We have proposed a novel application of Markov decision processes (MDPs), a reinforcement learning technique, to automatically generate hints using historical student data. Using this technique, we have modified a an existing, non-adaptive logic proof tutor called Deep Thought with a Hint Factory that provides hints on the next step a student might take. This paper presents the results of our p...

This work explores the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. Generating hints automatically removes a large amount of development time for new tutors, and it also useful for already existing computer-aided instruction systems that lack intelligent feedback. We focus on a series of problems, after which, the control group is known to be 3.5 ...

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 datadriven ordered problem selection for Deep Thought, a logic proof tutor. The problem selection system presents problem sets of expert-determined higher or lower diffi...

Editor’s note: This editorial is part of a series written by editors and co-authored with a senior executive, thought leader, or scholar from a different field to explore new content areas and grand challenges with the goal of expanding the scope, interestingness, and relevance of the work presented in the Academy of Management Journal. The principle is to use the editorial notes as “stage sett...

Automatic problem generation for learning tools can provide the required quantity and variation of problems necessary for an intelligent tutoring system. However, this requires an understanding of problem difficulty and corresponding features of student performance. Our goal is to automatically generate new proof problems in Deep Thought – an online propositional logic learning tool – for indiv...

The Georgia Institute of Technology has been a catalyst for economic growth in the Southeast United States since its founding in 1885. Over the past 30 years, it has become known as one of the top technological universities in the world. As part of a strategic planning effort in mid2009, it sought to strengthen its thought leadership and impact through the implementation of a global innovation ...

Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Within Deep Thought is a data-driven mastery learning system (DDML), which calculates student proficiency based on rule scores weighted by expert-decided weights in order to assign problem sets of appropriate difficulty. In this study, we designed and tested a data-driven proficiency profiler (DDPP) metho...

We describe the “Deep Thought” algorithm, which can, among other things, take a commutator presentation for a finitely generated torsion-free nilpotent group G, and produce explicit polynomials for the multiplication of elements of G. These polynomials were first shown to exist by Philip Hall, and allow for “symbolic collection” in finitely generated nilpotent groups. We discuss various practic...

We propose a novel extension of the encoder-decoder framework, called a review network. The review network is generic and can enhance any existing encoderdecoder model: in this paper, we consider RNN decoders with both CNN and RNN encoders. The review network performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a thought vector after each review s...