Implementation of an R Shiny App for Instructors: An Automated Text Analysis Formative Assessment Tool for Evaluating Lewis Acid–Base Model Use (Journal Article)

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Abstract

Acid–base chemistry, and in particular the Lewis acid–base model, is foundational to understanding mechanistic ideas. This is due to the similarity in language chemists use to describe Lewis acid–base reactions and nucleophile–electrophile interactions. The development of artificial intelligence and machine learning technologies has led to the creation of predictive text analysis models that evaluate a large number of open-ended, written formative assessment items. One of these machine learning-based tools developed by the authors evaluates correct Lewis acid–base model use. Bridging the gap between educational research, technological innovation, and instructional practice, we report the development of a web-based, interactive app using R Shiny application technologies that automates scoring of written assessments about acid–base chemistry. Results given by this Shiny app, in the form of on-screen output or a downloadable file, provide instructors with immediate feedback to evaluate acid–base instruction in their organic chemistry courses.