Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-Learning Environment

Autor/inn/en	Ma, Hua; Huang, Zhuoxuan; Tang, Wensheng; Zhu, Haibin; Zhang, Hongyu; Li, Jingze
Titel	Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-Learning Environment
Quelle	In: IEEE Transactions on Learning Technologies, 16 (2023) 5, S.680-693 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ma, Hua) ORCID (Huang, Zhuoxuan) ORCID (Tang, Wensheng) ORCID (Zhu, Haibin) ORCID (Zhang, Hongyu) ORCID (Li, Jingze)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1109/TLT.2023.3240931
Schlagwörter	Prediction; Student Evaluation; Performance; Tests; Cognitive Measurement; Individualized Instruction; Electronic Learning; Knowledge Level; Misconceptions; Ambiguity (Semantics); Algorithms; Probability + Suchen Sie Ihr Suchwort? Vorhersage; Schulnote; Studentische Bewertung; Achievement; Leistung; Examination; Prüfung; Examen; Individualisierender Unterricht; Wissensbasis; Missverständnis; Algorithm; Algorithmus; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving the accuracy of performance prediction. A novel approach via neutrosophic cognitive diagnosis is proposed to predict student performance in a personalized e-learning environment. This approach innovatively employs the neutrosophic set (NS) theory to comprehensively measure the students' cognitive status on knowledge concepts from three features, i.e., understanding level, degree of misunderstanding, and uncertainty. A new measurement method of NS similarity is used to identify top-k neighboring students for one student. Then, we predict a student's performance on exercises in future exams by integrating a neighborhood-based collaborative filtering algorithm with a probabilistic matrix factorization method to address the data sparsity problem. Finally, the experiments based on real-world datasets demonstrate that the proposed approach provides higher prediction accuracy than the existing ones with a low execution cost. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01