Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F283

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 113–117 (2017)

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Abstract. Less than optimal choice of the university department is one of the serious problems Turkish high school students have been suffering. There are a number of potential factors affecting the student's choice of her future profession. Some of these have received attention in the literature, but such studies do not always involve an investigation of the relationship between the factors analyzed and subsequent levels of academic achievement. The present study examines the relationship between the level of academic achievement and the students' abilities, interests and expectations, by using different data mining methods and classifiers, as a preliminary work to develop a system that will guide the student to selecting a career that will be a better match for her in the future. C4.5, SVM, Naive Bayes and MLP algorithms are used for the analysis; 10-fold cross validation and train-test validation are used as models to evaluate the classifiers results. The student feature set is obtained through questionnaires and psychometric tests. The questionnaire and the psychometric test were applied to 210 and 52 students respectively, from the Computer Engineering Department at Cumhuriyet University. The class was labeled either``successful'' or``unsuccessful'' with reference to the grades received by each student in computer engineering courses. The comparisons of various data mining algorithms, different data set results, and models used are presented and discussed. are used as models to evaluate the classifiers results. The student feature set is obtained from questionnaires and psychometric tests. The questionnaire and psychometric test were applied to 210 and 52 students from Computer Engineering Department in Cumhuriyet University. The class label was``successful'' or ``unsuccessful'' according to the computer engineering related lecture grades of each student. The comparisons of data mining algorithms, of different data set results, and models used are presented.

References

1. N. Misran, N. Abd.Aziz, N. Arsad, N. Hussain, W. Zaki and S. Sahuri, "Influencing Factors for Matriculation Students in Selecting University and Program of Study.", Procedia-Social and Behavioural Science, vol. 60, pp. 567-574, 2012.
2. C. BobAlca, O. Tugulea and C. Bradu, "How are the students selecting their bachelor specialization? A qualitative approach.", Procedia Economics and Finance, vol. 15, pp. 894-902, 2014.
3. L. Shu-Hsien, C. Pei-Hui and H. Pei-Yuan, "Data mining techniques and applications âĂŞ a decade review from 2000 to 2011.", Expert Systems with Applications, vol. 39, pp. 11303-11311, 2012.
4. C. Romero and S. Ventura, "Educational data mining: a review of the state of the art.", IEEE Transactions on systems, man, and cybernetics, part C: applications and reviews, vol. 40, pp. 601-618, 2010.
5. I. Witten and E. Frank, Practical Machine Learning Tools and Techniques with Java Implementations., 1st ed. San Francisco, CA, USA: Morgan Kaufmann, 1999.
6. O. Zaine, "Web usage mining for a better web-based learning environment.", in Conference on Advanced Technology for Education, Banff, Alberta, Canada, 2001, pp. 60-64.
7. H. Cha, Y. Kim, S. Park, T. Yoon, Y. Jung and J. Lee, "Learning styles diagnosis based on user interface behaviours for the customization of learning interfaces in an intelligent tutoring system", in 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, 2006, pp. 513-524.
8. W. Hamalainen and M. Vinni, "Comparison of machine learning methods for intelligent tutoring systems.", in 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, 2006, pp. 525-534.
9. P. Pavlik, H. Cen, L. Wun and K. Koedigner, "Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor.", in 1st International Conference on Educational Data Mining, Montreal, Quebec, Canada, 2008, pp. 77-86.
10. R. Agrawal, S. Gollapudi, A. Kannan and K. Kenthapadi, "Data mining for improving textbooks.", ACM SIGKDD Explorations Newsletter, vol. 13, pp. 7-19, 2011.
12. R. Rabbany, M. Takaffoli and O. Zaiane, "Social Network Analysis and Mining to Support the Assessment of On-line Student Participation.", ACM SIGKDD Explorations Newsletter, vol. 13, pp. 20-29, 2011.
13. Z. Pardos, S. Gowda, R. Baker and N. Heffernan, "The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software.", ACM SIGKDD Explorations Newsletter, vol. 13, pp. 37-44, 2011.
14. M. San Pedro, R. Baker, A. Bowers and N. Heffernan, "Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School.", in 6th International Conference on Educational Data Mining;, Memphis, TN., USA, 2013, pp. 177-184.
15. S. Yadav, S. Pal, "A Prediction for Performance Improvement of Engineering Students using Classification.", World of Computer Science and Information Technology Journal , vol. 2, pp. 51-56, 2012.
16. M. Quadri, N. Kalyankar, "Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques.",Global Journal of Computer Science and Technology , vol. 10, pp. 2, 2010.
17. J. Quinlan, C4.5: Programs for Machine Learning, 1st ed. San Francisco, CA, USA: Morgan Kaufmann, 1992.
18. Q. Al-Radaideh, E. Al-Shawakfa, M. Al-Najjar, "Mining student data using decision trees.", in International Arab Conference on Information Technology ;,Yarmouk University, Jordan, 2006.
19. S. Yadav, B. Bharadwaj S. Pal, "Data Mining Applications: A comparative study for predicting students’ performance.",International Journal of Innovative Technology and Creative Engineering , vol. 12, pp. 13-19, 2011.
20. B. Bharadwaj S. Pal, "Data Mining: A prediction for performance improvement using classification.",International Journal of Computer Science and Information Security, vol. 9, pp. 136-140, 2011.
21. C. Cortes, V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.