Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems

Autor/inn/en	Liu, Ming-Tsung; Yu, Pao-Ta
Titel	Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems
Quelle	In: Educational Technology & Society, 14 (2011) 1, S.107-120 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Electronic Learning; Statistics; Tutoring; Computer Assisted Instruction; Item Response Theory; Individual Differences; Student Interests; Academic Achievement; Models; Behavior Patterns; College Freshmen; Statistical Analysis + Suchen Sie Ihr Suchwort? Statistik; Förderkonzept; Nachhilfeunterricht; Computer based training; Computerunterstützter Unterricht; Item-Response-Theorie; Individueller Unterschied; Studieninteresse; Schulleistung; Analogiemodell; Studienanfänger; Statistische Analyse
Abstract	A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy behind the LCI is that if any non-cognitive factor influences a learner, the effect will eventually be reflected in his/her learning achievement. Therefore, it's our explicit attempt to build a prototype system aimed at assessing aspects of learning other than cognitive factors. This study proposes a personalized e-learning system based on Item Response Theory which considers both course difficulty and learner's ability to provide adaptive learning paths. The LCI, which originates from the person-fit statistics in psychometric theory, statistically judges whether the observed learning achievement is significantly different from the achievement predicted by the Item Response Theory (IRT) models. If such an aberrant learning pattern is detected, a computer tutoring agent appears to notify and encourage that learner. Furthermore, human tutors may get involved periodically to offer further guidance to support learners with aberrant patterns. Experimental results show that such diagnostics could enhance the learning efficiency and smooth the learning experience. (Contains 6 tables and 7 figures.) (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10