Predicting Academic Performance of Students Using a Hybrid Data Mining Approach

  • Bindhia K. FrancisEmail author
  • Suvanam Sasidhar Babu
Education & Training
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student’s information which can be used to invent valuable pattern pertaining learning behavior of students. The institutions of education can utilize educational data mining to examine the performance of students which can support the institution in recognizing the student’s performance. In data mining classification is a familiar technique that has been implemented widely to find the performance of students. In this study a new prediction algorithm for evaluating student’s performance in academia has been developed based on both classification and clustering techniques and been ested on a real time basis with student dataset of various academic disciplines of higher educational institutions in Kerala, India. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students.


Student academic performance Educational data mining Prediction accuracy K-means clustering 


Compliance with ethical standards

Conflict of interest

This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Soni, A., Kumar, V., Kaur, R., and Hemavath, D., Predicting student performance using data mining techniques. International Journal of Pure and applied Mathematics 119(12):221–227, 2018.Google Scholar
  2. 2.
    Yassein, N. A., Helall, R. G. M., and Mohomad, S. B., Predicting student academic performance in KSA using data mining techniques. Journal of Information Technology and Software Engineering 7(5):1–5, 2017.CrossRefGoogle Scholar
  3. 3.
    Saa, A. A., Educational data mining & students’ performance prediction. Int. J. Adv. Comput. Sci. Appl. 7(5):212–220, 2016.Google Scholar
  4. 4.
    Shahiri, A. M., Husain, W., Rashid, N. A. A review on predicting students performance using data mining techniques. The 3rd Information System International Conference, Elsevier, 72, p 414–422, 2015.Google Scholar
  5. 5.
    Ahmad, F., Ismail, N., and Aziz, A. A., The prediction of students’ academic performance using classification data mining techniques. Appl. Math. Sci. 9(129):6415–6426, 2015.Google Scholar
  6. 6.
    Agaoglu, M., Predicting instructor performance using data mining techniques in higher education. IEEE Access 4:2379–2387, 2016.CrossRefGoogle Scholar
  7. 7.
    Acharya, A., and Sinha, D., Early prediction of student performance using machine learning techniques. Int. J. Comput. Appl. (1):107, 2017.CrossRefGoogle Scholar
  8. 8.
    Mueen, A., Zafar, B., and Manzoor, U., Modeling and predicting students academic performance using data mining techniques. International Journal of Mod. Education, Computer Science 8:36–42, 2016.CrossRefGoogle Scholar
  9. 9.
    Ahmad, A. B. E. D., and Elaraby, I. S., Data mining: A prediction for students performance using classification method. World Journal of Computer Applications Technology 2:43–47, 2014.Google Scholar
  10. 10.
    Kaur, M, and Sinngh, G. S. J. Classification and prediction based data mining algorithms to predict slow learners in education sector. 3rd International Conference on Recent Trends in Computing (ICRTC-2015), 2015.Google Scholar
  11. 11.
    Asogbon, M. G., Samuel, O. W., Omisore, M. O., and Ojokoh, B., A multi-class support vector machine approach for students academic performance prediction. International Journal of Multidiscplinary and Current Research 4:210–215, 2016.Google Scholar
  12. 12.
    Pratiyush, G., and Manu, S., Classifying educational data using support vector machines: A supervised data mining technique. Indian J. Sci. Technol. 9(34), 2016.Google Scholar
  13. 13.
    Kadambande, A., Thakur, S., Mohol, A., and Ingole, A. M., Predicting students performance system. International Research Journal of Engineering and Technology 4(5):2814–2816, 2017.Google Scholar
  14. 14.
    Oloruntoba, S. A., and Akinode, J. L., Student academic performance prediction using support vector machine. International Journal of Engineering Sciences and Research Technology 6(12):588–597, 2017.Google Scholar
  15. 15.
    Raihana, Z., and Farah Nabilah, A. M., Classification of students based on quality of life and academic performance by using support vector machine. Journal of Academia UiTM Negeri Sembilan 6(1):45–52, 2018.Google Scholar
  16. 16.
    Shaziya, H., Zaheer, R., and Kavitha, G., Prediction of students performance in semester exams using a Naïve Bayes classifier. International Journal of Innovative Research in Science, Engineering and Technology 4(10):9823–9829, 2015.Google Scholar
  17. 17.
    Makhtar, M., Nawang, H., and Shamsuddin, S. N. W., Analysis on students performance using Naïve Bayes classifier. J. Theor. Appl. Inf. Technol. 95(16):3993–3999, 2017.Google Scholar
  18. 18.
    Patil, V., Suryawanshi, S., Saner, M., Patil, V., and Sarode, B., Student performance prediction using classification data mining techniques. International Journal of Scientific Development and Research 2(6):163–167, 2017.Google Scholar
  19. 19.
    Razaque, F., Soomro, N., Shaikh, S. A., Soomro, S., Samo, J. A., Kumar, N., and Dharejo, H. Using naïve bayes algorithm to students’ bachelor academic performances analysis. In: Engineering Technologies and Applied Sciences (ICETAS), 2017 4th IEEE International Conference. p 1-5, 2017.Google Scholar
  20. 20.
    Divyabharathi, Y., and Someswari, P., A framework for student academic performance using naive Bayes classification technique. J. of Advancement in Engineering and Technology 6(3):1–4, 2018.Google Scholar
  21. 21.
    Kolo, K. D., Adepoju, S. A., and Alhassan, J. K., A decision tree approach for predicting students academic performance. IJ Education and Management Engineering 5:12–19, 2015.Google Scholar
  22. 22.
    Hamsa, H., Indiradevi, S., and Kizhakkethottam, J. J., Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technology 25:326–332, 2016.CrossRefGoogle Scholar
  23. 23.
    Raut, A. B., and Nichat, M. A. A., Students performance prediction using decision tree. Int. J. Comput. Intell. Res. 13(7):1735–1741, 2017.Google Scholar
  24. 24.
    Olaniyi, A. S., Kayode, S. Y., Abiola, H. M., Tosin, S. I. T., and Babatunde, A. N., Students performance analysis using decision tree algorithms, Annals. Computer Science Series 15(1), 2017.Google Scholar
  25. 25.
    Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., and Sarker, K. U. Student Academic Performance Prediction by using Decision Tree Algorithm, 4th International Conference on Computer and Information Sciences. 2018.Google Scholar
  26. 26.
    Zaldivar-Colado, A., Aguilar-Calderon, J. A., Garcia-Sanchez, O. V., Zurita-Cruz, C. E., Moncada Estrada, M., and Bernal-Guadiana, R., Artificial neural networks for the prediction of students academic performance, 8th International Technology, Education and Development Conference, 2014.Google Scholar
  27. 27.
    Binh, H. T., and Duy, B. T., Predicting students' performance based on learning style by using artificial neural networks, 9th International Conference on Knowledge and Systems Engineering, Vietnam, 2017.Google Scholar
  28. 28.
    Gerritsen, L. Predicting student performance with Neural Networks (Doctoral dissertation, Tilburg University). 2017.Google Scholar
  29. 29.
    Okubo, F., Yamashita, T., Shimada, A., and Ogata, H., A neural network approach for students' performance prediction. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 598-599, 2017.Google Scholar
  30. 30.
    Bendangnuksung and Prabu, Students performance prediction using deep neural network. Int. J. Appl. Eng. Res. 13(2):1171–1176, 2018.Google Scholar
  31. 31.
    Tripathy, B. K., Sahu, S. K., Prasad, M. B. N. V., and Barik, K. K., A support vector machine binary classification and image segmentation of remote sensing data of Chilika Lagloon. International Journal of Research in Information Technology 3(5):191–204, 2015.Google Scholar
  32. 32.
    Sharma, H., and Kumar, S., A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR) 5(4):2094–2097, 2016.CrossRefGoogle Scholar
  33. 33.
    Jebaseeli, A. N., Neural network classification algorithm with M-learning reviews to improve the classification accuracy. Int. J. Comput. Appl. 71(23), 2014.Google Scholar
  34. 34.
    Taheri, S., and Mammodov, M., Learning the naives Bayes classifier with optimization models. Int. J. Appl. Math. Comput. Sci. 23(4):787–795, 2014.CrossRefGoogle Scholar
  35. 35.
    Wankhede, S. B., Analytical study of neural network techniques: SOM, MLP and classifier-a survey. IOSR Journal of Computer Engineering 16(3):86–92, 2015.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bharathiar UniversityCoimbatoreIndia
  2. 2.Department of Computer ApplicationSt Thomas College (Autonomous)ThrissurIndia
  3. 3.Department of CSESree Narayana Gurukulam College of EngineeringKolenchery, Ernamkulam DTIndia

Personalised recommendations