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Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology

Talkat Learning at Scale 2020 · 2020-08-12..08-14· [ai-assisted-development]
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name:Shan Jing
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name:Hong Li
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Two mixed-method studies probe student views: 11 in-class observations with affect-sensing wristbands plus interviews and a survey of online OMSCS students. Students accept affect data for legitimate uses but cite accuracy confounds, privacy/security, and fear of using signals for grading; many prefer anonymized aggregated sharing and full control. Design wants unobtrusive, socially acceptable wearables, clear but minimal feedback, and sensors that support scaling for empowerment by enabling self-regulated learning rather than only instructor surveillance.

AI-written summary · about

  1. 0:00Start
  2. 5:00Methods
  3. 6:17Students' attitudes
  4. 7:24Students' concerns
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