Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Aloise-Young PA, Chavez EL (2002) Not all school dropouts are the same: Ethnic differences in the relation between reason for leaving school and adolescent substance use. Psychol Sch 39(5):539–547

Araque F, Roldan C, Salguero A (2009) Factors influencing university drop out rates. Comput Educ 53:563–574

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall, New York

Cano A, Zafra A, Ventura S (2011) An EP algorithm for learning highly interpretable classifiers. In: Proceedings of the 10th international conference on intelligent systems design and applications, ISDA’11, pp 325–330

Cendrowska J (1987) Prism: an algorithm for inducing modular rules. Int J Man-Mach Stud 27(4):349–370

Cohen WW (1995) Fast effective rule induction. In: Twelfth international conference on machine learning, pp 115–123

Dekker GW, Pechenizkiy M, Vleeshouwers JM (2009) Predicting students drop out: a case study. In: International conference on educational data mining

Diosan L, Rogozan A, Pecuchet J (2012) Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36:280–294

Elkan C (2001) The foundations of cost-sensitive learning. In: International joint conference on artificial intelligence, pp 1–6

Fourtin L, Marcotte D, Potvin P, Roger E, Joly J (2006) Typology of students at risk of dropping out of school: description by personal, family and school factors. Eur J Psychol Educ 21(4):363–383

Freund Y, Mason L (1999) The alternating decision tree algorithm. In: Proceedings of the 16th international conference on machine learning, pp 124–133

Gu Q, Cai Z, Zhu L, Huang B (2008) Data mining on imbalanced data sets. In: Proceedings of international conference on advanced computer theory and engineering, pp 1020–1024

Hall MA, Holmes G (2002) Benchmarking attribute selection techniques for data mining. Tech. rep, University of Waikato, Department of Computer Science, Hamilton, New Zealand

Hämäläinen W, Vinni M (2011) Classifiers for educational data mining. Chapman & Hall/CRC, London

Hernández MM (2002) Causas del Fracaso Escolar. In: XIII Congreso de la Sociedad Española de Medicina del Adolescente

Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:63–91

Klösgen W, Zytkow JM (2002) Handbook of data mining and knowledge discovery. Oxford University Press, London

Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence, pp 1137–1143

Kotsiantis S (2009) Educational data mining: a case study for predicting dropout-prone students. Int J Knowl Eng Soft Data Paradig 1(2):101–111

Kotsiantis S, Patriarcheas K, Xenos M (2010) A combinational incremental ensemble of classifiers as a technique for predicting students performance in distance education. Knowl-Based Syst 23(6):529–535

Kotsiantis SB, Pintelas PE (2005) Predicting students’ marks in Hellenic Open University. In: IEEE international conference on advanced learning technologies, pp 664–668

Lykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V (2009) Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput Educ 53:950–965

Márquez-Vera C, Romero C, Ventura S (2011) Predicting school failure using data mining. In: Educational data mining conference

Martínez D (2001) Predicting student outcomes using discriminant function analysis. In: Annual meeting of the research and planning group, pp 163–173

McDonald B (2004) Predicting student success. J Math Teach Learn 1:1–14

Mendez G, Buskirk TD, Lohr S, Haag S (2008) Factors associated with persistence in science and engineering majors: an exploratory study using classification trees and random forests. J Eng Educ 97:57–70

Moseley LG, Mead DM (2008) Predicting who will drop out of nursing courses: a machine learning exercise. Nurse Educ Today 28:469–475

Pan W (2012) The use of genetic programming for the construction of a financial management model in an enterprise. Appl Intell 36:271–279

Parker A (1999) A study of variables that predict dropout from distance education. Int J Educ Technol 1(2):1–11

Quadril MN, Kalyankar NV (2010) Drop out feature of student data for academic performance using decision tree techniques. J Comput Sci Technol 10:2–5

Quinlan JR (1983) C45. Programs for machine learning. Morgan Kaufman, San Mateo

Richards D (2009) Two decades of RDR research. Knowl Eng Rev 24(2):159–184

Slavin RE, Karweit NL, Wasik BA (1994) Preventing early school failure. Allyn & Bacon, Needham Heights

Romero C, Espejo PG, Zafra A, Romero JR, Ventura S (2012, in press) Web usage mining for predicting final marks of MOODLE students. Comput Appl Eng Educ, 1–12

Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Trans Syst Man Cybern 6:601–618

Roy S (2002) Nearest neighbor with generalization. Master’s thesis, University of Canterbury, Christchurch, New Zealand

Deegalla S, Bostrom H (2006) Reducing high-dimensional data by principal component analysis vs random projection for nearest neighbor classification. In: International conference on machine learning and applications, pp 245–250

Superby JF, Vandamme JP, Meskens N (2006) Determination of factors influencing the achievement of the first-year university students using data mining methods. In: Educational data mining workshop, pp 1–8

Tinto V (1987) Leaving college: rethinking the causes and curse of students attrition. University of Chicago Press, Chicago

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

Veitch W (2004) Identifying characteristics of high school dropouts: data mining with a decision tree model. In: Annual meeting of the American educational Research Association, pp 1–11

Wang AY, Newlin MH (2002) Predictors of web-based performance: the role of self-efficacy and reasons for taking an on-line class. Comput Hum Behav J 18:151–163

Wegner L, Flisher AJ, Chikobvu P, Lombard C, King G (2008) Leisure boredom and high school dropout in Cape Town, South Africa. J Adolesc 31:421–431

Whigham PA (1996) Grammatical bias for evolutionary learning. PhD Dissertation, University of New South Wales

Witten IH, Eibe F, Hall MA (2011) Data mining, practical machine learning tools and techniques. Morgan Kaufman, San Mateo