Analysing the Use of Worked Examples and Tutored and Untutored Problem-Solving in a Dispositional Learning Analytics Context

Dirk Tempelaar, Bart Rienties, Quan Nguyen

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


The identification of students’ learning strategies by using multi-modal data that combine trace data with self-report data is the prime aim of this study. Our context is an application of dispositional learning analytics in a large introductory course mathematics and statistics, based on blended learning. Building on previous studies in which we found marked differences in how students use worked examples as a learning strategy, we compare different profiles of learning strategies on learning dispositions and learning outcome. Our results cast a new light on the issue of efficiency of learning by worked examples, tutored and untutored problem-solving: in contexts where students can apply their own preferred learning strategy, we find that learning strategies depend on learning dispositions. As a result, learning dispositions will have a confounding effect when studying the efficiency of worked examples as a learning strategy in an ecologically valid context.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019)
EditorsH Lane, Susan Zvacek, James Uhomoibhi
Place of PublicationLisbon, Portugal
Number of pages10
ISBN (Print)978-989-758-367-4
Publication statusPublished - Apr 2019

Publication series

SeriesProceedings of the International Conference on Computer Supported Education, CSEDU


  • Blended Learning
  • Dispositional Learning Analytics
  • Learning Strategies
  • Multi-modal data
  • Prediction Models
  • Tutored Problem-Solving
  • Untutored Problem-Solving
  • Worked Examples

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