Personalized Recommendation in the Adaptive Learning System: The Role of Adaptive Testing Technology

Autor/inn/en	Dai, Jing; Gu, Xiaoqing; Zhu, Jiawen
Titel	Personalized Recommendation in the Adaptive Learning System: The Role of Adaptive Testing Technology
Quelle	In: Journal of Educational Computing Research, 61 (2023) 3, S.523-545 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Dai, Jing) ORCID (Gu, Xiaoqing)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.1177/07356331221127303
Schlagwörter	Individualized Instruction; Intelligent Tutoring Systems; Evaluation Methods; Tests; Computer Assisted Testing; Junior High School Students; Academic Achievement; Foreign Countries; China (Shanghai) + Suchen Sie Ihr Suchwort? Individualisierender Unterricht; Intelligentes Tutorsystem; Examination; Prüfung; Examen; Junior High Schools; Student; Students; Sekundarstufe I; Schüler; Schülerin; Schulleistung; Ausland
Abstract	Personalized recommendation plays an important role on content selection during the adaptive learning process. It is always a challenge on how to recommend effective items to improve learning performance. The aim of this study was to examine the feasibility of applying adaptive testing technology for personalized recommendation. We proposed the adaptation of applicable adaptive testing technology as a solution based on the widely accepted teaching philosophy that providing learners with challenging content can stimulate their potential and improve their performance. Specifically, we adapted item selection algorithms to make the difficulty of recommended items above the learner's current knowledge level on the basis of the uniform scale of learner's ability and item difficulty in the adaptive testing area. Participants were recruited from two classes in a junior middle school, one served as the experimental group by applying the adaptive testing recommendation, and the other served as the control group by applying the random recommendation. The results showed that the experimental group students achieved significantly higher scores and demonstrated higher learning abilities than the control group students. Therefore, the adapted testing technology that is being used for personalized recommendation is effective in improving learning performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01