Early Prediction of Students Performance in Higher Education

Dr. Geeta Tripathi; Kethati Nandini Reddy; Kotagoda Sahithi; Maddineni Ajay Kumar

doi:10.32628/IJSRST24112166

Authors

Dr. Geeta Tripathi Professor & HOD, Dept of CSE, Guru Nanak Institutions Technical Campus, Hyderabad, India Author
Kethati Nandini Reddy Department of CSE, Guru Nanak Institutions Technical Campus, Hyderabad, India Author
Kotagoda Sahithi Department of CSE, Guru Nanak Institutions Technical Campus, Hyderabad, India Author
Maddineni Ajay Kumar Department of CSE, Guru Nanak Institutions Technical Campus, Hyderabad, India Author

DOI:

https://doi.org/10.32628/IJSRST24112166

Keywords:

Graph Mining, Students’ Performance Prediction, Student Academic Performance, Early Prediction, Data Mining

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.

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