Development of Machine Learning Models to Predict Student Performance in Computer Literacy Courses
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This paper reports on a study carried out to build machine learning models for the purpose of predicting student performance in a second course of a two-course sequence, based on performance in various aspects of the first course’s exam. Detailed data is collected from the first course’s exam, which is a multiple choice exam, using an Optical Mark Recognition scanner. This data is then pre-processed and used to build machine learning models. Several machine learning models are experimented with, yielding excellent results for predicting Pass or Fail (greater than 93% accuracy), validating our hypothesis that our approach can be used to help students with preventative measures in order to increase pass rates in the courses concerned.
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B.K. Baradwaj, S. Pal, Mining Educational Data to Analyze Students’ Performance, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 2 n. 6, June 2011, pp. 63–69.
C. Schaffer, A conservation law for generalization performance, Proceedings of the 11th International Conference on Machine Learning, 1994, New Brunswick, USA.
S. Kotsiantis, C. Pierrakeas, P. Pintelas, Predicting Students’ Performance in Distance Learning Using Machine Learning Techniques, Applied Artificial Intelligence (AAI), vol. 18 n. 5, May-June 2004, pp. 411–426.
S. T. Hijazi, R. S. M. Naqvi, Factors affecting student’s performance: A Case of Private Colleges, Bangladesh e-journal of Sociology, vol. 3 n. 1, January 2006, pp. 90–100.
B. K. Bhardwaj, S. Pal, Data Mining: A prediction for performance improvement using classification, International Journal of Computer Science and Information Security (IJCSIS), vol. 9 n. 4, April 2011, pp. 136–140.
U. K. Pandey, S. Pal, Data Mining: A prediction of performer or underperformer using classification, International Journal of Computer Science and Information Technology (IJCSIT), vol. 2 n. 2, 2010, pp. 686 – 690.
S. K. Yadav, B. K. Bharadwaj, S. Pal, Data Mining Applications: A comparative study for predicting students’ performance, International Journal of Innovative Technology and Creative Engineering (IJITCE), vol. 1 n. 12, December 2011, pp. 13–19.
S. Pal, Mining Educational Data to Reduce Dropout Rates of Engineering Students, International Journal of Information Engineering and Electronic Business (IJIEEB), vol. 4 n. 2, April 2012, pp. 1–7.
B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, W. Punch, Predicting student performance: An application of data mining methods with the educational web-based system LON-CAPA, Proceedings of the 33rd ASEE/IEEE Frontiers in Education Conference, November 5-8, 2003, Boulder, USA.
Q. A. AI-Radaideh, E. W. AI-Shawakfa, M. I. AI-Najjar, Mining student data using decision trees, International Arab Conference on Information Technology (ACIT'2006), 2006, Yarmouk University, Jordan.
M. Falakmasir, H. Jafar, Using educational data mining methods to study the impact of virtual classroom in elearning, Proceedings of the 3rd International Conference on Educational Data Mining, June, 2010, Pittsburgh, USA.
S. K. Yadav, S. Pal, Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification, World of Computer Science and Information Technology Journal (WCSIT), vol. 2 n. 2, 2012, pp. 51 – 56.
M. Abdous, W. He, C.-J. Yen, Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade, Educational Technology & Society, vol. 15 n. 3, July 2012, pp. 77 – 88.
T.O. Ayodele, Types of Machine Learning Algorithms, In Y. Zhang (Ed.), New Advances in Machine Learning, (Croatia: In-Tech, 2010, 19-48).
C. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).
Hicham, A., Bouhorma, M., Abdellah, E., Integration of Fuzzy Delphi, Fuzzy Clustering and Back-Propagation Neural Networks with Adaptive Learning Rate for Sales Forecasting in ERP Architecture, (2013) International Journal on Information Technology (IREIT), 1 (1), pp. 11-21.
Anguraj, K., Padma, S., A Precise Facial Paralysis Degree Evaluation with Severity Classification Using Image Processing and Neural Network, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 569-576.
Anderson, G., Masizana, A., Mpoeleng, D., An Exact and Inexact Data Matching Approach for Saving Time and Preventing Errors in Processing of Student Exam Results at the University of Botswana, (2013) International Journal on Information Technology (IREIT), 1 (3), pp. 179-185.
Tang, Z., Tang, B., Han, Y., Lu, Y., Luo, X., Tian, W., Wang, H., A New Pattern Recognition Method Based on Nonlinear Support Vector Machine, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 262-266.
S. Marsland, Machine Learning: An Algorithmic Perspective (CRC Press, 2009).
University of Waikato Machine Learning Group, WEKA: Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/index.html (Accessed June 2013).
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