NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1310382
Record Type: Journal
Publication Date: 2021-Oct
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-1756-1108
EISSN: N/A
Development of a Machine Learning-Based Tool to Evaluate Correct Lewis Acid-Base Model Use in Written Responses to Open-Ended Formative Assessment Items
Yik, Brandon J.; Dood, Amber J.; Cruz-Ramirez de Arellano, Daniel; Fields, Kimberly B.; Raker, Jeffrey R.
Chemistry Education Research and Practice, v22 n4 p866-885 Oct 2021
Acid-base chemistry is a key reaction motif taught in postsecondary organic chemistry courses. More specifically, concepts from the Lewis acid-base model are broadly applicable to understanding mechanistic ideas such as electron density, nucleophilicity, and electrophilicity; thus, the Lewis model is fundamental to explaining an array of reaction mechanisms taught in organic chemistry. Herein, we report the development of a generalized predictive model using machine learning techniques to assess students' written responses for the correct use of the Lewis acid-base model for a variety (N = 26) of open-ended formative assessment items. These items follow a general framework of prompts that ask: why a compound can act as (i) an acid, (ii) a base, or (iii) both an acid and a base (i.e., amphoteric)? Or, what is happening and why for aqueous proton-transfer reactions and reactions that can only be explained using the Lewis model. Our predictive scoring model was constructed from a large collection of responses (N = 8520) using a machine learning technique, i.e., support vector machine, and subsequently evaluated using a variety of validation procedures resulting in overall 84.5-88.9% accuracies. The predictive model underwent further scrutiny with a set of responses (N = 2162) from different prompts not used in model construction along with a new prompt type: non-aqueous proton-transfer reactions. Model validation with these data achieved 92.7% accuracy. Our results suggest that machine learning techniques can be used to construct generalized predictive models for the evaluation of acid-base reaction mechanisms and their properties. Links to open-access files are provided that allow instructors to conduct their own analyses on written, open-ended formative assessment items to evaluate correct Lewis model use.
Royal Society of Chemistry. Thomas Graham House, Science Park, Milton Road, Cambridge, CB4 0WF, UK. Tel: +44-1223 420066; Fax: +44-1223 423623; e-mail: cerp@rsc.org; Web site: http://www.rsc.org/cerp
Publication Type: Journal Articles; Reports - Research; Tests/Questionnaires
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Florida
Grant or Contract Numbers: N/A