| Summary: | 碩士 === 國立清華大學 === 服務科學研究所 === 103 === E-learning becomes an alternative learning mode since the prevalence of the Internet. Especially, the advance of MOOC (Massive Open Online Course) technology enabled a course to accommodate tens of thousands of online learners. How to improve learners’ online learning experiences on MOOC platforms becomes a crucial task for platform providers. This research adopts EEG technology to detect learners’ learning states while they are watching videos in online e-learning activities, hoping to improve their learning outcomes. In this research, we built a system to capture and tag the mental states while subjects are watching online videos and use different normalization methods and time windows to process the data obtained from EEG devices. Finally, we used different supervised learning algorithms to train and test the classifiers and evaluate the results. The results proved that we provide an efficient data processing way to train classifiers and obtain the high accuracy rate comparing with that of previous researches. We consider this system can facilitate users’ self-awareness of learning states in an efficient way while they are in online e-learning activities, and improve their experiences in MOOC platforms.
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