Significance of Education Data Mining in Student’s Academic Performance Prediction and Analysis


  • Shujat Hussain University Institute of Information Technology, Arid Agriculture University, Rawalpindi, 00666, Pakistan
  • Saif Ur Rehman University Institute of Information Technology, Arid Agriculture University, Rawalpindi, 00666, Pakistan
  • Syed Shaheeq Raza University Institute of Information Technology, Arid Agriculture University, Rawalpindi, 00666, Pakistan
  • Khalid Mahmood Institute of Computing and Information Technology, Gomal University, D.I.Khan, 29220, Pakistan
  • Qamar Abbas Department of Computer Science, Faculty of Computing and Information Technology, International Islamic University, Islamabad, 04403, Pakistan
  • Mahwish Kundi University of Leicester, Leicester, LE1, United Kingdom


Machine Learning, Support Vector Machine, Decision Support, Data Mining


Data Mining (DM) is relevant to extract the hidden patterns from the voluminous amount of the data. Applying DM, in education is an evolving interdisciplinary research domain, which is also called as educational data mining (EDM). At present, student data about their academics is available to identify important hidden trends to be explored for enhancing student academic performance. In higher education, forecasting student success is essential for helping with course selection and creating individualized study schedules. It helps instructors and managers keep tabs on students, ensure their development, and modify training programs for the best results. Growth and development of any nation depend on educational institutions since they are fundamental social foundations. It is now feasible to use past data for effective learning and prediction of future behavior in a variety of troublesome areas thanks to the development of DM as a potent approach. Educational institutions may make wise judgments and promote improvements in the education sector by utilizing the possibilities of DM supported EDM approaches. It is feasible to pinpoint improvement areas and direct upcoming skill development by examining pupils' performance on various academic evaluations. Furthermore, this procedure lessens the frequency of official warnings and ineffective student expulsions, fostering a more encouraging and fruitful learning atmosphere. In this work, a unique algorithm that combines classification and clustering approaches to predict students' academic success has been suggested. Real-time student datasets from several academic institutes in higher education were used to test the suggested approach. The findings show that the suggested model worked well for predicting students' academic achievement.


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How to Cite

Shujat Hussain, Ur Rehman, S., Syed Shaheeq Raza, Mahmood, K., Abbas, Q., & Kundi, M. (2023). Significance of Education Data Mining in Student’s Academic Performance Prediction and Analysis. International Journal of Innovations in Science & Technology, 5(3), 215–231. Retrieved from

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