Webcam-based attention tracking in Online Learning: A Feasibility Study

A main weakness of the Massive Open Online Learning movement is retention: a small minority of learners (on average 5-10%, in extreme cases <1%) that start a MOOC complete it successfully. There are many reasons why learners are unsuccessful, among the most important ones is the lack of self-regulation: learners are often not able to self-regulate their learning behavior. Designing tools that provide learners with a greater awareness of their learning is vital to the future success of MOOC environments. Detecting learners’ loss of focus during learning is particularly important, as this can allow us to intervene and return the learners’ attention to the learning materials. One technological affordance to detect such loss of focus are Web-cams—ubiquitous pieces of hardware available in almost all laptops today. Recently, researchers have begun to make use of Web-cams as part of complex machine learning-based solutions to detect inattention or loss of focus based on eye tracking and eye gaze data. However, those approaches tend to have a high detection lag, are inaccurate, and are complex to design and maintain. In contrast, in this paper, we explore the possibility to make use of simple metrics such as gaze presence or face presence to detect a loss of focus in the online learning setting. To this end, we evaluate the performance of three consumer and professional eye-tracking frameworks using a benchmark suite we designed specifically for this purpose: it contains a set of common xMOOC user activities and behaviours. The results of our study show that already those simple metrics pose a significant challenge to current hard- and software based eye-tracking solutions.