Feature Selection Mechanism for Attention Classification Using Gaze Tracking Data

Khan, A. R., Bokhari, S. M., Khosravi, S., Hussain, S. , Ghannam, R. , Imran, M. A. and Zoha, A. (2022) Feature Selection Mechanism for Attention Classification Using Gaze Tracking Data. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9970936)

Abstract

The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Khan, Ahsan Raza and Imran, Professor Muhammad and Ghannam, Professor Rami and Hussain, Dr Sajjad and Khosravi, Sara
• Authors: Khan, A. R., Bokhari, S. M., Khosravi, S., Hussain, S., Ghannam, R., Imran, M. A., and Zoha, A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISBN: 9781665488235
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy