Skip to main content

Advertisement

SpringerLink
Log in

Predicting Academic Performance of Students Using a Hybrid Data Mining Approach

  • Education & Training
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  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. 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.

    Article  Google Scholar 

  3. Saa, A. A., Educational data mining & students’ performance prediction. Int. J. Adv. Comput. Sci. Appl. 7(5):212–220, 2016.

    Google Scholar 

  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.

  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. Agaoglu, M., Predicting instructor performance using data mining techniques in higher education. IEEE Access 4:2379–2387, 2016.

    Article  Google Scholar 

  7. Acharya, A., and Sinha, D., Early prediction of student performance using machine learning techniques. Int. J. Comput. Appl. (1):107, 2017.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

  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. Pratiyush, G., and Manu, S., Classifying educational data using support vector machines: A supervised data mining technique. Indian J. Sci. Technol. 9(34), 2016.

  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. 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. 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. 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. 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. 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. 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.

  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. 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. 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.

    Article  Google Scholar 

  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. 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.

  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.

  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.

  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.

  28. Gerritsen, L. Predicting student performance with Neural Networks (Doctoral dissertation, Tilburg University). 2017.

  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.

  30. Bendangnuksung and Prabu, Students performance prediction using deep neural network. Int. J. Appl. Eng. Res. 13(2):1171–1176, 2018.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  33. Jebaseeli, A. N., Neural network classification algorithm with M-learning reviews to improve the classification accuracy. Int. J. Comput. Appl. 71(23), 2014.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bharathiar University, Coimbatore, India

    Bindhia K. Francis

  2. Department of Computer Application, St Thomas College (Autonomous), Thrissur, Kerala, India

    Bindhia K. Francis

  3. Department of CSE, Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Kolenchery, Ernamkulam DT, Kerala, India

    Suvanam Sasidhar Babu

Authors
  1. Bindhia K. Francis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suvanam Sasidhar Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bindhia K. Francis.

Ethics declarations

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.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Education & Training

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Francis, B.K., Babu, S.S. Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. J Med Syst 43, 162 (2019). https://doi.org/10.1007/s10916-019-1295-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1295-4

Keywords

Search

Navigation