Analyzing Student’s Academic Performance Using Multilayer Perceptron Model
Gino Sinthia1, M. Balamurugan2
1Gino Sinthia, Department of Computer Science and Engineering, CHRIST Deemed to be University, Faculty of Engineering, Bangalore (Karnataka), India.
2M. Balamurugan, Department of Computer Science and Engineering, CHRIST Deemed to be University, Faculty of Engineering, Bangalore (Karnataka), India.
Manuscript received on 23 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 156-160 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11290275S19/19©BEIESP
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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: Identification of the student’s behavior in the class room environment is very important. It helps the lecturer to identify the needs of the students. It also aids in identifying the strength and weakness of the individual and guide them to improve on their performance. Observing and supervising the students regularly can improve their performance. The data has been collected from 120 students who took the common the course taught by two different lectures. The students were observed based on the internal assignments and quizzes and the model exam given by the respective lecturers. In this paper the students are categorized into different groups based on their performance using Multilayer Perceptron (MLP) and also different factors which are influencing the performance of the students are identified.
Keywords: Student’s Performance, Machine Learning, Multilayer Perceptron, k- Nearest Neighbor (KNN), K-Means.
Scope of the Article: High Performance Computing