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Autor/inn/enOzpolat, Ebru; Akar, Gozde B.
TitelAutomatic Detection of Learning Styles for an E-Learning System
QuelleIn: Computers & Education, 53 (2009) 2, S.355-367 (13 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0360-1315
DOI10.1016/j.compedu.2009.02.018
SchlagwörterCognitive Style; Classification; Measures (Individuals); Measurement Techniques; Comparative Analysis; Educational Technology; Computer Uses in Education; Computer Software; Computer System Design; Electronic Learning; Information Technology; Instructional Design; Computer Assisted Testing; Artificial Intelligence; Automation
AbstractA desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user's browsing history and knowledge factors like user's prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder-Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners' learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner's learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder-Silverman learning style. (Contains 17 tables and 7 figures.) (As Provided).
AnmerkungenElsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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