Can Students' Computer Programming Learning Motivation and Effectiveness Be Enhanced by Learning Python Language? A Multi-Group Analysis

Do Flipped Learning and Adaptive Instruction Improve Student Learning Outcome? A Case Study of a Computer Programming Course in Taiwan

Flipped learning could improve the learning effectiveness of students. However, some studies have pointed out the limitations related to flipped classrooms because the content of the flipped course does not vary according to the needs of the students. On the other hand, adaptive teaching, which customizes the learning mode according to the individual needs of students, can make up for some of the shortcomings of flipped teaching. This study combines adaptive teaching with flipped teaching and applies it to face-to-face classroom activities. The purpose of this research is to explore whether the implementation of flipping and adaptive learning in a computer programming course can improve the learning effectiveness of students. The experimental subjects of this study are the sophomore students in the Department of Information Management. The flipped classroom with adaptive instruction has been realized in the limited course time. This study uses questionnaires to collect pre- and post-test data on the "learning motivation" of students. The learning effectiveness was evaluated based on the students' previous programming course (C language) and the semester scores of this course. Research results show that the post-test "learning motivation" has improved overall compared with the pre-test, and the learning effect is significant. The results of this research not only prove the effectiveness of modern teaching theories in programming courses but also lay the foundation for future teaching design.

Learning Performance in Adaptive Learning Systems: A Case Study of Web Programming Learning Recommendations

Students often face challenges 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 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 instructions. 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 to provide appropriate learning recommendations and establish personalized learning paths. The test group included 13 beginner programmers. After 4 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, online real-time feedback and adaptive instruction platform is developed, which is different from the past adaptive curriculums mainly 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 the adaptive instruction group is better than those in the fixed instruction group.