Recommendation of Instructional Video Clips for HTML Learners Based on the ID3 Algorithm

This study proposes a recommendation system for video clips based on the ID3 algorithm. The system was developed using the Python programming language. There were totally seven videos instructing how to design a webpage. The videos were micro lectures, so the length of each was only 10 minutes for each learning unit. When the system diagnoses the prior knowledge of the students, the recommendation system will decide the starting learning node for the individual students. The system was used in remedial instruction of webpage design. The system checked which individual students weaknesses required strengthening after one round of self-learning. The results showed that the remedial instruction recommended by the system was successful enough to significantly improve the students learning effectiveness. The results indicated that the self-efficacy of the low-achievement students achieved a level as high as that of the high-achievement students after the students employed the proposed approach to enhancing their webpage design learning and received the required learning videos. Because the low-achievement students received more remedial instruction learning materials, they had higher external cognitive loads. However, because the difficulty level of the learning materials which were recommended to the individuals conformed to their prior knowledge, the internal cognitive loads of the high-achievement students were not significantly different from those of the low-achievement students.