Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education

S Tacoma, Paul Drijvers, Johan Jeuring

Research output: Contribution to journalArticleAcademicpeer-review


Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first-year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.

Original languageEnglish
Pages (from-to)319-332
Number of pages14
JournalJournal of Computer Assisted Learning
Issue number2
Publication statusPublished - Apr 2021


  • Domain reasoner
  • Feedback
  • Inspectable student models
  • Intelligent tutoring systems
  • Statistics education
  • domain reasoner
  • feedback
  • inspectable student models
  • intelligent tutoring systems
  • statistics education


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