Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor

Autor/inn/en	Rivers, Kelly; Koedinger, Kenneth R.
Titel	Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor
Quelle	In: International Journal of Artificial Intelligence in Education, 27 (2017) 1, S.37-64 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rivers, Kelly)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-015-0070-z
Schlagwörter	Programming; Coding; Computers; Data; Decision Making; Intelligent Tutoring Systems; Programmed Tutoring + Suchen Sie Ihr Suchwort? Programmierung; Codierung; Digitalrechner; Daten; Decision-making; Entscheidungsfindung; Intelligentes Tutorsystem; Programmiertes Lernen
Abstract	To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not occurred in the data before. We provide a detailed description of the system's implementation and perform a technical evaluation on a small set of data to determine the effectiveness of the component algorithms and ITAP's potential for self-improvement. The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01