A Novel Automated Essay Scoring Approach for Reliable Higher Educational Assessments

Autor/inn/en	Beseiso, Majdi; Alzubi, Omar A.; Rashaideh, Hasan
Titel	A Novel Automated Essay Scoring Approach for Reliable Higher Educational Assessments
Quelle	In: Journal of Computing in Higher Education, 33 (2021) 3, S.727-746 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Beseiso, Majdi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1042-1726
DOI	10.1007/s12528-021-09283-1
Schlagwörter	Automation; Scoring; Essays; Writing Tests; Higher Education; Artificial Intelligence; Models; Test Reliability + Suchen Sie Ihr Suchwort? Bewertung; Essay; Aufsatzunterricht; Writing test; Schreibtest; Hochschulbildung; Hochschulsystem; Hochschulwesen; Künstliche Intelligenz; Analogiemodell; Testreliabilität
Abstract	E-learning is gradually gaining prominence in higher education, with universities enlarging provision and more students getting enrolled. The effectiveness of automated essay scoring (AES) is thus holding a strong appeal to universities for managing an increasing learning interest and reducing costs associated with human raters. The growth in e-learning systems in the higher education system and the demand for consistent writing assessments has spurred research interest in improving the accuracy of AES systems. This paper presents a transformer-based neural network model for improved AES performance using Bi-LSTM and RoBERTa language model based on Kaggle's ASAP dataset. The proposed model uses Bi-LSTM model over pre-trained RoBERTa language model to address the coherency issue in essays that is ignored by traditional essay scoring methods, including traditional NLP pipelines, deep learning-based methods, a mixture of both. The comparison of the experimental results on essay scoring with human raters concludes that the proposed model outperforms the existing methods in essay scoring in terms of QWK score. The comparative analysis of results demonstrates the applicability of the proposed model in automated essay scoring at higher education level. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01