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Automated Short Answer Scoring Using an Ensemble of Neural Networks and Latent Semantic Analysis Classifiers

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Title: Automated Short Answer Scoring Using an Ensemble of Neural Networks and Latent Semantic Analysis Classifiers
Language: English
Authors: Ormerod, Christopher (ORCID 0000-0002-2129-7021), Lottridge, Susan, Harris, Amy E., Patel, Milan, van Wamelen, Paul, Kodeswaran, Balaji, Woolf, Sharon, Young, Mackenzie
Source: International Journal of Artificial Intelligence in Education. Sep 2023 33(3):467-496.
Availability: 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/
Peer Reviewed: Y
Page Count: 30
Publication Date: 2023
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Computer Assisted Testing, Scoring, Artificial Intelligence, Semantics, Classification, Performance, Scoring Rubrics, Sex, Ethnicity, Language Proficiency, Disabilities, Economically Disadvantaged
DOI: 10.1007/s40593-022-00294-2
ISSN: 1560-4292
1560-4306
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.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1388572
Database: ERIC
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  Data: 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/
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  Data: 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.
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