Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Autor/inn/en	Kochmar, Ekaterina; Vu, Dung Do; Belfer, Robert; Gupta, Varun; Serban, Iulian Vlad; Pineau, Joelle
Titel	Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems
Quelle	In: International Journal of Artificial Intelligence in Education, 32 (2022) 2, S.323-349 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kochmar, Ekaterina)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-021-00267-x
Schlagwörter	Intelligent Tutoring Systems; Automation; Feedback (Response); Dialogs (Language); Natural Language Processing; Intervention; Problem Solving; Data; Academic Achievement + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Dialog; Dialogs; Dialogue; Dialogues; Natürliche Sprache; Problemlösen; Daten; Schulleistung
Abstract	Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using "hints" and "Wikipedia-based explanations." Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in Korbit, a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback. (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