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Research
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 more information.

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.
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