Using causal inference in learning processes to predict student proficiency

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The search for causal relations from observational data is an open problem that spans many fields. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is always desirable. In computer science, integrated development environments (IDE) offer students many features promising to instill the necessary competency skills for migration to industry. In this chapter, current causal discovery methods are applied to investigate a causal link between IDE consultations and student competency which is measured by the number of issues at the end of the coding timeline. The coding activities of students were timestamped over the coding lifetime. Due to the nature of the data, the authors were able to test for causality using methods for static and methods for dynamic data. The authors show the presence of a causal link between IDE consults and student improvement. In addition, they show the time it takes to see the effect of a system consult.

Original languageEnglish
Title of host publicationPerspectives on Learning Analytics for Maximizing Student Outcomes
Number of pages20
ISBN (Electronic)9781668495285
Publication statusPublished - 24 Oct. 2023


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