000			02078nam a22002537a 4500
005			20231116121759.0
008			231020b ||||| |||| 00| 0 eng d
022			_a0021-9584
037			_bRIEBPL Library
082			_a540.7
100			_aBrandon J. Yik, David G. Schreurs, and Jeffrey R. Raker
245			_a Implementation of an R Shiny App for Instructors: An Automated Text Analysis Formative Assessment Tool for Evaluating Lewis Acid–Base Model Use _b (Journal Article)
260			_aUSA _b:American Chemical Society _cAugust 2023
300			_a3107–3113 P.
490			_aAmerican Chemical Society, Volume 100, Issue 8
505			_a***______{For Hard Copy, Please visit Library.}________***
520			_aAbstract 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.
650			_aSecond-Year Undergraduate
650			_aUpper-Division Undergraduate
650			_aOrganic Chemistry Acid−Base
650			_aTheories Mechanisms of Reactions
856			_uhttps://doi.org/10.1021/acs.jchemed.3c00400
942			_cPER
999			_c44894 _d44893