PREDICTING STUDENT’S PASS/FAIL STATUS IN AN ACADEMIC COURSE USING DEEP LEARNING: A CASE STUDY
In this paper, we propose new classification models based on Deep Learning (DL) to predict a student’s pass/fail status who took the “Probability and Statistics” course at the Computer Engineering Department of Çukurova University. The dataset used to create the models consisted of data related to 132 students and included various variables such as personal information of the students, different quiz and exam scores, conference attendance and overall absence from the course. For comparison purposes, classification models based on three further machine learning classifiers including Multilayer Perceptron (MLP), Cascade Correlation Network (CCN) and Support Vector Machine (SVM) have also been developed. The results show that the DL-based models, in general, exhibit the most successful classification accuracies, ranging from 65.38% to 100.00%. Furthermore, it is seen that inclusion of average quiz, midterm and final exam scores in models have the most improving effect in predicting a student’s pass/fail status in the course.
C. G. Petersen and T. G. Howe, “Predicting Academic Success in Introduction to Computers,” AEDS J., vol. 12, no. 4, pp. 182–191, Jun. 1979.
L. J. Mazlack and L. J., “Identifying potential to acquire programming skill,” Commun. ACM, vol. 23, no. 1, pp. 14–17, Jan. 1980.
J. Konvalina, L. Stephens, and S. Wileman, “Identifying Factors Influencing Computer Science Aptitude and Achievement,” AEDS J., vol. 16, no. 2, pp. 106–112, Jan. 1983.
D. F. Butcher and W. A. Muth, “Predicting performance in an introductory computer science course,” Commun. ACM, vol. 28, no. 3, pp. 263–268, Mar. 1985.
G. E. Evans and M. G. Simkin, “What best predicts computer proficiency?,” Commun. ACM, vol. 32, no. 11, pp. 1322–1327, Nov. 1989.
C. W. Allinson and J. Hayes, “The Cognitive Style Index: A Measure of Intuition-Analysis For Organizational Research,” J. Manag. Stud., vol. 33, no. 1, pp. 119–135, Jan. 1996.
J. W. Henry, M. J. Martinko, and M. A. Pierce, “Attributional style as a predictor of success in a first computer science course,” Comput. Human Behav., vol. 9, no. 4, pp. 341–352, Dec. 1993.
A. T. Chamillard, A. T., Chamillard, and A. T., “Using student performance predictions in a computer science curriculum,” in Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education, 2006, vol. 38, no. 3, p. 260.
J. Bennedsen and M. E. Caspersen, “Optimists have more fun, but do they learn better? On the influence of emotional and social factors on learning introductory computer science,” Comput. Sci. Educ., vol. 18, no. 1, pp. 1–16, Mar. 2008.
J. A. Cottam, S. Menzel, and J. Greenblatt, “Tutoring for retention,” in Proceedings of the 42nd ACM technical symposium on Computer science education, 2011, p. 213.
M. Fire, G. Katz, Y. Elovici, B. Shapira, and L. Rokach, “Predicting Student Exam’s Scores by Analyzing Social Network Data,” Springer, Berlin, Heidelberg, 2012, pp. 584–595.
J. Schmidhuber, Deep Learning in neural networks: An overview, vol. 61. Pergamon, 2015, pp. 85–117.
W. H. Delashmit and L. M. Missiles, “Recent Developments in Multilayer Perceptron Neural Networks,” in Proceedings of the 7th Annual Memphis Area Engineering and Science Conference, 2005.
G. Balazs, “Cascade-Correlation Neural Networks: A Survey,” University of Alberta, 2009.
S. R. Gunn, “Support vector machines for classification and regression,” Analyst, vol. 135, no. 2, pp. 230–267, 2010.