Attention Classification and Lecture Video Recommendation Based on Captured EEG Signal in Flipped Learning Pedagogy

Abstract

Flipped learning (FL) utilizes blended learning approaches, where students first learn the lesson from preloaded lecture videos (i.e., online lectures). They complete their activities such as assignments, doubt clearing, practical work, real-life problemsolving inside classroom. Learning is directly connected to brain activities, and it becomes crucial to analyze the brain signals to identify the attention level of the learner. In order to analyze students' activity during the lesson, we capture the brain signals of the students and propose a framework for the feature extraction of brain wave (Electroencephalogram (EEG)) signals using variational autoencoder (VAE) in this article. The classification techniques are exploited to identify the weak students in the flipped learning scenario based on their cognitive state; subsequently, cognitive-aware lecture video recommendation system is developed to recommend the non-attentive lecture video/videos to the weak students. This study can be useful for instructors to identify learners who require special care to enhance their learning ability.

Acknowledgment

We sincerely thank the Science and Engineering Research Board (SERB), Govt. of India, New Delhi, India for providing research grant [Project No: EMR/2017/004357, Dated 18/06/2018].

Disclosure statement

The authors are declaring that there is no conflicts of interest.

Data and code availability statement

Data and Code are available upon request to the corresponding author.

Notes

1 http://neurosky.com/

Additional information

Notes on contributors

Rabi Shaw

Rabi Shaw was born in Kolkata, India. He is currently pursuing Ph.D. degree in Computer Science and Engineering from National Institute of Technology, Rourkela, India. His major interests include Signal Processing, Recommender System and Educational Technology. He has published research articles in Future Generation Computer Systems, Cognitive Computation and ICALT.

Bidyut Kr. Patra

Bidyut Kr. Patra is currently an Associate Professor, CSE, IIT(BHU), Varanasi. He earned Ph.D. degree in CSE from IIT Guwahati. He was awarded ERCIM (Marie-Curie) fellowship by European Research Consortium, France. He published research articles in Knowledge and Information Systems (KAIS), Knowledge-Based Systems (KBS), Pattern Recognitions, Expert System With Applications.

Animesh Pradhan

Animesh Pradhan has been born in Odisha, India. After completing his B.Tech in Computer Science and Engineering from National Institute of Technology Rourkela, he currently works with Indian Oil Corporation Limited. At Indian Oil, he is involved in implementing the digitization initiatives using the power of Machine Learning and Artificial Intelligence.

Swayam Purna Mishra

Swayam Purna Mishra has been born in Odisha, India. She currently works as a software engineer in Microsoft. Her current works focuses on understanding customer needs and work integrating them into the company's products. She is interested in data science, development and would like to create some tangible impact.