Using a Cognitive/Metacognitive Task Model to Analyze Students Learning Behaviors

  • Gautam Biswas
  • John S. Kinnebrew
  • James R. Segedy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


Adapting to learners’ needs and providing useful, individualized feedback to help them succeed has been a hallmark of most intelligent tutoring systems. More recently, to promote deep learning and critical thinking skills in STEM disciplines, researchers have begun developing open-ended learning environments that present learners with complex problems and a set of tools for learning and problem solving. To be successful in such environments, learners must employ a variety of cognitive skills and metacognitive strategies. This paper discusses a framework that combines a theory-driven, top-down approach with a bottom-up, pattern-discovery approach for analyzing learning activity data in these environments. Combining these approaches allows for more complex qualitative and quantitative interpretation of a student’s cognitive and metacognitive abilities. The results of this analysis provide a foundation for developing performance- and behavior-based learner models in conjunction with adaptive scaffolding mechanisms to promote effective, personalized learning experiences.


metacognition theory-driven top-down analysis pattern-driven bottom-up analysis effectiveness measures pattern mining adaptivity tutoring 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gautam Biswas
    • 1
  • John S. Kinnebrew
    • 1
  • James R. Segedy
    • 1
  1. 1.ISIS/EECS DepartmentVanderbilt UniversityNashvilleUSA

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