Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics

In this dissertation, I investigated applying a form of machine learning, reinforcement learning, to induce tutorial tactics from pre-existing data collected from real subjects. Tutorial tactics are policies as to how the tutor should select the next action when there are multiple ones available at each step. In order to investigate whether micro-level tutorial decisions would impact students' learning, we induced two sets of tutorial tactics: the ``Normalized Gain' tutorial tactics were derived with the goal of enhancing the tutorial decisions that contribute to the students' learning while the "Inverse Normalized Gain" ones were derived with the goal of enhancing those decisions that contribute less or even nothing to the students' learning. The two sets of tutorial tactics were compared on real human participants. Results showed that when the contents were controlled so as to be the same, different tutorial tactics would indeed make a difference in students' learning gains. The "Normalized Gain" students out-performed their ``Inverse Normalized Gain' peers. This dissertation sheds some light on how to apply reinforcement learning to induce tutorial tactics in natural language tutoring systems