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000223607 005__ 20190317000606.0
000223607 0247_ $$2doi$$a10.1145/2883851.2883902
000223607 02470 $$2ISI$$a000390844700053
000223607 037__ $$aCONF
000223607 245__ $$aA gaze-based learning analytics model: in-video visual feedback to improve learner's attention in MOOCs
000223607 269__ $$a2016
000223607 260__ $$bACM$$c2016$$aNew York
000223607 300__ $$a5
000223607 336__ $$aConference Papers
000223607 520__ $$aIn the context of MOOCs, “With-me-ness” refers to the extent to which the learner succeeds in following the teacher, specifically in terms of looking at the area in the video that the teacher is explaining. In our previous works, we employed eye-tracking methods to quantify learners’ With-me-ness and showed that it is positively correlated with their learning gains. In this contribution, we describe a tool that is designed to improve With-me-ness by providing a visual aid superimposed on the video. The position of the visual aid is suggested by the teachers’ dialogue and deixis, and it is displayed when the learner’s With-me-ness is under the average value, which is computed from the other students’ gaze behavior. We report on a user-study that examines the effectiveness of the proposed tool. The results show that it significantly improves the learning gain and it significantly increases the extent to which the students follow the teacher. Finally, we demonstrate how With-me-ness can create a complete theoretical framework for conducting gaze based learning analytics in the context of MOOCs.
000223607 6531_ $$aEye-tracking
000223607 6531_ $$aGaze-aware feedback
000223607 6531_ $$aLearning analytics
000223607 6531_ $$aMassive open online Courses
000223607 6531_ $$aMOOCs
000223607 6531_ $$aVideo based learning
000223607 700__ $$0247261$$g211610$$aSharma, Kshitij
000223607 700__ $$aAlavi, Hamed S.
000223607 700__ $$0240138$$g157873$$aJermann, Patrick
000223607 700__ $$aDillenbourg, Pierre$$g155704$$0240137
000223607 7112_ $$dApril 25 - 29, 2016$$cEdinburgh, United Kingdom$$aSixth International Conference on Learning Analytics & Knowledge
000223607 773__ $$tProceedings of the Sixth International Conference on Learning Analytics & Knowledge$$q417-421
000223607 8564_ $$uhttps://infoscience.epfl.ch/record/223607/files/LAK16-sharma.pdf$$zn/a$$s1023817$$yn/a
000223607 909C0 $$xU12753$$0252475$$pCHILI
000223607 909C0 $$pCEDE$$xU12750$$0252478
000223607 909CO $$qGLOBAL_SET$$pconf$$pIC$$ooai:infoscience.tind.io:223607
000223607 917Z8 $$x211610
000223607 937__ $$aEPFL-CONF-223607
000223607 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000223607 980__ $$aCONF