Structuring Competency-Based Courses Through Skill Trees

TL;DR

The paper addresses the mismatch between traditional theory-focused course structures and the needs of competency-based CS education by introducing Skill Trees (nodes = skills, edges = prerequisites) and Concept Trees (nodes = concepts) that together form Skill/Concept Graphs. It provides formal definitions, construction guidelines, and planning rules (ordering and grouping) to plan and optimize courses, demonstrated through a university database course. Empirical feedback from teaching staff and students suggests improved course structure and student well-being, with mixed effects on exam performance, and highlights opportunities to digitize exercises for real-time skill tracking. The work advances scalable, automated coaching potential in CS education and lays groundwork for further research on measurable outcomes and adaptive practice.

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.

Figures (3)

• Figure 1: An example Skill Tree (strongly simplified) based around the skill Solve linear equation. It shows how a skill can be split up into subskills, as well as how skills eventually require concepts (discussed in Section \ref{['s:ConceptTree']}).
• Figure 2: An example skill tree based on a skill $A$ with subskills $B$ and $C$, each with their own subskills. A possible teaching order for these skills, based on reversed DFS, is $DEBFGCA$, as shown by the numbers in the figure. An order with inconsistencies (like $B$ before $D$) is obviously inappropriate, but also an ordering based on BFS like $DEFGBCA$ is suboptimal.
• Figure 3: A grouping of skills into blocks, based on an example Skill Tree. Block goals are marked with an asterisk. Block 1 has $C$ as goal, and hence also encompasses $A$ and $B$. Block 2 has $E$ as goal, and hence also teaches $D$. Block 3 focuses on $H$ which also requires $F$ and $G$ (but not $B$ since it's already been taught). Block 4 then focuses on the main learning goal $J$ which also requires skill $I$.