This week’s readings
dealt with the concept of modelling in science education.

In the paper by
Hestenes et al., modeling instruction is touted as the most effective method of
science instruction. The authors emphasize that modeling differs from
traditional lecture-demonstration method by producing students that can think
in depth about problems instead of regurgitating what they learned from text.
The authors divide modeling into two components: model development and model
deployment. In the development stage, students collaborate in planning and
conducting experiments, coming up with ways to answer a particular question and
formulating a model to explain a phenomenon. In the deployment stage, students
apply their model to new situations to deepen their understanding. The authors
provides a really nice example of the modelling cycle showing what each sub
steps entail. One important message of the paper is that in model-based
learning, teachers’ role is more akin to that of a coach/facilitator, rather
than an instructor. The main purpose to help students formulate their own
understanding, and constant feedback between the teachers and the students
should help the latter to come to terms with the experiments themselves.

In Lehrer’s chapter 2,
the authors go into detail about what is a model, which is actually a
challenging concept for educators. The authors emphasize that the role of models
in science education is not as “illustrative devices” to explain scientific
concepts, but rather as a template to build a scientific theory. An interesting
definition of modeling is that it is a way of constructing the “conditions of
seeing” a scientific phenomenon. It is a simplified analog of the real world
that is conducive to form scientific arguments. Because now modeling is a tool of learning something
novel rather than a tool to represent a known scientific phenomenon, it is okay
then for students to experience results that differ from their model, and then
accordingly make adjustments or incorporate additional elements to their original
model. In effect it becomes an inquiry-based tool, where students come up with
questions to ask, and ways to answer those questions. As stated previously in
the Hestenes paper, the teacher would turn the students’ attention towards
things that are not immediately evident. What is also interesting is that the
authors go into detail about the tendency for some students to “miss” the point
of modelling and not making connections to the real world. This “representational
challenge” is a reminder to “novice thinking” where superficial learning permeates
and that students do not know when to leave extraneous information out. It is
therefore again up to the teachers to help students making the logical
connection between modeling and what they are trying to get out of the
exercise.

In chapter 5, the
authors expound the concept of modeling by assessing the role of experiments. The
authors explain that experiment must exist only as a component of modeling to
understand a bigger concept, rather than just something that students do to
replicate a known result. The authors recount a telling example where students
finished a rock erosion experiment but completely missed the point of the
exercise, even having spent an entire unit on it. Therefore it is important for
students to realize that experiments serve to model real world phenomenon. It
would be a waste of time and effort when there is a disconnect between the two.
To reduce the chance of disconnect, the authors introduce the concept of “scaffolding,”
where teachers generate a set of inquiries on design features that would
explicitly help students understand the point of experiments. In essence,
experiments are done only in the context of understanding a bigger concept, as
tools to define question, collect data, and make sense of their relationship.

All three papers demonstrate
the importance of modeling in science education. I believe given enough time
and effort, modeling would be an extremely useful tool in raising student
science standards. However, many of the examples, such as fruit ripening and
rocket construction, seem to stretch over a long period of time, whereas
nowadays oftentimes a topic is covered in the span of one week, with multiple
topics to follow. In addition, the authors of chapter 2 point out that some
fundamental math skills are required for students to make sense of modeling and
to draw conclusions. I would like to know how to apply modeling in lower
performing schools that are racing to cover every topic mandated by the state.
Perhaps modeling in smaller chunks?

I like the reference to last week's 'chunking' research and connecting it to this modeling appraoch. It is a pretty unfair condition that we must navigate, balancing the need to fit curriculum requirements while providing an adequate modeling activities. I also wonder what the most effective institution of modeling activities to young students in this situation is--there must be some greater holistic approach to be taken to the sequentiality of science topics that can allow us to model despite the time constraints or other limitations.

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