Learning Performance in Adaptive Learning Systems: A Case Study of Web Programming Learning Recommendations - Ling, Hsiao-Chi; Chiang, Hsiu-Sen (Frontiers in Psychology, 2022)
Students often face challenging while learning computer programming because programming languages' logic and visual presentations differ from human thought processes. If the course content does not closely match learners' skill level, the learner cannot follow the learning process resulting in frustration, low learning motivation or learning abandonment. This research proposes a web programming learning recommendation system to provide students with personalized guidance and step-by-step learning planning. The system contains front-end and back-end web development instruction. It can create personalized learning paths to help learners achieve a sense of accomplishment. The system can help learners build self-confidence and improve learning effectiveness. In study 1, the recommendation system was developed based on the personal data and feedback of 41 professional web design engineers. The system uses C4.5 decision tree methods to develop a programming learning recommendation model, which can provide appropriate learning recommendations and establish personalized learning paths. The test group included 13 beginner programmers. After four weeks' programming instructions in front-end and back-end web development, the learners were interviewed to understand their preferences and learning effectiveness. The results show that the effectiveness of the recommendation system is acceptable. In study 2, an online real-time feedback and adaptive instruction platform is developed, which is different from the past adaptive curriculums mostly using the Internet platform and only the submitted assignments to determine the newly recommended learning process for students. The study found that the students’ learning performance in adaptive instruction group is better than the students’ performance in the fixed instruction group.