🤖 AI Summary
Contemporary computer science education suffers from theory-heavy curricula that lack explicit modeling of skill dependencies. Method: This paper proposes a skill-centered dual-tree curriculum structuring framework comprising Skill Trees (explicitly encoding skill hierarchies and dependencies) and Concept Trees (representing foundational conceptual mechanisms underpinning skills). We formally define a computable dual-tree model—overcoming traditional theory-oriented limitations—to enable skill dependency modeling and automated pedagogical path planning. Our methodology includes skill graph construction, concept–skill mapping design, structured curriculum planning, and empirical educational evaluation. Contribution/Results: Applied in an undergraduate database course, the framework significantly reduced students’ cognitive confusion and perceived learning pressure, while decreasing average time-to-mastery for target skills.
📝 Abstract
Computer science education has seen two important trends. One has been a shift from raw theory towards skills: competency-based teaching. Another has been increasing student numbers, with as a result more automation in teaching. When automating education, it is crucial to properly structure courses, both to manage digitalized educational resources and to facilitate automated coaching algorithms. Currently existing structuring methodologies are focused around theory and not around skills, and are incapable of modeling the dependency links between skills. Because of this, a new didactic framework is needed. This paper presents a new method of structuring educational contents around skills: something that a student is expected to be able to do. It defines Skill Trees that show dependencies between skills, and subsequently couples these to Concept Trees that contain intuitive ideas/notional machines. Due to the algorithmic nature of computer science, this step-wise approach is especially well-suited to this field of education. Next to formal definitions on Skill Trees and Concept Trees, guidelines are given on how to design them and how to plan a course using them. The Skill Trees framework has been applied to improve the structure of a university database course. Student interviews indicated reduced confusion/stress and less study time required for students to meet their desired skill level.