Detecting and Monitoring Students Engagement in MOOC and Virtual Environment using Deep Learning Technique

Authors

  • A Ezil Sam Leni
  • Rangesh B
  • Praveen C
  • Sandeep C

Abstract

The Existing System uses various sensors such as, 1. Photoplethysmographic (PPG),  2. Galvanic Skin response.

The use of these sensor data makes the whole project expensive. To overcome the costly approach, we have proposed a system that uses the primary laptop/mobile RGB camera to capture the video and extract frames and use a deep learning algorithm; the emotion and engagement of students can be detected and monitored. The algorithm that we use is CNN(Convolutional Neural Network). CNN's are famous records pushed system mastering technique that uses deep mastering to extract capabilities and classify photo records.

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Published

2021-06-13

How to Cite

Ezil Sam Leni, A., B, R. ., C, P., & C, S. (2021). Detecting and Monitoring Students Engagement in MOOC and Virtual Environment using Deep Learning Technique. Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 2(2), 7 - 10. Retrieved from https://digitalintelligentsiaconsultancyservices.com/ojs/index.php/iamecs2/article/view/60