Can Eye Movement Synchronicity Predict Test Performance With Unreliably-Sampled Data in an Online Learning Context?

Webcam-based eye-tracking promises easy and quick data collection without the need for specific or additional eye-tracking hardware. This makes it especially attractive for educational research, in particular for modern formats, such as MOOCs. However, in order to fulfill its promises, webcam-based eye tracking has to overcome several challenges, most importantly, varying spatial and temporal resolutions. Another challenge that the educational domain faces especially, is that typically individual students are of interest in contrast to average values. In this paper, we explore whether an attention measure that is based on eye movement synchronicity of a group of students can be applied with unreliably-sampled data. Doing so we aim to reproduce earlier work that showed that, on average, eye movement synchronicity can predict performance in a comprehension quiz. We were not able to reproduce the findings with unreliably-sampled data, which highlights the challenges that lie ahead of webcam-based eye tracking in practice.