International Journal of Education and Management Engineering(IJEME)
ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)
Published By: MECS Press
IJEME Vol.5, No.5, Oct. 2015
A Decision Tree Approach for Predicting Students Academic Performance
Full Text (PDF, 280KB), PP.12-19
This research is on the use of a decision tree approach for predicting students' academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.
Cite This Paper
Kolo David Kolo, Solomon A. Adepoju, John Kolo Alhassan,"A Decision Tree Approach for Predicting Students Academic Performance", International Journal of Education and Management Engineering(IJEME), Vol.5, No.5, pp.12-19, 2015.DOI: 10.5815/ijeme.2015.05.02
Osmanbegovic E., Suljic M. "Data mining approach for predicting student performance" Economic Review- Journal of Economics and Business. Volume 10(1) (2012)
Behrouz, M, Karshy, D, Korlemeyer G, Punch, W. "Predicting student performance: an application of data Mining methods with the educational web-based system" Lon-capa. 33rd ASEE/IEEE Frontiers in Education Conference. Boulder C.O. USA, (2003).
Bekele, R., Menzel, W. "A bayesian approach to predict performance of a student (BAPPS): A Case with Ethiopian Students". Journal of Information Science (2013).
Bhardwaj, K., Pal, S "Data Mining: A prediction for performance improvement using classification". International Journal of Computer Science and Information Security. Volume 9(4). (2011).
Romero, C, Ventura, S. "Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C" Applications and Reviews. Volume 40(6) (2012).
Bae, E., Bailey, J: "COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity". Proceedings of the Sixth International Conference on Data Mining. Pp. 53 – 62. (2006).
Kovacic, Z. "Early prediction of student success: Mining student enrollment data" Proceedings of Informing Science & IT Education Conference. (2010).
Cortez P, Silva A. Using data mining to predict Secondary school student performance. Journal of information science Volume 2(6). (2013).
Ahmed, A. B. E, Ibrahim S. E.. "Data Mining: A prediction for Student's Performance Using Classification Method." World Journal of Computer Application and Technology Volume 2(2) (2014).
Sembiring S, Zarlis, M, Hartama, D. Ramliana S, Elvi W. "Prediction of student academic performance by an application of data mining techniques." International Conference on Management and Artificial Intelligence IPEDR Volume.6, (2011).
Surjeet K, Yadav, Bharadwaj, B. Pal B." Data Mining Applications: A comparative Study for Predicting Student's performance." International journal of innovative technology & creative engineering. Volume 1(12). (2012).
Mladen D., Mirjana P. B., Vanja Š., "Improving University Operations with Data Mining: Predicting Student Performance", International Journal of Social, Behavioral, Educational, Economic and Management Engineering Volume 8(4), 2014.
Meltem, D. "Gender difference in academic performance in a large public university in Turkey". Economic Research center working papers in economics. 4(17). Pp. 22-23, (2004).
Abubakar, R. B. and Oguguo, O. D. "Age and gender as predictors of academic achievements of College mathematics and science students." Proceedings of the International Conference of teaching, learning and change. International Association of Teaching and learning. (2011).
Nnamani, C. N, Dikko, H. G and Kinta, L. M. "Impact of students' financial strength on their academic performance: Kaduna Polytechnic experience". African Research Review 8(1), (2014).
Ogunde A.O., Ajibade D.A. "A data Mining System for Predicting University Students F=Graduation Grade Using ID3 Decision Tree approach", Journal of Computer Science and Information Technology, Volume 2(1) (2014).
Ryan S.J.D. Baker, Kalina Yacef. "The State of Educational Data Mining in 2009: A Review and Future Visions", Journal of Educational Mining, Volume 1(2009).
Undavia, J. N., Dolia, P. M.; Shah, N. P. "Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment". International Journal of Computer Applications; Volume 74 (21), (2013).