Literaturnachweis - Detailanzeige
| Autor/inn/en | Ormerod, Christopher; Lottridge, Susan; Harris, Amy E.; Patel, Milan; van Wamelen, Paul; Kodeswaran, Balaji; Woolf, Sharon; Young, Mackenzie |
|---|---|
| Titel | Automated Short Answer Scoring Using an Ensemble of Neural Networks and Latent Semantic Analysis Classifiers |
| Quelle | In: International Journal of Artificial Intelligence in Education, 33 (2023) 3, S.467-496 (30 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Ormerod, Christopher) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1560-4292 |
| DOI | 10.1007/s40593-022-00294-2 |
| Schlagwörter | Computer Assisted Testing; Scoring; Artificial Intelligence; Semantics; Classification; Performance; Scoring Rubrics; Sex; Ethnicity; Language Proficiency; Disabilities; Economically Disadvantaged |
| Abstract | We introduce a short answer scoring engine made up of an ensemble of deep neural networks and a Latent Semantic Analysis-based model to score short constructed responses for a large suite of questions from a national assessment program. We evaluate the performance of the engine and show that the engine achieves above-human-level performance on a large set of items. Items are scored using 2-point and 3-point holistic rubrics. We outline the items, data, handscoring methods, engine, and results. We also provide an overview of performance key student groups including: gender, ethnicity, English language proficiency, disability status, and economically disadvantaged status. (As Provided). |
| Anmerkungen | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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