ChemSense has been designed and improved through a series of
baseline and design studies. A variety of qualitative and quantitative
methods have been used to evaluate its impact, including pre-
and post- student interviews, video analysis, and scoring of
pre-tests, post-tests, retention tests, and representations
created by students. We summarize some of our past findings
below. See our papers for
Working with high school students
using our Solubility curriculum module, we found that students
who created more drawings and animations in ChemSense over a
three-week period showed greater representational competence
(ability to create and analyze representations) and deeper understanding
of geometry-related aspects of chemical phenomena in their animations.
Specifically, we found a significant, positive correlation between
the number of drawings and animations created in ChemSense and
the quality of the animations produced, as scored by raters
using our chemical geometry and representational competence
rubrics (p<.05). Students using ChemSense also showed significant
improvement in representational competence and in their understanding
of connectivity and geometry from pre- to post-test (p<.05).
These findings suggest that the use of ChemSense as a representation
"creation" tool facilitates representational ability and chemical
understanding of underlying, nanoscopic mechanisms.
Analysis of videotapes of two high school groups working in the ChemSense environment shows
that use of the tools requires students to think carefully through more specific aspects of
chemical phenomena to which they might not otherwise attend, such as the number of molecules
involved in a reaction, the particular bonds created in the reaction, the bond angles, or
the sequence of steps in a reaction. Throughout the collaborative sessions we videotaped,
students use the representations to both develop and reveal their understandings of chemical phenomena.
Using the ChemSense tool, high school students who started out with the most limited
representational competence demonstrated the greatest improvement in representational
competence over time. Specifically, we found a significant, negative correlation between
pre-test scores and gain (post-test minus pre-test) scores (p<.05). Since the biggest
gain in representational ability was by those students who started with minimal representational
ability, ChemSense may be an effective way to level the playing field between students by providing
all students, regardless of their initial representational competence or attunements, with an
effective way to generate and communicate chemical ideas.
In another study, University of
Michigan undergraduate chemistry students worked with ChemSense
tools in representing multi-step, organic chemical reactions.
Our preliminary quantitative findings show a positive correlation
between the use of ChemSense and deeper chemical understanding.
Our video analysis revealed that in the process of planning
(storyboarding) animations, students were speaking with each
other about the stages of reactions in a more detailed way than
they might normally precisely because they needed to consider
a greater level of detail. In other words, students arrived
at a shared understanding of the chemical content through their
planning and discussion of animations. These promising findings
suggest that further, extended investigation is needed to fully
understand the extent to which the ChemSense software promotes
understanding of college level curriculum.