Study path analyses for quality assurance and support of study planning

Informatik-Spektrum, vol. 47, pp. 97–104

Abstract

Abstract Utilizing student lifecycle data provided by campus management systems yields the opportunity to conduct study path analyses. Methods of artificial intelligence (AI) and data science can be used to analyze study paths, identify indicators for success, and gain insights into problems and issues of student cohorts following different study paths. Meanwhile, AI can also be used to support students through informed study planning. This article presents the project AIStudyBuddy with its focus on utilizing rule-based AI and process mining to support study planning and cohort monitoring. The concept of a reference architecture and data model for study path analytics as well as details on the development of the two user applications, StudyBuddy for students and BuddyAnalytics for study program designers, are presented. By exploring how AI and process mining can be applied in the scope of the two applications, the article addresses the question of how AI can be used for quality assurance in study planning and student cohort monitoring.

Authors 3

1. Rene Roepke Aachen Learning Technologies Research Group

   RWTH Aachen University · TU Wien

   Affiliation as printed

   Learning Technologies Research Group, RWTH Aachen University, Ahornstr. 55, 52074, Aachen, Germany

   Learning Technologies and eDidactics, TU Wien, Favoritenstr. 9–11, 1030, Vienna, Austria

2. Sven Judel corresponding Aachen Learning Technologies Research Group

   RWTH Aachen University

   Affiliation as printed

   Learning Technologies Research Group, RWTH Aachen University, Ahornstr. 55, 52074, Aachen, Germany

3. Ulrik Schroeder Aachen Learning Technologies Research Group

   RWTH Aachen University

   Affiliation as printed

   Learning Technologies Research Group, RWTH Aachen University, Ahornstr. 55, 52074, Aachen, Germany