CS-BKT: Introducing Item Relationship to the Bayesian Knowledge Tracing Model

Autor/inn/en	Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu
Titel	CS-BKT: Introducing Item Relationship to the Bayesian Knowledge Tracing Model
Quelle	In: Interactive Learning Environments, 29 (2021) 8, S.1393-1403 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2019.1629600
Schlagwörter	Bayesian Statistics; Intelligent Tutoring Systems; Student Evaluation; Knowledge Level; Mastery Learning; Models; Prediction; Accuracy; Markov Processes; Probability; Scores; Cognitive Measurement + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Schulnote; Studentische Bewertung; Wissensbasis; Analogiemodell; Vorhersage; Markowscher Prozess; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when he masters knowledge A. Therefore, this work introduces a new student model based on BKT. It takes the relationship between knowledge into account. By doing this, the new model proves higher prediction accuracy and performs better. Then this paper uses the new model to make a cognitive diagnosis according to students' test scores. The diagnostic results can help teachers provide personalized guidance to students and improve teaching efficiency. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01