Machine Learning-Enabled Personalization of Programming Learning Feedback

Acquiring programming skills is daunting for most learners and is even more challenging in heavily attended courses. This complexity also makes it difficult to offer personalized feedback within the time constraints of instructors. This study offers an approach to predict programming weaknesses in each learner to provide appropriate learning resources based on machine learning. The machine learning models selected for training and testing and then compared are Random Forest, Logistic Regression, Support Vector Machine, and Decision Trees. During the comparison based on the features of prior knowledge, time spent, and GPA, Logistic Regression was found to be the most accurate. Using this model, the programming weaknesses of each learner are identified so that personalized feedback can be given. The paper further describes a controlled experiment to evaluate the effectiveness of the personalized programming feedback generated based on the model. The findings indicate that learners receiving personalized programming feedback achieve superior learning outcomes than those receiving traditional feedback. The implications of these findings are explored further, and a direction for future research is suggested.

@article{Alshammari2025,
  title     = {Machine Learning-Enabled Personalization of Programming Learning Feedback},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mohammad T. Alshammari},
  doi       = {10.14569/IJACSA.2025.01602108},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602108}
}