Stimuli-based Gaze Analytics to Enhance Motivation and Learning in MOOCs

Eye-tracking and artificial intelligence to enhance motivation and learning

The interaction with the various learners in a Massive Open Online Course (MOOC) is often complex. Contemporary MOOC learning analytics relate with click-streams, keystrokes and other user-input variables. Such variables however, do not always capture users' learning and behavior (e.g., passive video watching). In this paper, we present a study with 40 students who watched a MOOC lecture while their eye-movements were being recorded. We then proposed a method to define stimuli-based gaze variables that can be used for any kind of stimulus. The proposed stimuli-based gaze variables indicate students' content-coverage (in space and time) and reading processes (area of interest based variables) and attention (i.e., with-me-ness), at the perceptual (following teacher's deictic acts) and conceptual levels (following teacher discourse). In our experiment, we identified a significant mediation effect of the content coverage, reading patterns and the two levels of with-me-ness on the relation between students' motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process. One key ingredient in the process of achieving a high level of adaptation in providing gaze-aware feedback to the students is to use Artificial Intelligence (AI) algorithms for prediction of student performance from their behaviour. In this contribution, we also present a method combining state-of-the-art AI technique with the eye-tracking data to predict student performance. The results show that the student performance can be predicted with an error of less than 5%.

SPRINGER HEIDELBERG2020

How Students Learn using MOOCs: An Eye-tracking Insight

Pierre Dillenbourg, Patrick Jermann, Kshitij Sharma

We present the results of an eye-tracking study on a Massive Open Online Course (MOOC) lecture showing the relation between gaze variables and students’ performance and learning strategy. 40 students watched a MOOC lecture while their eye-movements were being recorded. We present a method to define stimuli-based gaze variables that can be defined for any kind of stimulus. The advantage of using stimuli-based gaze variables is that the relation of the gaze indicators with performance measures and behavioral measures can be interpreted differently for different kinds of stimulus. MOOCs are very diverse in nature; having such a variable description enables the researchers to have a common measure for the different kind of stimulus present in different MOOCs. The long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide the students with gaze-aware feedback to improve the overall learning process.

2014

Gaze Analysis methods for Learning Analytics

Kshitij Sharma

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 gaze is displayed the perceived difficulty of the content decreases significantly as compared to the moments when there is no gaze augmentation. In a nutshell, through this dissertation, we show that the gaze can be used to understand, support and improve the dyadic interaction, in order to increase the chances of achieving a higher level of task-based success.

EPFL2015

Gaze Analysis methods for Learning Analytics

Kshitij Sharma

EPFL2015