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A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques

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Intelligent Communication, Control and Devices

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 989))

Abstract

It is necessary, in education to identify the students who are facing problems in studies so that preventive actions can be taken to improve the students’ performance. Early prediction of student performance is one of Educational Data Mining (EDM) application. In this paper, a set of factors like (academic, demographic, social, and behavior) and their influence on student performance have been studied for early prediction system. The authors have applied seven different machine learning (ML) models on the real data of students of Chitkara University, India. The study shows that to take preventive measures for students at risk not only current performance but the previous academic performance and demographical factors also play an important role. Ensemble model provides the most accurate results.

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References

  1. Scheuer, O., McLaren, B.M.: Educational data mining. In: Encyclopedia of the Sciences of Learning, pp. 1075–1079. Springer, Boston, MA (2012)

    Chapter  Google Scholar 

  2. Farooq, M.S., Chaudhry, A.H., Shafiq, M., Berhanu, G.: Factors affecting students’ quality of academic performance: a case of secondary school level. J. Qual. Technol. Manag. 7(2), 1–14 (2011)

    Google Scholar 

  3. Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)

    Article  Google Scholar 

  4. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4(5–6), 318–331 (2012)

    Article  Google Scholar 

  5. Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. (2012). arXiv:1201.3417

  6. Lu, O.H., Huang, A.Y., Huang, J.C., Lin, A.J., Ogata, H., Yang, S.J.: Applying learning analytics for the early prediction of students’ academic performance in blended learning. J. Educ. Technol. Soc. 21(2), 220–232 (2018)

    Google Scholar 

  7. Aziz, A.A., Ismail, N.H., Ahmad, F.: MINING STUDENTS’ACADEMIC PERFORMANCE. J. Theor. Appl. Inf. Technol. 53(3) (2013)

    Google Scholar 

  8. Baker, R.S.J.D.: Data mining for education. Int. Encycl. Educ. 7(3), 112–118 (2010)

    Article  Google Scholar 

  9. Sachin, R.B., Vijay, M.S.: A survey and future vision of data mining in educational field. In: 2012 Second International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 96–100. IEEE (2012)

    Google Scholar 

  10. Zhang, Y., Oussena, S., Clark, T., Kim, H.: Using data mining to improve student retention in higher education: a case study. In: International Conference on Enterprise Information Systems (2010)

    Google Scholar 

  11. Borkar, S., Rajeswari, K.: Predicting students academic performance using education data mining. Int. J. Comput. Sci. Mob. Comput. 2(7), 273–279 (2013)

    Google Scholar 

  12. Stephens, N.M., Fryberg, S.A., Markus, H.R., Johnson, C.S., Covarrubias, R.: Unseen disadvantage: how American universities’ focus on independence undermines the academic performance of first-generation college students. J. Pers. Soc. Psychol. 102(6), 1178 (2012)

    Article  Google Scholar 

  13. Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S. (eds.): Handbook of Educational Data Mining. CRC press (2010)

    Google Scholar 

  14. Zaffar, M., Hashmani, M.A., Savita, K.S.: Performance analysis of feature selection algorithm for educational data mining. In: 2017 IEEE Conference on Big Data and Analytics (ICBDA), pp. 7–12. IEEE (2017)

    Google Scholar 

  15. Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D.J.: Identifying key factors of student academic performance by subgroup discovery. Int. J. Data Sci. Anal. 1–19 (2018)

    Google Scholar 

  16. Sivakumar, S., Selvaraj, R., Predictive Modeling of students performance through the enhanced decision tree. In: Advances in Electronics, Communication and Computing, pp. 21–36. Springer, Singapore (2018)

    Google Scholar 

  17. Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., Honrao, V.: Predicting students’ performance using ID3 and C4. 5 classification algorithms (2013). arXiv:1310.2071

  18. Ramaswami, M., Bhaskaran, R.: A CHAID based performance prediction model in educational data mining (2010). arXiv:1002.1144

  19. García, E., Romero, C., Ventura, S., De Castro, C.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Model. User-Adapt. Interact. 19(1–2), 99–132 (2009)

    Article  Google Scholar 

  20. Breiman, L.: Random For. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  21. Pandey, M., Taruna, S.: A comparative study of ensemble methods for students’ performance modeling. Int. J. Comput. Appl. 103(8) (2014)

    Google Scholar 

  22. Pandey, M., Taruna, S.: An ensemble-based decision support system for the students’ academic performance prediction. In: ICT Based Innovations, pp. 163–169. Springer, Singapore (2018)

    Google Scholar 

  23. Costa, E.B., Fonseca, B., Santana, M.A., de Araújo, 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)

    Article  Google Scholar 

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Acknowledgments

This research was supported by Chitkara University, Punjab, India. Authors would like to thank Chitkara University faculties and administration who provided academic data of students that greatly assisted the research. We also like to show our gratitude to the Chitkara University students who agreed and provided information to analysis demographic, social and behavior factors that greatly improve the result.

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, India

    Anu Marwaha & Anshu Singla

Authors
  1. Anu Marwaha
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  2. Anshu Singla
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Corresponding author

Correspondence to Anu Marwaha .

Editor information

Editors and Affiliations

  1. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Sushabhan Choudhury

  2. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Ranjan Mishra

  3. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Raj Gaurav Mishra

  4. Department of Electronics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Adesh Kumar

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Marwaha, A., Singla, A. (2020). A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques. In: Choudhury, S., Mishra, R., Mishra, R., Kumar, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_15

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