Date of Award

Summer 7-11-2019

Track

Chemistry

Degree Type

Thesis

Degree Name

Master of Science in Chemical Sciences (MSCB)

Department

Chemistry

Committee Chair

Kimberly Cortes

Committee Member

Adriane Randolph

Committee Member

Thomas Leeper

Abstract

Understanding how students learn and process information is critical to developing physical modeling activities that facilitate student learning by decreasing cognitive load in the working memory. Optimizing cognitive load during physical modeling activities in organic chemistry is the key to effective and efficient learning. Using EEG (electroencephalogram) and eye tracking technologies, researchers measured and recorded the cognitive processing of participants while they completed a chiral physical modeling activity. Analysis of the data using the Engagement Index developed by Pope et al provided information necessary to develop curriculum that does not undermine student learning due to excessive cognitive load.

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