The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student’s competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a mal-adaptive order.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalInteractive Learning Environments
StateAccepted/In press - Jun 7 2016


  • Adaptive learning
  • adaptive test items selection
  • Bayesian Knowledge Tracing
  • question generation
  • student modeling

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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