Optimization of the Semi-Automated Course Transfer System Using a Hybrid Method With Dynamic Weighting

Course transfer credit evaluation is a crucial process in the educational system that helps students transfer their academic credits in accordance with university curriculum requirements. However, the traditional process faces challenges in comparing course content, as responsible instructors must manually review courses, and the transfer credit evaluation process takes considerable time. To address this challenge, we developed a semi-automated course transfer credit system using a Hybrid Method with Dynamic Weighting (HMDW) that combines Cosine Similarity, Levenshtein Distance, and Jaccard Similarity algorithms, and adjusts the weight of each method based on the characteristics of the transfer credit data. The implementation was divided into 4 phases: 1) synthesizing appropriate components and technologies for system development, 2) comparing the performance and accuracy of the proposed algorithms, 3) developing a semi-automated course transfer credit system using the HMDW in a web application format along with evaluating system quality, and 4) studying user acceptance of the system. The results indicate that the HMDW achieved high performance, with an F1-Score of 0.726 and a Recall of 1.000, ensuring that no eligible course for credit transfer was overlooked. In this system, Recall is the primary success criterion because a missed course that a student is eligible to transfer causes direct harm to the student both academically and financially, whereas a False Positive simply adds one item for faculty review. The reported Accuracy of 58.47% reflects standard Accuracy and should be interpreted alongside the Recall and F1-Score.The developed system can recommend courses and has achieved the highest instructor approval rate. Furthermore, experts evaluated the system quality at the highest level, and users also demonstrated the highest level of system acceptance. Although the system requires a high processing time for transfer credit evaluation, this time is acceptable given the increased accuracy. This demonstrates that combining the strengths of multiple algorithms with a dynamic weighting mechanism can improve course-matching performance compared to using a single method alone. The developed system can be implemented in practice to reduce instructors' workload and increase students' opportunities to receive appropriate, rapid transfer credit evaluation.