Literaturnachweis - Detailanzeige
Autor/inn/en | Shen, Huajie; Liu, Teng; Zhang, Yueqin |
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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 |
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 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 |