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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. Recent years have seen growing interest in data-driven techniques for such pedagogical decision making, which 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, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels. In an empirical classroom study with 180 students, our results show that the HRL policy is significantly more effective than a Deep Q-Network (DQN) induced policy and a random yet reasonable baseline policy.
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Identify Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning
For many forms of e-learning environments, the system's behaviors can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for deciding the next system action when there are multiple ones available. Each of these system decisions a ects the user's successive actions and performance and some of them are more important than others. Thus, this raises an open ques- tion: how can we identify the critical system interactive de- cisions that are linked to student learning from a long trajec- tory of decisions? In this work, we proposed and evaluated Critical-Reinforcement Learning (Critical-RL), an adversar- ial deep reinforcement learning (ADRL) based framework to identify critical decisions and induce compact yet e ective policies. Speci cally, it induces a pair of adversarial policies based upon Deep Q-Network (DQN) with opposite goals: one is to improve student learning while the other is to hin- der; critical decisions are identi ed by comparing the two adversarial policies and using their corresponding Q-value di erences; nally, a Critical policy is induced by giving op- timal action on critical decisions but random yet reason- able decisions on others. We evaluated the e ectiveness of Critical policy against a random yet reasonable (Random) policy. While no signi cant di erence was found between the two condition, it is probably because of small sample sizes. Much to our surprise, we found that students often experience so-called Critical phase: a consecutive sequence of critical decisions with the same action. Students were further divided into High vs. Low based on the number of Critical phases they experienced and our results showed that while no signi cant was found between the two Low groups, the High Critical group learned signi cantly more than the High Random group.
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- Award ID(s):
- 1651909
- PAR ID:
- 10136496
- Date Published:
- Journal Name:
- In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019)
- Page Range / eLocation ID:
- 595 – 598
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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