Improving University Operations with Data Mining: Predicting Student Performance
The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1092044Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2053
 A. Bertoncelj, D. Kovač, and R. Bertoncelj,"Success factors and competencies in organisational evolution,” Kybernetes, vol. 38, no. 9, pp. 1508-1517, 2009.
 T. Hernaus, M. Pejić Bach, and V. Bosilj Vukšić,"Influence of strategic approach to BPM on financial and non-financial performance,"Baltic Journal of Management, vol 7, no. 4, pp. 376-396, 2012.
 M. Berry, and G. Linoff, Mastering Data Mining: The Art and Science of Customer Relationship Management,New York: John Wiley & Sons, 2000.
 S. Sumathi, S.N. Sivanandam, Introduction to Data Mining And Its Applications, Netherlands: Springer, 2006.
 B.C. Hardgrave, R.L. Wilson, and K.A. Walstrom,"Predicting graduate student success: A comparison of neural networks and traditional techniques,” Computers & Operations Research, vol.21, no.3, pp. 249-263, 1994.
 I. Zaidah, and R. Daliela,"Predicting students’ academic performance, comparing artifcial neural network, decision tree and linear regression,"in 21st Annual SAS Malaysia Forum Proceedings, Kuala Lumpur, 2007, pp. 1-6.
 V.O. Oladokun, A.T. Adebanjo, and O.E. Charles-Owaba,"Predicting Students’ Academic Performance using Artifcial Neural Network: A Case Study of an Engineering Course,"The Pacifc Journal of Science and Technology, vol. 9,no. 1, pp. 72-79, 2008.
 T. Matković, I. Tomić, and M. Vehovec," Efikasnostnasuprotdostupnosti? O povezanostitroškovaiishodastudiranja u Hrvatskoj,"Revijazasocijalnupolitiku, vol. 17, pp. 2215-237, 2012.
 M. Zekić-Sušac, A. Frajman-Jakšić, and N. Drvenkar," Neuronskemrežeistablaodlučivanjazapredviđanjeuspješnostistudiranja,"Ekonomskivjesnik, no.2, pp. 314.-327, 2009.
 E.J. Shaw, J.P. Marini, and K.D. Mattern,"Exploring the Utility of Advanced Placement Participation and Performance in College Admission Decisions,"Educational and Psychological Measurement,vol. 73, no. 2, pp. 229-253, 2013.
 B. Rienties, and D. Tempelaar,"The role of cultural dimensions of international and Dutch students on academic and social integration and academic performance in the Netherlands,"International Journal of Intercultural Relations,vol. 37, no. 2, pp. 188-201, 2013.
 J. Allen, A. Gregory, and A. Mikami,"Observations of Effective Teacher-Student Interactions in Secondary School Classrooms: Predicting Student Achievement With the Classroom Assessment Scoring System-Secondary,”School Psychology Review,vol. 42 no. 1, pp. 76-98, 2013.
 B. Carnahan, G. Meyer,and L.A. Kuntz,"Comparing statistical and machine learning classifiers: Alternatives for predictive modeling in human factors research,"Human Factors,vol. 45 no. 3, pp. 408-423, 2003.
 T. J. Pleskac, J. Keeney, and S.M. Merritt,"A detection model of college withdrawal," Organizational Behavior and Human Decision Processes, vol. 115,no. 1, pp. 85-98, 2011.
 C. Marquez-Vera, A. Cano, and C. Romero,"Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data,"Applied Intelligence,vol. 38 no. 3, pp. 315-330, 2013.
 B.C. Chiu, and G.I. Web,"Using decision trees for agent modeling: Improving prediction performance,"User Modeling and User-Adapted Interaction,vol. 8 no. 1-2, pp. 131-152, 1998.
 W. Luo, K.M. Aye, and D. Hogan,"Parenting behaviors and learning of Singapore students: The mediational role of achievement goals," Motivation and Emotion,vol. 37,no. 2, pp. 274-285, 2013.
 F. Fischer, J. Schult, and B. Hell,"Sex differences in secondary school success: Why female students perform better,"European Journal of Psychology of Education,vol. 28,no. 2., pp. 529-543, 2013.
 M.P. Levpuscek, M. Zupancic, and G. Socan, "Predicting Achievement in Mathematics in Adolescent Students: The Role of Individual and Social Factors,"Journal of Early Adolescence,vol. 33,no. 4, pp. 523-551, 2013.
 University of Zageb,"Prvikoraci u Bolonjskomprocesu, Sveučilište u Zagrebu,” http://www.unizg.hr/fileadmin/rektorat/dokumenti/bologna/ Bologna.pdf, 2005.
 H. Hand, H. Mannila, and P. Smyth,Principles of Data Mining, Cambridge: MIT Press, 2001.
 N. Bijayananda, and R. Srinivasan,"Using neural networks to predict MBA student success," College Student Journal, vol. 38, no. 1, pp. 143-150, 2004.
 E. Cristobal, G. Pedro, and A. Zafra, "Web usage mining for predicting final marks of students that use Moodle courses,"Computer Applications in Engineering Education,vol. 21,no. 1, pp. 135-146, 2013.
 C. Hsu, S.P.J . Wu, and B. Lin,"The Discovery of Network Structure for E-Learning Participation Prediction: An Integrated Bayesian Networks Approach," Journal of Internet Technologyvol. 14,no. 2, pp. 251-263, 2013.
 S. Lee, and K.C. Lee,"Context-prediction performance by a dynamic Bayesian network: Emphasis on location prediction in ubiquitous decision support environment," Expert Systems With Applications,vol. 39,no. 5, pp. 4908-4914, 2012.
 J. Bošnjović, and V. Trivun,"Academic Mobility in the Western Balkans," Business Systems Research, vol. 4, no. 1, pp. 76-86, 2013.
 E. Shadach, and O. Ganor-Miller,"The role of perceived parental over-involvement in student test anxiety,"European Journal of Psychology of Education, vol. 28,no. 2, pp. 585-596, 2013.
 M .Komarraju, A. Ramsey, and V. Rinella,"Cognitive and non-cognitive predictors of college readiness and performance: Role of academic discipline," Learning and Individual Differences, vol. 24, pp. 103-109, 2013.
 E.P. Bettinger, B.J. Evans, and D.G. Pope,"Improving College Performance and Retention the Easy Way: Unpacking the ACT Exam," American Economic Journal-Economic Policy,vol. 5 no. 2, pp. 26-52, 2013.
 M. Sanchez-Ruiz, S. Mavroveli, and J. Poullis,"Trait emotional intelligence and its links to university performance: An examination," Personality and Individual Differences,vol. 54,no. 5, pp. 658-662, 2013.
 A. Fischbach, U. Keller, and F. Preckel, "PISA proficiency scores predict educational outcomes,"Learning and Individual Differences,vol. 24, pp. 63-72, 2013.
 A. Lizzio, and K. Wilson,"First-year students' appraisal of assessment tasks: implications for efficacy, engagement and performance," Assessment & Evaluation in Higher Education,vol. 38,no. 4, pp. 389-406, 2013.
 Naik, B., andRagothaman, S. "Using Neural Networks to Predict MBA Student Success,” College Student Journal, vol. 38, no. 1, pp. 143-149, 2004.