Traditional Learning Management Systems (LMS) often fail to adequately capture student engagement and mainly rely on summative outcomes when assessing learning performance. This limitation is particularly critical in programming education, where the learning process itself is as important as the final code output. This paper proposes a Hybrid Intelligent LMS and a Hybrid Assessment Model designed specifically for programming courses. The system integrates an automated code evaluation module (Online Judge), gamified assessment components, and a real-time activity monitoring mechanism. Based on students’ learning mastery (P) and engagement level (A), an overall efficiency index (E) is calculated. To evaluate the effectiveness of the proposed approach, a controlled quasi-experiment was conducted with 82 first-year Software Engineering students. Participants were divided into control and experimental groups and studied using a traditional LMS and the proposed Hybrid LMS, respectively. The collected data were analyzed using Student’s t-test and Pearson correlation analysis. The results show that students in the experimental group achieved significantly higher learning mastery scores (p<0.001) and that strong positive correlations exist between engagement and learning mastery, as well as between engagement and overall efficiency. These findings confirm that the proposed hybrid approach effectively improves student engagement and academic performance in programming education.
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