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Top1. Introduction
E-learning has been defined as a platform or mode of learning allowing learners to attend a course online where they will neither meet the instructor face to face nor access on-campus learning materials (Xin, 2009). E-learning provides an important means of education which can reach masses irrespective of their locations all over the world (Al-Alwani, 2016).
Online learning can be synchronous or asynchronous. Asynchronous online communication occurs in a time-independent environment, whereas synchronous conferencing systems are time-dependent systems. Learners in the synchronous system faced challenges such as time constraints (Park and Bonk, 2007). Asynchronous online learning allows learning independent of time, place and pace (Nortvig et al. 2018). The researchers implemented asynchronous e-learning using a Learning Management System (LMS) in their study. One of the disadvantages of asynchronous e-learning is some students find themselves all alone in their learning process. This feeling of loneliness results in frustration and loss of motivation, leading to a high drop-out rate (Leo et al. 2009). More ever, 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 (Al-Alwani, 2016).
E-learning systems could be improved by tracking students’ disengagement that, in turn, would allow personalized interventions at appropriate times to reengage students (Cocea and Weibelzahl, 2011). Student engagement is about students putting time, energy, thought, and effort and to some extent feelings into their learning (Dixon, 2015). Student engagement, in online courses, is generally poorer than in face-to-face courses. Poor student engagement poses risks to both the students themselves and their instructor (Stot, 2016). Engagement is the key factor for successful learning (Pesare et al., 2016). Learner’s engagement can be traced from the interaction of the learner with online content through a learning tool (Beck, 2004). The utilization of facial features can also be used to automatically detect student engagement (Bosch et al. 2016).