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Deep Thought

Undergraduate students often struggle to learn optimal logic proof solving strategies in Discrete Math courses, primarily because of the open-ended nature of the domain. Students can, therefore, benefit from personalized tutoring, where they can receive user-adaptive support. Over the past decade, the advancements in the field of intelligent tutoring systems (ITSs) have made it possible to provide personalized tutoring with minimal involvement of a teacher or a human expert. While such tutoring systems have the potential to augment student learning on a large scale, few intelligent tutors are made open source. Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Extensive research has been conducted for 11 years to provide data-driven intelligent tutoring support in Deep Thought. The logic tutor provides adaptive support using data-driven approaches on two levels: problem level, where the tutor decides whether the student should view the next problem as a worked example or they should solve it, and step level, where the tutor decides when an unsolicited partially-worked step should be provided to the student to direct them towards optimal problem-solving strategies. We have found encouraging evidence to support that the intelligent policies in Deep Thought help undergraduate students learn logic. Deep Thought is currently being used in discrete math classes at two universities: North Carolina State University, and the University of North Carolina at Charlotte. Our aim is to make this tutor available to a larger audience so as to contribute to the Computer Science Education community.

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