2010) indicates that
students need to engage in modeling practices in multiple contexts to develop
MMK. At the same time, we also have seen the value of relatively brief but rel-
evant MMK discussion guided by teachers before, during, and after each MIS
feature (e.g., empirical investigations). We theorize that a sophisticated inte-
gration of modeling practices and MMK activities in MIS over an extended
period of time will likely promote students’ meaningful engagement in scientific
modeling.
A second aspect for further consideration is how to develop the social dimen-
sions of MIS to make them more accessible for students. Our finding that students
began to attend to basic communicative features of models without attending to
criteria-based peer evaluation and argumentation leads the authors to think about
the challenging nature of social dimension of scientific modeling. As previous
research has shown (Berland & Reiser, 2009; Jiménez-Aleixandre et al., 2000),
engaging students in scholarly social interactions can be difficult. This is an area in
which students’ everyday social norms take a strong hold, and shifts to new social
norms can be challenging for students and teachers alike. Our work indicates that
students need additional support for taking on intellectual roles as scientists to pro-
duce significant change. Prior work (e.g., Herrenkohl & Guerra, 1998) shows that
retaining a participant structure that bears the characteristics of scientists’ social
interactions throughout the unit or across multiple contexts could make this aspect
more accessible for students. In addition, we hypothesize that students’ participa-
tion in constructing and applying evaluation criteria is critical for their effective
use. In conjunction, students may use the criteria more effectively when scientific
discourse is made explicit and is distinguished from daily discourse. An every-
day meaning of evidence could be compared to the scientific meaning of evidence
for helping students better understand why empirical evidence is an important
evaluation criterion.
Finally, we note the important role of the teacher in mediating the implementa-
tion of MIS-embedded units and the need to explore this dimension further. Because
modeling practice is a sociocultural phenomenon, the teachers played an important
216 H. Baek et al.
role as “culture brokers” (Aikenhead, 1996) or agents who know both scientific
Discourse and student Discourse. The teachers in our work contributed and signif-
icantly impacted students’ uptake of scientific modeling. In particular, we found
that the teachers linked students’ and scientific Discourses by utilizing a hybrid or
blended instructional practice that contains both elements of school science prac-
tice and elements of authentic scientific inquiry (Schwarz, 2009). Incorporating an
understanding of this hybridization into MIS and the professional work around its
development and implementation will likely promote the impact of MIS on students’
modeling.
In this chapter we presented our approach to engaging elementary students in
scientific modeling. Analysis of some of the empirical outcomes from using the
MIS approach indicates that empirical investigations, computer simulations, and
social interactions in MIS assisted students in developing an emergent sense of
important features of models and modeling and in moving toward more sophisti-
cated modeling practices. Overall, our analysis indicates that MIS has potential as a
pedagogical approach for promoting students’ modeling practice and related knowl-
edge. As an instructional tool, MIS includes extensive features that are important
in mediating the students’ Discourse and the scientific Discourse. Such features
should be combined with additional work to determine the nature and balance
of modeling practices and MMK, the support of social dimensions in the prac-
tice, and the attendance to the teacher’s role as a culture broker in the practice.
Further study and refinement of MIS along these lines may continue to help students
engage and progress in this important scientific practice and gain proficient scientific
literacy.
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Part III
Modeling and Teachers’ Knowledge
