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A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network
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Author(s): Kandula Neha (Lovely Professional University, India), Ram Kumar (Lovely Professional University, India)and Monica Sankat (Lovely Professional University, India)
Copyright: 2023
Pages: 19
Source title:
Concepts and Techniques of Graph Neural Networks
Source Author(s)/Editor(s): Vinod Kumar (Koneru Lakshmaiah Education Foundation (Deemed), India)and Dharmendra Singh Rajput (VIT University, India)
DOI: 10.4018/978-1-6684-6903-3.ch011
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Abstract
Predicting student performance becomes tougher thanks to the big volume of information in educational databases. Currently, in many regions, the shortage of existing system to investigate and monitor the coded progress and performance isn't being addressed. First, the study on existing prediction methods remains insufficient to spot the foremost suitable methods for predicting the performance of scholars in many institutions. Second is because of the shortage of investigations on the factors affecting student achievements particularly courses within specified context. Therefore, a systematic literature review on predicting student performance by using data processing techniques is proposed to enhance student achievements. The objective of this work is to supply an outline on the info techniques to predict student performance. Previous studies have extensively reported on optimizing performance predictions to highlight risky students and promote the achievement of good students. There are also contributions that overlap with various research fields.
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