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 |
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| Language: | English |
| Authors: | Ormerod, Christopher (ORCID |
| 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 |
| 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. |
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| ISSN: | 1560-4292 1560-4306 |
| DOI: | 10.1007/s40593-022-00294-2 |
