Effects of Automatically Generated Hints on Time in a Logic Tutor

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 times more likely to cease logging onto an online tutor when compared to the group who were given hints. We found a consistent trend in which students without hints spent more time on problems when compared to students that were provided hints. Exploration of the interaction networks for these problems revealed that the control group often spent this extra time pursuing buggy-strategies that did not lead to solutions.