Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this...

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Vydáno v:	International journal of artificial intelligence in education Ročník 30; číslo 2; s. 236 - 270
Hlavní autoři:	Behera, Ardhendu, Matthew, Peter, Keidel, Alexander, Vangorp, Peter, Fang, Hui, Canning, Susan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer New York 01.06.2020 Springer Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Behavior Patterns Cognitive tasks Computer Science Computer Science Education Computer vision Computers and Education Context Deep learning Difficulty Level Distance learning Education Educational Environment Educational Researchers Educational Strategies Educational Technology Emotional factors Emotions Eye Movements Head Image classification Intelligent Tutoring Systems Learner Engagement Learning Processes Nonverbal Communication Online instruction Problem Solving Students Teaching Teaching Methods Tutoring Undergraduate Students User Interfaces and Human Computer Interaction Video Technology Neural models applied to AIED systems Adaptive and intelligent multimedia and hypermedia systems Intelligent Tutoring Systems (ITSs) Computer-supported collaborative learning
ISSN:	1560-4292, 1560-4306
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Abstract	Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%).
AbstractList	Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%). Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner's nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of "hand-over-face gestures" (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of "hand-over-face" gestures in images with a classification accuracy of 86.87%. There is a prominent increase in "hand-over-face" gestures when the difficulty level of the given exercise increases. The "hand-over-face" gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%).
Audience	Higher Education Postsecondary Education
Author	Keidel, Alexander Fang, Hui Matthew, Peter Behera, Ardhendu Vangorp, Peter Canning, Susan
Author_xml	– sequence: 1 givenname: Ardhendu orcidid: 0000-0003-0276-9000 surname: Behera fullname: Behera, Ardhendu email: beheraa@edgehill.ac.uk organization: Department of Computer Science, Edge Hill University – sequence: 2 givenname: Peter surname: Matthew fullname: Matthew, Peter organization: Department of Computer Science, Edge Hill University – sequence: 3 givenname: Alexander surname: Keidel fullname: Keidel, Alexander organization: Department of Computer Science, Edge Hill University – sequence: 4 givenname: Peter surname: Vangorp fullname: Vangorp, Peter organization: Department of Computer Science, Edge Hill University – sequence: 5 givenname: Hui surname: Fang fullname: Fang, Hui organization: Department of Computer Science, Loughborough University – sequence: 6 givenname: Susan surname: Canning fullname: Canning, Susan organization: Department of Computer Science, Edge Hill University, Centre of Educational Development and Support, Faculty of Health and Life Sciences
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Snippet	Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of...
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SubjectTerms	Algorithms Artificial Intelligence Behavior Patterns Cognitive tasks Computer Science Computer Science Education Computer vision Computers and Education Context Deep learning Difficulty Level Distance learning Education Educational Environment Educational Researchers Educational Strategies Educational Technology Emotional factors Emotions Eye Movements Head Image classification Intelligent Tutoring Systems Learner Engagement Learning Processes Nonverbal Communication Online instruction Problem Solving Students Teaching Teaching Methods Tutoring Undergraduate Students User Interfaces and Human Computer Interaction Video Technology
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Title	Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems
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