Computer programming is a core component of computer science education and is widely recognized as a vital skill for aspiring professionals. Repetitive coding assessments help students improve their programming abilities, but the manual creation and evaluation of these assessments can be time-consuming and challenging for instructors. To address this, we developed an Adaptive Student Assessment System (ASAS) that automatically generates subjective programming questions aligned with CourseLearning Objectives(CLOs) and assists in evaluating student responses. The system was evaluated using a controlled study involving two groups:a test group and a control group. Results demonstrated that the test group consistently outperformed the control group across cognitive assessments, with overall performance improvements of 13.5%. Affective feedback collected through a post-term survey showed a 48.20% higheragreement rate in the test group regarding motivation, clarity, and satisfaction with the assessment process. Teacher evaluations further confirmed the system's effectiveness, with improvements of 23.33% in assessment creation, 26.67% in assessment conduction, and 43.33% in result compilation compared to traditional methods. Teachers reported reduced workload, increased efficiency, and a positive attitude toward long-term adoption of the system. These findings highlight that ASAS not only enhances student engagement and academic performance but also improves instructional efficiency, making it a scalable and effective solution for programming education.
Amin et al. (Mon,) studied this question.