Real-time Attention Span Tracking in Online Education
Kavyashree N1, Shailaja L K, Anitha J2, Sindhu P3
1Kavyashree N, Assistant Professor, Department of Master of Computer Applications, Dr. Ambedkar Institute of Technology Bangalore (Karnataka), India.
2Shailaja L K, Assistant Professor, Department of Master of Computer Applications, Dr. Ambedkar Institute of Technology, Bangalore (Karnataka), India.
3Anitha J, Assistant Professor Department of Master of Computer Applications, Dr. Ambedkar Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 05 July 2022 | Revised Manuscript received on 11 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 11-17 | Volume-11 Issue-9, August 2022 | Retrieval Number: 100.1/ijitee.G91910811922 | DOI: 10.35940/ijitee.G9191.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: E-learning has changed how students grow over the past ten years by allowing them access to high-quality education whenever and wherever they need it. In any case, understudies frequently get occupied in light of different reasons, which influence the learning ability by and large. Numerous experts have been striving to address the nature of online education, but we really need a comprehensive solution to this problem. This essay aims to present a method for monitoring students’ continuing attention during online classes using the surveillance camera and oral input. We investigate different picture handling strategies and AI calculations all through this review. We suggest a framework that makes use of five specific non-verbal cues to calculate an understudy’s consideration score during computer-based tasks and generate continual feedback for both the association and the understudy. The output can be used as a heuristic to investigate both the speakers’ and understudy’ general methods of exhibiting themselves.
Keywords: AI, Attention, Blink Rate, Drowsiness, Eye gaze Tracking, Emotion Classification, Face Identification, Posture assessment, and Noise recognition.
Scope of the Article: Classification