Advertisement

Survey on Predicting Educational Trends by Analyzing the Academic Performance of the Students

  • Selvaprabu Jeganathan
  • Arunraj LakshminarayananEmail author
  • Aranganathan Somasundaram
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Driving decisions using data is being followed in most of the business units. Industries and Institutions use complex computational techniques to improve and identify their growth trend by using Business Intelligence techniques. Adoption of data mining in educational system(s) is fairly new, data mining techniques can detect patterns from the educational system data which might be continuous or discrete and drive a prediction rule to identify the academic performance of students. Our study is focused on exploring various factors affecting educational performance of undergraduate students based on the data from their course activities. The survey explores various data mining techniques applied on educational data and advocates to integrate the learning management system with the data pattern models identified.

Keywords

Educational data mining Education Data mining Mining algorithms Learning management system 

References

  1. 1.
    Bakhshinategh, B., Zaiane, O.R., ElAtia, S., Ipperciel, D.: Educational data mining applications and tasks: a survey of the last 10 years. Educ. Inf. Technol. 23, 537–553 (2018)Google Scholar
  2. 2.
    Monjurul Alom, B.M., Courtney, M.: Educational data mining: a case study perspectives from primary to university education in Australia. Int. J. Inf. Technol. Comput. Sci. 10(2), 1–9 (2018)Google Scholar
  3. 3.
    Costa, E.B., Fonseca, B., Santana, M.A., de Araujo, F.F., Rego, J.: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput. Hum. Behav. 73, 247–256 (2017)Google Scholar
  4. 4.
    Hussain, S., Dahan, N.A.-A., Ba-Alwib, F.M., Ribata, N.: Educational data mining and analysis of students academic performance using WEKA. Indonesian J. Electr. Eng. Comput. Sci. 9(2), 447–459 (2018)Google Scholar
  5. 5.
    Asif, R., Merceron, A., Ali, S.A., Haider, N.G.: Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)Google Scholar
  6. 6.
    Almarabeh, H.: Analysis of students’ performance by using different data mining classifiers. J. Modern Educ. Comput. Sci. 8, 9–15 (2017)Google Scholar
  7. 7.
    Ariouat, H., Hicheur-Cairns, A., Barkaoui, K., Akoka, J.: A two-step clustering approach for improving educational process model discovery. In: International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 38–43, August 2016Google Scholar
  8. 8.
    Rubiano, S.M.M., Garcia, J.A.D.: Analysis of data mining techniques for constructing a predictive model for academic performance. IEEE Latin Am. Trans. 14(6), 2783–2788 (2016)Google Scholar
  9. 9.
    Pena-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)Google Scholar
  10. 10.
    Archer, E., Chetty, Y.B., Prinsloo, P.: Benchmarking the habits and behaviors of successful students: a case study of academic-business collaboration. Int. Rev. Res. Open Distrib. Learn. 15(1) (2014)Google Scholar
  11. 11.
    Rakesh, A., Badal, D.: Mining association rules to improve academic performance. IJCSMC 3(1), 428–433) (2014)Google Scholar
  12. 12.
    Lorraine, D.P., Pamela, Q., Peter, J.S.: Exploring the factor structure of the career EDGE employability development profile. Educ. Training Monit. 56(4), 303–313 (2014)Google Scholar
  13. 13.
    Vanhercke, D., De Cuyper, N., Peeters, E., De Witte, H.: Defining perceived employability: a psychological approach. Pers. Rev. - Emerald Insight 43, 4592–4604 (2014)Google Scholar
  14. 14.
    Khan, M., Zahiduzzaman, A.K.M., Quasem, M.N., Rahman, R.M.: Geospatial data mining on education indicators of Bangladesh. IJCA 20(1), 10–22 (2013)Google Scholar
  15. 15.
    Torenbeek, M., Jansen, E., Suhre, C.: Predicting undergraduates’ academic achievement: the role of the curriculum, time investment and self-regulated learning. Stud. High. Educ. 38(9), 1393–1406 (2013)Google Scholar
  16. 16.
    Dejeager, K., Goethals, F., Giangreco, A., Mola, L., Baesens, B.: Gaining insight into student satisfaction using comprehensible data mining techniques. Eur. J. Oper. Res. 218, 548–562 (2012)Google Scholar
  17. 17.
    Osmanbegovic, E., Suljic, M.: Data mining approach for predicting student performance economic review. J. Econ. Bus. X(1), 3–12 (2012)Google Scholar
  18. 18.
    Balakrishnan, K., David, J.M.: Significance of classification techniques in prediction of learning disabilities. Int. J. Artif. Intell. Appl. 1, 111–120 (2010)Google Scholar
  19. 19.
    Thakar, P., Mehta, A., Manisha, S.: Performance analysis and prediction in educational data mining: a research travelogue. Int. J. Comput. Appl. (2015). ISSN 0975–8887Google Scholar
  20. 20.
    Saranya, S., Ayyappan, R., Kumar, N.: Student progress analysis and educational institutional growth prognosis using data mining. Int. J. Eng. Sci. Res. Technol. 3, 1982–1987 (2014)Google Scholar
  21. 21.
    Hicheur Cairns, A., et al.: Towards custom-designed professional training contents and curriculums through educational process mining. In: The Fourth International Conference on Advances in Information Mining and Management, IMMM 2014 (2014)Google Scholar
  22. 22.
    Finch, D.J., Hamilton, L.K. Baldwin, R., Zehner, M.: An exploratory study of factors affecting undergraduate employability. Educ. + Training 55(7), 681–704 (2013)Google Scholar
  23. 23.
    Potgieter, I., Coetzee, M.: Employability attributes and personality preferences of postgraduate business management students. SA J. Ind. Psychol. 39(1), 01–10 (2013)Google Scholar
  24. 24.
    Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.: Handbook of Educational Data Mining. Taylor & Francis, New York (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Selvaprabu Jeganathan
    • 1
  • Arunraj Lakshminarayanan
    • 1
    Email author
  • Aranganathan Somasundaram
    • 1
  1. 1.B.S. Abdur Rahman Crescent Institute of Science and TechnologyChennaiIndia

Personalised recommendations