Predicting Student Performance on Post-requisite Skills Using Prerequisite Skill Data: An alternative method for refining Prerequisite Skill Structures

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

Prerequisite skill structures have been closely studied in past years leading to many data-intensive methods aimed at refining such structures. While many of these proposed methods have yielded success, defining and refining hierarchies of skill relationships are often difficult tasks. The relationship between skills in a graph could either be causal, indicating a prerequisite relationship (skill A must be learned before skill B), or non-causal, in which the ordering of skills does not matter and may indicate that both skills are prerequisites of another skill. In this study, we propose a simple, effective method of determining the strength of pre-to-post-requisite skill relationships. We then compare our results with a teacher-level survey about the strength of the relationships of the observed skills and find that the survey results largely confirm our findings in the data-driven approach.

Citation: Seth Adjei, Anthony Botelho and Neil Heffernan (2016). "Predicting Student Performance on Post-requisite Skills Using Prerequisite Skill Data: An alternative method for refining Prerequisite Skill Structures". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.