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The interactions of concepts and problem-solving techniques needed to solve open-ended proof problems are varied, making it difficult to select problems that improve individual student performance. We have developed a system of data-driven ordered problem selection for Deep Thought, a logic proof tutor. The problem selection system presents problem sets of(More)
Deductive logic is essential to a complete understanding of computer science concepts, and is thus fundamental to computer science education. Intelligent tutoring systems with individualized instruction have been shown to increase learning gains. We seek to improve the way deductive logic is taught in computer science by developing an intelligent,(More)
Research shows that expert-crafted worked examples can have a positive effect on student performance. To investigate the potential for data-driven worked examples to achieve similar results, we generated worked examples for the Deep Thought logic tutor, and conducted an experiment to assess their impact on performance. Students who received data-driven(More)
Many tutors offer students reference material or tips that they can access as needed. We have logged data about student use of references with Deep Thought logic tutor which to understand why and how references are used. We find evidence that students use these references in systematic ways that change over the course of the tutor, and can be predic-tive of(More)
We have been incrementally adding data-driven methods into the Deep Thought logic tutor for the purpose of creating a fully data-driven intelligent tutoring system. Our previous research has shown that the addition of data-driven hints, worked examples, and problem assignment can improve student performance and retention in the tutor. In this study, we(More)