Title

Integrating learning progressions in unsupervised After-School online intelligent tutoring

Abstract

We present the design of a novel conversational intelligent tutoring system, called DeepTutor. DeepTutor is based on cognitive theories of learning, the framework of Learning Progressions proposed by the science education research community, and deep natural language and dialogue processing techniques and principles. The focus of the paper is on the role of Learning Progressions on the design of DeepTutor. Furthermore, we emphasize the role of Learning Progressions in guiding macro-adaptivity in conversational ITSs. We conducted a large-scale, after-school experiment with hundreds of high-school students using DeepTutor. Importantly, these students interacted with the system totally unsupervised, i.e. without any supervision from an instructor or experimenter. Our work so far validates the Learning Progressions theory.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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