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
Scheuer, O., McLaren, B.M.: Educational data mining. In: Encyclopedia of the Sciences of Learning, pp. 1075–1079. Springer, Boston, MA (2012)
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)
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)
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)
Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. (2012). arXiv:1201.3417
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)
Aziz, A.A., Ismail, N.H., Ahmad, F.: MINING STUDENTS’ACADEMIC PERFORMANCE. J. Theor. Appl. Inf. Technol. 53(3) (2013)
Baker, R.S.J.D.: Data mining for education. Int. Encycl. Educ. 7(3), 112–118 (2010)
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)
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)
Borkar, S., Rajeswari, K.: Predicting students academic performance using education data mining. Int. J. Comput. Sci. Mob. Comput. 2(7), 273–279 (2013)
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)
Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S. (eds.): Handbook of Educational Data Mining. CRC press (2010)
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)
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)
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)
Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., Honrao, V.: Predicting students’ performance using ID3 and C4. 5 classification algorithms (2013). arXiv:1310.2071
Ramaswami, M., Bhaskaran, R.: A CHAID based performance prediction model in educational data mining (2010). arXiv:1002.1144
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)
Breiman, L.: Random For. Mach. Learn. 45(1), 5–32 (2001)
Pandey, M., Taruna, S.: A comparative study of ensemble methods for students’ performance modeling. Int. J. Comput. Appl. 103(8) (2014)
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)
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)
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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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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