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Abstract: Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy. 

Abstract: In interactive e-learning environments such as Intelligent Tutoring Systems, there are pedagogical decisions to make at two main levels of granularity: whole problems and single steps. In recent years, there is growing interest in applying datadriven techniques for adaptive decision making that can dynamically tailor students’ learning experiences. Most existing data-driven approaches, however, treat these pedagogical decisions equally, or independently, disregarding the long-term impact that tutor decisions may have across these two levels of granularity. In this paper, we propose and apply an offline Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels. An empirical classroom study shows that the HRL policy is significantly more effective than a Deep QNetwork (DQN) induced policy and a random yet reasonable baseline policy.