A Combined Approach to Improve Supervised E-Learning using Multi-Sensor Student Engagement Analysis

Abstract

E-learning provides an important means of education which can reach masses irrespective of their locations all over the world. The E-learning systems and platforms have evolved over the years, but E-learning methodologies are still lagging in matching the benefits of teacher-student interaction in a classroom. The absence of human supervision is always a concern as a student cannot be monitored for losing interest or not getting engaged in the e-learning session. Given this problem, this research was carried out in two phases, first to identify a solution which can augment the emotional and mental state of the student to a feedback system and second, use the feedback to change the content as per learner's level of engagement or interest. The findings presented in this study relates to the first phase of the research. A novel methodology was used to use three types of measurements to assess the interest or engagement of the student during an E-learning session. These measurements were carried out using Facial recognition based engagement analysis, Electro Dermal Activity (EDA) data and pulse rate information. Facial recognition was carried out to infer interest level from the student's facial expressions and was used as a reference to find correlation with EDA and pulse rate. A single timeline was used to carry out all these three mode of measurements. Statistical correlation results showed that all the three modes of measurements exhibit significant correlation between them and thus these can be effectively used together to ascertain the engagement or interest of the student in an E-learning session. These findings will help in improving the efficacy of E-learning environment by altering the content structure and visual presentation as per learner's learning curve.