DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Sung-Ju | - |
dc.contributor.advisor | 이성주 | - |
dc.contributor.author | Lee, Taeckyung | - |
dc.date.accessioned | 2023-06-26T19:34:34Z | - |
dc.date.available | 2023-06-26T19:34:34Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1008365&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/310005 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2022.8,[iii, 26 p. :] | - |
dc.description.abstract | Online education has become more important due to COVID. However, there is a gap between lecturers and students in online learning: lecturers demand to know students' attentional states | - |
dc.description.abstract | however, online setting limits observing the entire class' attention. Moreover, existing attentional state prediction methods utilize specialized sensors such as eye trackers, which are not readily deployable in real-world settings. To solve the problem, we utilize facial recordings from student webcams for online learners' attentional state prediction. By the experiment in the wild with 37~participants, we end up with a dataset consisting of a total of 15~hours of facial recordings with corresponding 1,100~attentional state probings. We present $\textsc{Pafe}$ (Predicting Attention with Facial Expression), a facial-video-based framework for attentional state prediction that focuses on the vision-based representation of traditional physiological mind-wandering features related to partial drowsiness, emotion, and gaze. Based on $\textsc{Pafe}$, we present the end-to-end visualization system providing the attentional state of students. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Online Education▼aAttention▼aComputer Vision▼aPsychology | - |
dc.subject | 온라인 교육▼a집중력▼a컴퓨터 비전▼a심리학 | - |
dc.title | Predicting attentional states with facial videos in online lectures | - |
dc.title.alternative | 얼굴 영상을 통한 온라인 강의 수강생의 집중력 예측 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 이택경 | - |
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