A New Adaptive Online Learning using Computational Intelligence

• Main Authors: Wahyono, Irawan Dwi, Asfani, Khoirudin, Mohamad, Mohd. Murtadha, Saryono, Djoko, Ashar, M., Sunarti, S.
• Format: Conference or Workshop Item
• Published: 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/92461/; http://dx.doi.org/10.1109/ICVEE50212.2020.9243193
• Institution: Universiti Teknologi Malaysia

This study aimed to develop an online learning system that was adaptive to students who wishedto learn electrical machine modules based on their abilities. Adaptive use of online learning functioned to determine the category of students' ability to access modules in online learning. Online learning was also able to provide the determination of modules which can then be done by students, so students can learn independently. Adaptive capabilities in online learning were implemented by utilizing computational intelligence algorithms, namely Naive Bayes and Bayes Network. Naive Bayes was tasked with processing students 'pre-test data in adaptive online learning for the classificationof students' abilities so that after the results of the pretest appeared, students will be given modules that matched their abilities. Whereas Bayes Network used to process student post-test data after students worked on the modules that have been given, adaptive online learning provided the nextmodule to work according to the abilities and desires of students. The testing results of the use of Naive Bayes and Bayes Network on Adaptive Online Learning obtained an average accuracy of 85%.