MetaTutor: Analyzing self-regulated learning in a tutoring system for biology

Roger Azevedo, Amy Witherspoon, Arthur Graesser, Danielle McNamara, Amber Chauncey, Emily Siler, Zhiquiang Cai, Vasile Rus, Mihai Lintean

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

We report preliminary data of an initial laboratory study examining the effectiveness of self-regulated learning (SRL) training versus no training on learners' ability to deploy SRL processes and learn about the circulatory system with MetaTutor. MetaTutor is an intelligent tutoring system (ITS) designed to train and foster learners' SRL processes while learning about several complex human body systems. We used a mixed methodology approach and include the results of a subset of the participants (N=30) whose product and process data we have analyzed. Overall, the results indicate that the SRL training group significantly outperformed the control group.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages635-637
Number of pages3
Edition1
ISBN (Print)9781607500285
DOIs
StatePublished - Jan 1 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume200
ISSN (Print)0922-6389

Keywords

  • Biology
  • Hypermedia
  • ITS
  • Metacognition
  • Scaffolding
  • Self-regulated learning

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Azevedo, R., Witherspoon, A., Graesser, A., McNamara, D., Chauncey, A., Siler, E., Cai, Z., Rus, V., & Lintean, M. (2009). MetaTutor: Analyzing self-regulated learning in a tutoring system for biology. In Frontiers in Artificial Intelligence and Applications (1 ed., pp. 635-637). (Frontiers in Artificial Intelligence and Applications; Vol. 200, No. 1). IOS Press. https://doi.org/10.3233/978-1-60750-028-5-635