When Does Scaffolding Provide Too Much Assistance? A Code-Tracing Tutor Investigation

Autor/inn/en	Jennings, Jay; Muldner, Kasia
Titel	When Does Scaffolding Provide Too Much Assistance? A Code-Tracing Tutor Investigation
Quelle	In: International Journal of Artificial Intelligence in Education, 31 (2021) 4, S.784-819 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-020-00217-z
Schlagwörter	Scaffolding (Teaching Technique); Tutors; Tutoring; Programming; Troubleshooting; Problem Solving; Individual Differences; Individualized Instruction; Coding + Suchen Sie Ihr Suchwort? Förderlehrer; Lehrender; Tutor; Förderkonzept; Nachhilfeunterricht; Programmierung; Störfall; Problemlösen; Individueller Unterschied; Individualisierender Unterricht; Codierung
Abstract	When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, as evidenced by prior work and our own experiences in the classroom. To address this, we designed a "Code Tracing" (CT)-Tutor. We varied the level of assistance provided in the tutor, based on (1) the interface scaffolding available during code tracing, and (2) instructional order, operationalized by when examples were provided, either before or after the corresponding problem was solved. We collected data by having participants use the tutor to solve code tracing problems (N = 97) and analyzed both learning outcomes and process data obtained by extracting features of interest from the log files. We used a multi-layered approach for the analysis, including standard inferential statistics and unsupervised learning to cluster students by their behaviors in the tutor. The results show that the optimal level of assistance for code tracing falls in the middle of the assistance spectrum included in the tutor, but also that there are individual differences in terms of optimal assistance for subgroups of individuals. Based on these results, we outline opportunities for future work around personalizing instruction for code tracing. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01