Student Monitoring System using Machine Learning
Ashok Kumar S1, Ragul R N2, Gokula krishnan D3, Praveen Kumar S4
1Ashok kumar S*, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
2Ragul R.N, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
3Praveen Kumar S, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
4Gokula krishnan D, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 1475-1479 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4213049620/2020©BEIESP | DOI: 10.35940/ijitee.E4213.049620
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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: The period behavioral engagement is commonly used to describe the scholar’s willingness to participate within the getting to know the system. Emotional engagement describes a scholar’s emotional attitude toward learning. Cognitive engagement is a chief part of overall learning engagement. From the facial expressions the involvement of the students in the magnificence can be decided. Commonly in a lecture room it’s far difficult to recognize whether the students can understand the lecture or no longer. So that you can know that the comments form will be collected manually from the students. However the feedback given by the students will now not be correct. Hence they will no longer get proper comments. This hassle can be solved through the use of facial expression detection. From the facial expression the emotion of the students may be analyzed. Quantitative observations are achieved in the lecture room wherein the emotion of students might be recorded and statistically analyzed. With the aid of the use of facial expression we will directly get correct information approximately college students understand potential, and determining if the lecture becomes exciting, boring, or mild for the students. And the apprehend capability of the scholar is recognized by the facial emotions.
Keywords: Facial Emotions, Engagement, Emotion Detection.
Scope of the Article: Machine Learning