Synergistic Performance Forecasting: Harnessing Gradient Boost and Linear Discriminant Analysis for Student Achievement Prediction

DOI

10.2991/978-94-6463-471-6_97How to use a DOI?

Keywords

Analysis of Academic Performance; Statistical Approaches; Academic Prediction; Machine Learning; Classification Models; Rule-Based Recommendation

Abstract

In the realm of student information systems, educational institutions grapple with the complexities posed by an ever-expanding repository of academic data, encompassing diverse files, records, and multimedia. Traditional statistical methods and database systems often fall short in managing the sheer volume of information, hindering the extraction of actionable insights. This study introduces an innovative tool a comprehensive framework rooted in a rule-based suggestion approach to tackle these challenges. The tool not only facilitates thorough analysis but also delves into predictive modeling of students’ academic paths. By examining student demographics, academic performance metrics, and psychological attributes, the framework proves to be a valuable resource for educators, students, and parents alike. Leveraging robust data-mining techniques, the system significantly improves the accuracy of academic forecasting. Despite inherent limitations, it emerges as an invaluable guide. A detailed case study involving 1000 students highlights the superior performance of this approach compared to existing frameworks, confirming its effectiveness in assisting educational institutions. This tool stands out as a vital asset, unlocking the untapped potential within academic datasets and empowering educators to discern high-achieving and low-performing students.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Tamilkodi
AU  - K. Valli Madhavi
AU  - J. Annie Christi
AU  - S. Revanth Kumar
AU  - M. Naga Pavan
AU  - A. Phani Manohar
PY  - 2024
DA  - 2024/07/30
TI  - Synergistic Performance Forecasting: Harnessing Gradient Boost and Linear Discriminant Analysis for Student Achievement Prediction
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1013
EP  - 1021
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_97
DO  - 10.2991/978-94-6463-471-6_97
ID  - Tamilkodi2024
ER  -