Discovering Hidden Course Requirements and Student Competences from Grade Data

Mara Houbraken*, Chang Sun, Evgueni Smirnov, Kurt Driessens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a "Data Science and Knowledge Engineering" Bachelor study, Maastricht University.
Original languageEnglish
Title of host publicationAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY, USA
PublisherThe Association for Computing Machinery
Number of pages6
ISBN (Print)978-1-4503-5067-9
Publication statusPublished - 2017

Publication series

SeriesUMAP '17


  • collaborative filtering
  • course competences
  • grade prediction
  • recommender systems


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