AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866915097738215424 |
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| author | Ravenor, R. Yamamoto |
| author_facet | Ravenor, R. Yamamoto |
| contents | Existing information on AI-based facial emotion recognition (FER) is not easily comprehensible by those outside the field of computer science, requiring cross-disciplinary effort to determine a categorisation framework that promotes the understanding of this technology, and its impact on users. Most proponents classify FER in terms of methodology, implementation and analysis; relatively few by its application in education; and none by its users. This paper is concerned primarily with (potential) teacher-users of FER tools for education. It proposes a three-part classification of these teachers, by orientation, condition and preference, based on a classical taxonomy of affective educational objectives, and related theories. It also compiles and organises the types of FER solutions found in or inferred from the literature into "technology" and "applications" categories, as a prerequisite for structuring the proposed "teacher-user" category. This work has implications for proponents', critics', and users' understanding of the relationship between teachers and FER. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_15119 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories Ravenor, R. Yamamoto Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Human-Computer Interaction Existing information on AI-based facial emotion recognition (FER) is not easily comprehensible by those outside the field of computer science, requiring cross-disciplinary effort to determine a categorisation framework that promotes the understanding of this technology, and its impact on users. Most proponents classify FER in terms of methodology, implementation and analysis; relatively few by its application in education; and none by its users. This paper is concerned primarily with (potential) teacher-users of FER tools for education. It proposes a three-part classification of these teachers, by orientation, condition and preference, based on a classical taxonomy of affective educational objectives, and related theories. It also compiles and organises the types of FER solutions found in or inferred from the literature into "technology" and "applications" categories, as a prerequisite for structuring the proposed "teacher-user" category. This work has implications for proponents', critics', and users' understanding of the relationship between teachers and FER. |
| title | AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2308.15119 |