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A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network

A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network

Kandula Neha, Ram Kumar, Monica Sankat
Copyright: © 2023 |Pages: 19
ISBN13: 9781668469033|ISBN10: 1668469030|ISBN13 Softcover: 9781668469040|EISBN13: 9781668469057
DOI: 10.4018/978-1-6684-6903-3.ch011
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MLA

Neha, Kandula, et al. "A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network." Concepts and Techniques of Graph Neural Networks, edited by Vinod Kumar and Dharmendra Singh Rajput, IGI Global, 2023, pp. 167-185. https://doi.org/10.4018/978-1-6684-6903-3.ch011

APA

Neha, K., Kumar, R., & Sankat, M. (2023). A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network. In V. Kumar & D. Rajput (Eds.), Concepts and Techniques of Graph Neural Networks (pp. 167-185). IGI Global. https://doi.org/10.4018/978-1-6684-6903-3.ch011

Chicago

Neha, Kandula, Ram Kumar, and Monica Sankat. "A Comprehensive Study on Student Academic Performance Predictions Using Graph Neural Network." In Concepts and Techniques of Graph Neural Networks, edited by Vinod Kumar and Dharmendra Singh Rajput, 167-185. Hershey, PA: IGI Global, 2023. https://doi.org/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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