The Implementation of a Spartan Model’s Effect on Students’ Overall Understanding of Esterification Reactions
Florida Southern College
Organic Chemistry students often struggle with using information provided to them to extend to new situations. Inquiry-based labs and assignments have shown to improve students’ ability to extend their knowledge to new situations. For example, rather than confirming what students already know, inquiry-based labs can help students internalize the concepts. This may help students, for example, rationalizing how substituents may impact a reaction. Students at Florida Southern College have an inquiry-based lab on greener esterification that explores the effect of substituents on greener esterification reactions, which requires students understand and apply many different conceptual phenomenon. Though students can recognize there is a pattern in the data, they struggle with justifying their observations. An activity using Spartan, a computational software that produces calculated visualizations and numerical values for molecules and reactions, was introduced prior to the students completing the inquiry-based lab and writing their lab report. Its effect on students' understanding of the lab was then determined through comparing students’ lab reports from this year to students’ lab reports from previous semesters who did not complete the model-based activity. Students’ learning progression throughout Organic Chemistry I and II was followed to determine the effect of the model-based activity on students’ understanding. Students’ foundational understanding of electron density was determined at the beginning Organic Chemistry I and followed as students learned about electrophilicity and the reactivity of benzene ring derivatives via questions on in class quizzes. From the analysis of the open-ended questions on the in-class quizzes emerging misconceptions were also determined.
Honors Thesis Spring 2021
Esterification, Chemistry, Organic, Information behavior, Information retrieval, Inquiry-based learning