Using Active Learning Methods to Strategically Select Essays for Automated Scoring

Autor/inn/en	Firoozi, Tahereh; Mohammadi, Hamid; Gierl, Mark J.
Titel	Using Active Learning Methods to Strategically Select Essays for Automated Scoring
Quelle	In: Educational Measurement: Issues and Practice, 42 (2023) 1, S.34-43 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/emip.12537
Schlagwörter	Active Learning; Automation; Scoring; Essays; Writing (Composition); Electronic Learning; Teaching Methods + Suchen Sie Ihr Suchwort? Aktives Lernen; Bewertung; Essay; Aufsatzunterricht; Schreibübung; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	Research on Automated Essay Scoring has become increasing important because it serves as a method for evaluating students' written responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods that can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern Automated Essay Scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included in the Automated Student Assessment Prize competition that were then classified using a scoring model that was trained with the bidirectional encoder representations from a transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01