Abstract
During the last decades, higher educational institutes have managed to accumulate a large volume of data about their students’ characteristics and performance. Machine learning techniques offer a first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students’ progress and performance. In this work, we present a two-level classification algorithm for predicting students’ graduation time. The proposed algorithm has two major features. Firstly, it identifies with high accuracy the students at risk of not completing their studies; secondly, it classifies the students based on their expected graduation time. Our preliminary numerical experiments indicate that the proposed algorithm exhibits reliable predictions based on the students’ performance in their courses during the first two years of their studies.
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References
Aha, D.: Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997)
Aud, S., Planty, M., Hussar, W.: Condition of education 2011. Government Printing Office (2011)
Baker, R., Yacef, K.: The state of educational data mining in 2009: a review future visions. J. Educ. Data Mining 1(1), 3–17 (2009)
Barabash, Y.: Collective statistical decisions in recognition. Radio i Sviaz’ (1983)
bin Mat, U., Buniyamin, N., Arsad, P., Kassim, R.: An overview of using academic analytics to predict and improve students’ achievement: a proposed proactive intelligent intervention. In: 2013 IEEE 5th Conference on Engineering Education (ICEED), pp. 126–130. IEEE (2013)
Cohen, W.: Fast effective rule induction. In: International Conference on Machine Learning, pp. 115–123 (1995)
National Research Council: Building a Workforce for the Information Economy. National Academies Press, Washington, D.C. (2001)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29, 103–130 (1997)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explor. Newslett. 11, 10–18 (2009)
Johnson, N.: Three Policies to Reduce Time to Degree. Complete College America, Washington, DC (2011)
Kuncheva, L.: “Change-glasses” approach in pattern recognition. Pattern Recogn. Lett. 14, 619–623 (1993)
Livieris, I.E., Drakopoulou, K., Kotsilieris, T., Tampakas, V., Pintelas, P.: DSS-PSP - a decision support software for evaluating students’ performance. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds.) EANN 2017. CCIS, vol. 744, pp. 63–74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65172-9_6
Livieris, I., Drakopoulou, K., Pintelas, P.: Predicting students’ performance using artificial neural networks. In: Information and Communication Technologies in Education, September 2012
Livieris, I., Drakopoulou, K., Tampakas, V., Mikropoulos, T., Pintelas, P.: Predicting secondary school students’ performance utilizing a semi-supervised learning approach. J. Educ. Comput. Res. (2018)
Livieris, I., Mikropoulos, T., Pintelas, P.: A decision support system for predicting students’ performance. Themes Sci. Technol. Educ. 9, 43–57 (2016)
Musso, M., Kyndt, E., Cascallar, E., Dochy, F.: Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learn. Res. 1(1), 42–71 (2013)
Nagy, H., Aly, W., Hegazy, O.: An educational data mining system for advising higher education students. World Acad. Sci. Eng. Technol. Int. J. Inf. Eng. 7(10), 175–179 (2013)
Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)
Platt, J.: Using sparseness and analytic QP to speed training of support vector machines. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, pp. 557–563. MIT Press, Cambridge (1999)
Livieris, I.E., Drakopoulou, K., Tampakas, V., Mikropoulos, T.A., Pintelas, P.: Predicting secondary school students’ performance utilizing a semi-supervised approach. J. Educ. Comput. Res. 52(2), 448–470 (2018)
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE on Trans. Syst. Man Cybern. - Part C: Appl. Rev. 40(6), 601–618 (2010)
Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: Rumelhart, D., McClelland, J. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Massachusetts, Cambridge, pp. 318–362 (1986)
Saa, A.: Educational data mining & students’ performance prediction. Int. J. Adv. Comput. Sci. Appl. 7(5), 212–220 (2016)
Stetser, M., Stillwell, R.: Public high school four-year on-time graduation rates and event dropout rates: school years 2010–2011 and 2011–2012. First Look. NCES 2014–391. National Center for Education Statistics (2014)
Xu, J., Moon, K., van der Schaar, M.: A machine learning approach for tracking and predicting student performance in degree programs. IEEE J. Sel. Topics Sig. Process. 11, 742–753 (2017)
Xu, L., Suen, A.K.C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)
Yasmeen, A., Wejdan, A., Isra, A., Muna, A.: Predicting critical courses affecting students performance: a case study. Procedia Comput. Sci. 82, 65–71 (2016)
Yassein, N., Helali, R., Mohomad, S.: Predicting critical courses affecting students performance: a case study. J. Inf. Technol. Softw. Eng. 7(5), 1–5 (2017)
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The authors would like to thank the Technological Institute of Western Greece for granting the corresponding data collection utilized in this study.
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Tampakas, V., Livieris, I.E., Pintelas, E., Karacapilidis, N., Pintelas, P. (2019). Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm. In: Tsitouridou, M., A. Diniz, J., Mikropoulos, T. (eds) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2018. Communications in Computer and Information Science, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-20954-4_42
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