Analysis of Student’s Education Data Based on Data Mining Techniques

Authors

  • Vijayakumar Thota Associate Professor, Business Management, NSB Business School, Bangalore
  • S. Sharanyaa Assistant Professor, Department of Information Technology, Panimalar Engineering College, Chennai 600123.
  • Ayisha Noori V. K. Assistant professor, Dept of Artificial Intelligence, Madanappalle Institute of Technology and Science, Kadiri Road, Angallu Madanapalle, Andhrapradesh - 517325
  • K. R. Shanthy Associate Professor, ECE Department, Loyola Institute of Technology, Palanchur, Chennai.
  • M. Bharathiraja Professor, Automobile Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode.

Keywords:

Performance prediction, Education data mining, DM techniques

Abstract

One of the trickiest and most popular research fields in educational data mining (EDM) is student achievement analysis. Scientists get attracted to this issue owing to the fluctuating implications of multiple factors on functionality. This dedication is additionally ignited by the huge consumption of instructional records, especially when it comes to online learning. Although there are numerous EDM surveys in the scholarship, there aren't plenty that solely concentrate on student achievement evaluation and projection. These specialized assessments are small in focus and largely emphasize investigations that seek to find potential predictors or patterns of student achievement. This paper proposes data mining through the required algorithms for the accurate extraction of data for further analysis. A brief overview of the current situation of studies in that field is the goal that this literature review attempts to communicate. We employed a couple of methods for performing a literature review: initially, we employed the primary search engines to identify documents, and then we picked them based on previously established requirements. The info collected from student conversations with learning management systems and assessment tasks were the most important elements in early forecasting. At last, the kind of schooling system identified how early projections could be formed.

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References

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Published

12.01.2024

How to Cite

Thota, V. ., Sharanyaa, S. ., V. K., A. N. ., Shanthy, K. R. ., & Bharathiraja, M. . (2024). Analysis of Student’s Education Data Based on Data Mining Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 240–247. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4509

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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