Early Prediction of Students Performance in Higher Education

Geeta Tripathi, Kethati Nandini Reddy, Kotagoda Sahithi and Maddineni Ajay Kumar

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 01-10

Abstract: Evaluating students' learning performance is a fundamental aspect of evaluating any educational institution. When addressing challenges related to the learning process, student performance is critical, and it is one of the key factors used to quantify learning outcomes. The topic of research known as educational data mining (EDM) has grown out of the potential to leverage data knowledge to enhance educational systems. EDM is the development of methods to analyze data collected from educational environments, enabling a more complete and precise understanding of students and the enhancement of their educational results. Evaluating the students' learning results is a crucial part of evaluating any educational institution. One of the key variables used to quantify learning outcomes is student performance, which is significant when addressing problems with the learning process. The field of research known as educational data mining, or EDM, was born out of the potential to leverage data knowledge to enhance educational systems. EDM is the process of developing methods for evaluating information obtained from educational environments. This makes it possible to learn more precise and in-depth information about students and enhances their academic achievement. Academic achievement tests (AAT), general aptitude tests (GAT), admission scores, first-level courses, and other early-stage factors are used in the paper's dimensionality reduction mechanism by T-SNE algorithm for the clustering technique. This allows the study to investigate the relationship between these aspects and GPAs. Regarding the categorization method, the study showcases tests conducted on various machine learning models that forecast student achievement in the initial phases by utilizing diverse attributes such as course grades and entrance exam results. To gauge the models' quality, we employ various evaluation measures. Based on the findings, it appears that early student failure rates can be reduced by educational institutions.

Keywords: Graph Mining; Students’ Performance Prediction; Student Academic Performance; Early Prediction; Data Mining (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:162

DOI: 10.32628/IJSRST24112166

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