Type: Taxonomy Development
Purpose: To support the definition of learning paths, adaptable to student performance, it is useful to have a large set of exercises, organized according to a taxonomy that includes different dimensions and parameters relevant to the choice of appropriate exercises at any moment. To present this taxonomy is the main objective of this paper.
Findings: The taxonomy allowed the authors to make a database of exercises (continually evolving). They classify any exercise based on: topics (vectors arrays), complexity (algorithm complexity, code complexity, cognitive effort), and levels (Bloom's taxonomy, exercise levels- beginner, intermediate, advanced). The taxonomy could promote student motivation.
Recommendations: Instead of insisting that a student should solve some problem or be given a solution, it is better to propose different exercises so that the student can progress and eventually solve the original exercise. The taxonomy can help develop faster learning paths for better students, avoiding their boredom of going through a series of exercises too easy for their level. The taxonomy will be even more useful if there is a tool that automatically proposes exercises to be done by a student.