Classification of Student’s Confusion Level in E-Learning using Machine Learning
Bikram Kumar1, Deepak Gupta2, Rajat Subhra Goswami3
1Bikram Kumar, M.Tech Scholar, Department of CSE, NIT, Naharlagun (Arunachal Pradesh), India.
2Deepak Gupta, Assistant Professor, Department of CSE, NIT, Naharlagun (Arunachal Pradesh), India.
3Rajat Subhra Goswami, Assistant Professor, Department of CSE, NIT, Naharlagun (Arunachal Pradesh), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 346-351 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10921292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1092.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the advancement of technology, the traditional mode of teaching-learning pedagogy has evolved into online education system as it is easily accessible. But, it is very difficult to detect whether the students are ‘confused’ or ‘not confused’ while watching online videos. Getting confused while watching online videos is one of the root causes of less performance of the students. Keeping in mind the above statements, we would like to investigate whether the students are ‘confused’ or ‘not confused’ while watching Massive Open Online Course (MOOC) videos. There are a lot of studies that prove electroencephalogram (EEG) signals behave differently as we are in different conditions such as happy, sad, angry, etc. So, in this paper, we have applied several supervised learning algorithms to detect if the students are ‘confused’ or ‘not confused’ while watching MOOC videos using EEG data. The results of this paper show that machine learning is a potential technique, for the analysis of EEG data that can detect the confusion level of the students which is comparable to human observation for predicting the confusion level of the students that can improve the quality of online education system.
Keywords: Confusion, EEG, Machine Learning, MOOC, Supervised Learning.
Scope of the Article: Machine Learning