An Analytical Model to Track, Analyze and Predict Scholar’s Academic Performance
Keywords:Academic prediction, Detection of grades, Score analysis, Tracking student performance
The objective of this machine learning project is to classify and predict the future academic grades and leadership scores of the students through building a convolution neural network to predict the scores. The application works as a platform for exposing the semester marks of the students through machine learning technique. The main goal is to predict the academic performance using machine learning to develop a model to predict the student’s semester grade result. The ability to predict student performance in education is very significant in educational environments. The stored database contains student’s information to improve student’s perspective and behaviour. Using that information, we can analyse the performance, which will help for both students and mentors. The system learns the Attendance of the student, Difficulty of the future subjects and previous performance of a student to predict the future semester grades with the help of attendance and activities. An institution needs to know the case history of their registered students of their institute to predict their performance. This will help mentors consolidate the student on improving and developing each student’s curriculum record. It refers to performing various data produced by students in order to evaluate learning process like, predict the future performance and identify probable problems. The mentor can identify student’s performance and can counsel the students to perform better, likewise the student can improve their performance in examinations. This analysis will be performed to improve educational process. The main intention is to identify and support the students to score better marks.
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Copyright (c) 2021 G. Vigneshwaran, S. Simritha, R. Rasika, S. Sangeetha
This work is licensed under a Creative Commons Attribution 4.0 International License.