dc.contributor.author | Sharma, Kshitij | |
dc.contributor.author | Dillenbourg, Pierre | |
dc.contributor.author | Giannakos, Michail | |
dc.date.accessioned | 2020-03-24T10:04:33Z | |
dc.date.available | 2020-03-24T10:04:33Z | |
dc.date.created | 2019-12-31T17:08:09Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-7281-3485-7 | |
dc.identifier.uri | https://hdl.handle.net/11250/2648299 | |
dc.description.abstract | 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 learners' 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' 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 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) | |
dc.title | Stimuli-Based Gaze Analytics to Enhance Motivation and Learning in MOOCs | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.doi | 10.1109/ICALT.2019.00052 | |
dc.identifier.cristin | 1764588 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |