Recognizing Opportunities for Mixed-Initiative Interactions based on the Principles of Self-Regulated Learning

Jurika Shakya, Samir Menon, Liam Doherty, Mayo Jordanov, Vive Kumar

Successful mixed initiative systems employ mechanisms that explicitly recognize opportunities for initiatives among the system and the users. In this paper, we propose a theory based framework, founded on the principles of Self Regulated Learning, that recognizes strategies and tactics learners used in their interactions. These interactions are observed from within gStudy, a learning tool that students use as part of learning activity. Production rules encode SRL-specific knowledge in an OWL-based formal ontology and JESS is used as an inference engine to recognize strategies and tactics used by learners in specific reading and problem-solving activities. Using such inferences we demonstrate how the system recognizes opportunities for mixed-initiative interactions to guide learners who veer away from optimal SRL strategies.


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