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.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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