Discovery of Learning Path Based on Bayesian Network Association Rule Algorithm

Autor/inn/en	Shen, Huajie; Liu, Teng; Zhang, Yueqin
Titel	Discovery of Learning Path Based on Bayesian Network Association Rule Algorithm
Quelle	In: International Journal of Distance Education Technologies, 18 (2020) 1, S.65-82, Artikel 4 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-3100
DOI	10.4018/IJDET.2020010104
Schlagwörter	Correlation; Distance Education; Efficiency; Bayesian Statistics; Learning Processes; Educational Objectives; Educational Improvement; Data Analysis; Comparative Analysis; Probability + Suchen Sie Ihr Suchwort? Korrelation; Distance study; Distance learning; Fernunterricht; Effectiveness; Effektivität; Wirkungsgrad; Learning process; Lernprozess; Educational objective; Bildungsziel; Erziehungsziel; Teaching improvement; Unterrichtsentwicklung; Auswertung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	This study aims to create learning path navigation for target learners by discovering the correlation among micro-learning units. In this study, the learning path is defined as a sequence of learning units used to realize a learning goal, and a period used for realizing the learning goal is regarded as a learning cycle. Furthermore, the learning unit datasets are extracted according to the learning cycle. In order to discover the correlations of learning units, we proposed an algorithm named Bayesian Network Association Rule (BNAR), which is used to establish a dynamic learning path according to the learning history of reference learners group who achieved learning goals. Based on the successful learning history, the dynamic learning path navigation will help target learners to improve learning efficiency. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01