Code-DKT: A Code-Based Knowledge Tracing Model for Programming Tasks [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W.
Titel	Code-DKT: A Code-Based Knowledge Tracing Model for Programming Tasks [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Programming; Knowledge Level; Prediction; Instructional Innovation; Comparative Analysis; Introductory Courses; Computer Science Education; Case Studies; Assignments; Models; Bayesian Statistics; Evaluation Methods; Learning Processes; Programming Languages; Undergraduate Students + Suchen Sie Ihr Suchwort? Programmierung; Wissensbasis; Vorhersage; Educational Innovation; Bildungsinnovation; Einführungskurs; Computer science lessons; Informatikunterricht; Case study; Fallstudie; Case Study; Assignment; Auftrag; Zuweisung; Analogiemodell; Learning process; Lernprozess
Abstract	Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In this work, we propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 introductory programming assignments. Our results show that Code-DKT consistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, a comparable improvement to other state-of-the-art domain-general KT models over DKT. Finally, we analyze problem-specific performance through a set of case studies for one assignment to demonstrate when and how code features improve Code-DKT's predictions. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01