Students' learning style detection using tree augmented naive Bayes

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Li, Ling Xiao, Abdul Rahman, Siti Soraya
التنسيق: مقال
منشور في: The Royal Society 2018
الموضوعات:
الوصول للمادة أونلاين:http://eprints.um.edu.my/21479/
https://doi.org/10.1098/rsos.172108
الوسوم: إضافة وسم
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المؤسسة: Universiti Malaya
الوصف
الملخص:Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely usedmethod to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.