Ai Based Structural Equation Modelling to Classify the Students’ Performance in Higher Education Institutions

Authors

  • R. Josphineleela Professor, Department of CSE, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India.
  • Vasanthakumari Sundararajan Associate Professor , Pediatric and Neonatal Nursing department, Institute of Health Sciences, Wollega University, Ethiopia
  • Meenakshi K. Professor, Department of Mathematics, CMR Institute of Technology, Bengaluru, Karnataka, India
  • Alaparthi Maruthi Varaprasad Associate Professor, Department of Accounting and Finance, College of Business and Economics, Ambo Universty, Ambo, Ethiopia, East Africa
  • Pavan Kumar Yadavalli Professor, Department of Special Needs and Inclusive Education, Institute of Education and Behavioral Science, Ambo University, P.B.No-19, Ambo, Ethiopia, East Africa.
  • D. Praveenadevi Assistant Professor, KL Business School, Koneru Lakshmaiah Education foundation (Deemed to be University), Andhra Pradesh, India.

Keywords:

Structural Modelling, Classification, Student Performance, Education

Abstract

When conducting classification tests, one of the most difficult challenges that can occur is ensuring that a high degree of accuracy is maintained in spite of the presence of unbalanced data sets. Achieving a high accuracy result in a classification study in which a class with a large number of samples can be better learned does not, however, provide information about the efficiency of the results of the other classes, and the accuracy provides conclusions that are misleading due to the fact that the results are so accurate. Using this strategy, it is possible to classify the great majority of students into a range of different categories (pass/fail, risky/not hazardous, etc.). When dealing with data that is not evenly distributed, the F1-score and the ROC AUC score are more accurate evaluations of the overall performance of the model compared to the other metrics. On the other hand, certain measurements, such as recall and precision, represent the level of achievement for lessons and provide direction for understanding the material covered in those classes. If the findings of the study solely depend on the accuracy metric, then it is possible that it will be challenging to integrate these findings into reality.

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Published

13.02.2023

How to Cite

Josphineleela, R. ., Sundararajan, V. ., K., M. ., Maruthi Varaprasad, A. ., Kumar Yadavalli, P. ., & Praveenadevi, D. . (2023). Ai Based Structural Equation Modelling to Classify the Students’ Performance in Higher Education Institutions. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 203–212. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2647

Issue

Vol. 11 No. 4s (2023)

Section

Research Article

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