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

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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.