AI-Based Diagnostic Assessment System: Integrated With Knowledge Map in MOOCs

Autor/inn/en	Lee, Chia-An; Huang, Nen-Fu; Tzeng, Jian-Wei; Tsai, Pin-Han
Titel	AI-Based Diagnostic Assessment System: Integrated With Knowledge Map in MOOCs
Quelle	In: IEEE Transactions on Learning Technologies, 16 (2023) 5, S.873-886 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lee, Chia-An) ORCID (Huang, Nen-Fu) ORCID (Tzeng, Jian-Wei) ORCID (Tsai, Pin-Han)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1109/TLT.2023.3308338
Schlagwörter	Artificial Intelligence; MOOCs; Concept Mapping; Student Evaluation; Mastery Learning; Data Collection; Data Processing; Models; Video Technology; Instructional Materials; Testing + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Concept Map; Schulnote; Studentische Bewertung; Data capture; Datensammlung; Datenverarbeitung; Analogiemodell; Lehrmaterial; Lehrmittel; Unterrichtsmedien; Testdurchführung; Testen
Abstract	Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning performance levels. This study proposes an artificial-intelligence-based knowledge assessment system that integrates knowledge maps to determine students' familiarity with and mastery of course contents. This study employs a structural approach encompassing data collection, data preprocessing, model training, testing, and evaluation. In detail, the system can then customize the knowledge maps and recommend videos according to the knowledge nodes. Students consequently dedicate additional time to studying concepts with which they are unfamiliar and adjust their learning efforts accordingly. After teachers and teaching assistants have captured students' performance metrics and idiosyncratic weaknesses through knowledge maps, teachers can modify the teaching materials. Through the use of education data mining and learning analytics, our system can benefit both teachers and online learners. We hope that the proposed system provides a more personalized and intelligent online learning environment within which students can learn in a more efficient and flexible manner. (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