Simulating Preservice Teachers’ Information-Seeking Behaviors While Learning with an Intelligent Web Browser

ID: 51061 Type: Brief Paper
  1. Eric Poitras and Negar Fazeli, University of Utah, United States

Tuesday, March 7 3:00 PM-4:00 PM Location: Bonnell View on map

Presider: Mamta Shah, Drexel University, United States

Abstract: Learner models enable open-ended learning environments (OELEs) to adapt instruction to the specific needs of different learners. Simulation-based methods allow researchers to reproduce and model learner behaviors to evaluate and improve the adaptive capabilities of OELEs in a manner that would not otherwise be possible in a classroom or laboratory setting. We discuss a computer simulation to study the impact of a network-based recommender system towards preservice teachers’ information-seeking behaviors. The findings show that the system is 76% more likely to recommend the most useful resource rather than other comparison documents. We discuss the broader implications for adaptive OELEs that recommend online resources to teachers based on their specific needs and interests.


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