• Fatih Abut Department of Computer Engineering, Adana Science and Technology University
  • M. Can Yüksel Department of Computer Engineering, Çukurova University
  • M. Fatih Akay Department of Computer Engineering, Çukurova University
  • Shahaboddin Daneshvar Department of Computer Engineering, Çukurova University
Keywords: Deep learning, machine learning, classification, academic performance


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.


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How to Cite
Abut, F., Yüksel, M. C., Akay, M. F., & Daneshvar, S. (2018). PREDICTING STUDENT’S PASS/FAIL STATUS IN AN ACADEMIC COURSE USING DEEP LEARNING: A CASE STUDY. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), 4(1), 87-91. Retrieved from
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