Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students’ cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students’ knowledge level to fuzzy weights, and using rule-based decision making, delivers adequate learning activities regarding their kind and complexity degree. Background: Tutoring through adaptive learning activities can be a powerful tool to support learners in practical courses, like computer programming. However, published results from pertinent literature are not oriented to the adaptivity of the domain knowledge in terms of the volume, kind, and complexity of the learning activities delivered to students. There is scope for a lot of improvement to this direction. Intended Outcomes: Combining learning theories with adaptive tutoring enhances student-centered learning, promotes student engagement, and improves knowledge acquisition. Application Design: An adaptive tutoring system was developed for supporting undergraduate students in the C# programming language course for an academic semester. The system delivers adaptive learning activities to students’ cognitive skills using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of a RBT for designing the learning material. Findings: At the end of the academic semester, students’ data have been collected and a detailed evaluation was conducted. The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students’ learning outcomes.

中文翻译：

---

通过自适应学习活动进行计算机编程的一种新颖的教学策略

贡献：本文根据修订的Bloom分类法（RBT）的学习理论，介绍考虑了学生的认知技能的，采用自适应学习活动的计算机程序设计的指导。为此，该系统将学生的知识水平转换为模糊的权重，并使用基于规则的决策制定方法，针对他们的种类和复杂程度提供适当的学习活动。背景：通过适应性学习活动进行辅导可以是一个强大的工具，可以为学习计算机课程等实际课程的学习者提供支持。但是，从相关文献中发表的结果并没有针对领域知识的适应性，就提供给学生的学习活动的数量，种类和复杂性而言。这个方向还有很多改进的余地。预期结果： 将学习理论与自适应辅导相结合，可以增强以学生为中心的学习，促进学生的参与度，并改善知识获取。 应用设计：开发了一种自适应辅导系统，以在一个学期为C＃编程语言课程的本科生提供支持。该系统使用基于规则的决策模块中的模糊权重技术和基于RBT的学习理论来设计学习材料，从而为学生的认知技能提供自适应学习活动。发现：在学期末，已收集了学生的数据并进行了详细的评估。结果表明，所提出的方法优于其他在领域知识和学习理论上缺乏适应性的方法，从而大大改善了学生的学习成果。