Gaze Analysis methods for Learning Analytics

Eye-tracking had been shown to be predictive of expertise, task-based success, task-difficulty, and the strategies involved in problem solving, both in the individual and collaborative settings. In learning analytics, eye-tracking could be used as a powerful tool, not only to differentiate between the levels of expertise and task-outcome, but also to give constructive feedback to the users. In this dissertation, we show how eye-tracking could prove to be useful to understand the cognitive processes underlying dyadic interaction; in two contexts: pair program comprehension and learning with a Massive Open Online Course (MOOC). The first context is a typical collaborative work scenario, while the second is a special case of dyadic interaction namely the teacher-student pair. We also demonstrate, using one example experiment, how the findings about the relation between the learning outcome in MOOCs and the students’ gaze patterns can be leveraged to design a feedback tool to improve the students’ learning outcome and their attention levels while learning through a MOOC video. We also show that the gaze can also be used as a cue to resolve the teachers’ verbal references in a MOOC video; and this way we can improve the learning experiences of the MOOC students. This thesis is comprised of five studies. The first study, contextualised within a collaborative setting, where the collaborating partners tried to understand the given program. In this study, we examine the relationship among the gaze patterns of the partners, their dialogues and the levels of understanding that the pair attained at the end of the task. The next four studies are contextualised within the MOOC environment. The first MOOC study explores the relationship between the students’ performance and their attention level. The second MOOC study, which is a dual eye-tracking study, examines the relation between the individual and collaborative gaze patterns and their relation with the learning outcome. This study also explores the idea of activating students’ knowledge, prior to receiving any learning material, and the effect of different ways to activate the students’ knowledge on their gaze patterns and their learning outcomes. The third MOOC study, during which we designed a feedback tool based on the results of the first two MOOC studies, demonstrates that the variables we proposed to measure the students’ attention, could be leveraged upon to provide feedback about their gaze patterns. We also show that using this feedback tool improves the students’ learning outcome and their attention levels. The fourth and final MOOC study shows that augmenting a MOOC video with the teacher’s gaze information helps improving the learning experiences of the students. When the teacher’s