Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course


  • Ijaz Muhammad Khan 1) College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Malaysia 2) Information Technology Dept. Al-Buraimi University College, Al-Buraimi, Oman
  • Abdul Rahim Ahmad College of Computing and Informatics, Universiti Tenaga Nasional, Kajang, Malaysia
  • Nafaa Jabeur Computer Science Dept. German University of Technology, Muscat, Oman
  • Mohammed Najah Mahdi Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang, Malaysia



student performance prediction, data mining, machine learning, decision tree, introductory programming


The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.




How to Cite

Khan, I. M., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course. International Journal of Emerging Technologies in Learning (iJET), 16(17), pp. 42–59.