Leveraging Natural Language Processing to Support Automated Assessment and Feedback for Student Open Responses in Mathematics

Autor/inn/en	Botelho, Anthony; Baral, Sami; Erickson, John A.; Benachamardi, Priyanka; Heffernan, Neil T.
Titel	Leveraging Natural Language Processing to Support Automated Assessment and Feedback for Student Open Responses in Mathematics
Quelle	In: Journal of Computer Assisted Learning, 39 (2023) 3, S.823-840 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Baral, Sami) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12793
Schlagwörter	Natural Language Processing; Artificial Intelligence; Computer Assisted Testing; Mathematics Tests; Student Evaluation; Test Format; Feedback (Response); Mathematics Achievement; Prediction; Mathematics Education + Suchen Sie Ihr Suchwort? Natürliche Sprache; Künstliche Intelligenz; Schulnote; Studentische Bewertung; Testentwicklung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Vorhersage; Mathematische Bildung
Abstract	Background: Teachers often rely on the use of open-ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close-ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time-consuming, to evaluate and provide directed feedback. It is a well-studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher-authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open-ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence-level semantic representations of student responses to open-ended questions, we propose a collaborative filtering-based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. (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