Abstract
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.
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Biswas, G., Kinnebrew, J.S., Segedy, J.R. (2014). Using a Cognitive/Metacognitive Task Model to Analyze Students Learning Behaviors. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems. AC 2014. Lecture Notes in Computer Science(), vol 8534. Springer, Cham. https://doi.org/10.1007/978-3-319-07527-3_18
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DOI: https://doi.org/10.1007/978-3-319-07527-3_18
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