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1. Exploring Induced Pedagogical Strategies Through a Markov Decision Process Framework: Lessons Learned

   Shen, S.; Mostafavi, B.; Barnes, T.; Chi, M. (January 2018, Journal of educational data mining)

   null (Ed.)

   An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students’ behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address this challenge, we explore three aspects of a Markov Decision Process (MDP) framework through four experiments. The three aspects are: 1) reward function, detecting the impact of immediate and delayed reward on effectiveness of the policies; 2) state representation, exploring ECR-based, correlation-based, and ensemble feature selection approaches for representing the MDP state space; and 3) policy execution, investigating the effectiveness of stochastic and deterministic policy executions on learning. The most important result of this work is that there exists an aptitude-treatment interaction (ATI) effect in our experiments: the policies have significantly different impacts on the particular types of students as opposed to the entire population. We refer the students who are sensitive to the policies as the Responsive group. All our following results are based on the Responsive group. First, we find that an immediate reward can facilitate a more effective induced policy than a delayed reward. Second, The MDP policies induced based on low correlation-based and ensemble feature selection approaches are more effective than a Random yet reasonable policy. Third, no significant improvement was found using stochastic policy execution due to a ceiling effect. 

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2. Exploring Induced Pedagogical Strategies Through a Markov Decision Process Framework: Lessons Learned

   Shitian Shen, Behrooz Mostafavi (December 2018, Journal of educational data mining)

   An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students’ behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address this challenge, we explore three aspects of a Markov Decision Process (MDP) framework through four experiments. The three aspects are: 1) reward function, detecting the impact of immediate and delayed reward on effectiveness of the policies; 2) state representation, exploring ECR-based, correlation-based, and ensemble feature selection approaches for representing the MDP state space; and 3) policy execution, investigating the effectiveness of stochastic and deterministic policy executions on learning. The most important result of this work is that there exists an aptitude-treatment interaction (ATI) effect in our experiments: the policies have significantly different impacts on the particular types of students as opposed to the entire population. We refer the students who are sensitive to the policies as the Responsive group. All our following results are based on the Responsive group. First, we find that an immediate reward can facilitate a more effective induced policy than a delayed reward. Second, The MDP policies induced based on low correlation-based and ensemble feature selection approaches are more effective than a Random yet reasonable policy. Third, no significant improvement was found using stochastic policy execution due to a ceiling effect. 

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3. Exploring Induced Pedagogical Strategies Through a Markov Decision Process Framework: Lessons Learned

   Shen, S. (January 2018, Journal of educational data mining)

   An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students’ behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address this challenge, we explore three aspects of a Markov Decision Process (MDP) framework through four experiments. The three aspects are: 1) reward function, detecting the impact of immediate and delayed reward on effectiveness of the policies; 2) state representation, exploring ECR-based, correlation-based, and ensemble feature selection approaches for representing the MDP state space; and 3) policy execution, investigating the effectiveness of stochastic and deterministic policy executions on learning. The most important result of this work is that there exists an aptitude-treatment interaction (ATI) effect in our experiments: the policies have significantly different impacts on the particular types of students as opposed to the entire population. We refer the students who are sensitive to the policies as the Responsive group. All our following results are based on the Responsive group. First, we find that an immediate reward can facilitate a more effective induced policy than a delayed reward. Second, The MDP policies induced based on low correlation-based and ensemble feature selection approaches are more effective than a Random yet reasonable policy. Third, no significant improvement was found using stochastic policy execution due to a ceiling effect. 

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4. Identify Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning

   Ju, S. (January 2019, In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019))

   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 aects 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 eective 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 dierences; 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 eectiveness of Critical policy against a random yet reasonable (Random) policy. While no signi cant dierence 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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5. “Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach.”

   Shen, S. (January 2018, In Proceedings of 26th ACM International Conference on User Modeling, Adaptation, and Personalization Conference (ACM UMAP-2018))

   Constrained action-based decision-making is one of the most challenging decision-making problems. It refers to a scenario where an agent takes action in an environment not only to maximize the expected cumulative reward but where it is subject to certain actionbased constraints; for example, an upper limit on the total number of certain actions being carried out. In this work, we construct a general data-driven framework called Constrained Action-based Partially Observable Markov Decision Process (CAPOMDP) to induce effective pedagogical policies. Specifically, we induce two types of policies: CAPOMDP-LG using learning gain as reward with the goal of improving students’ learning performance, and CAPOMDP-Time using time as reward for reducing students’ time on task. The effectiveness ofCAPOMDP-LG is compared against a random yet reasonable policy and the effectiveness of CAPOMDP-Time is compared against both a Deep Reinforcement Learning induced policy and a random policy. Empirical results show that there is an Aptitude Treatment Interaction effect: students are split into High vs. Low based on their incoming competence; while no significant difference is found among the High incoming competence groups, for the Low groups, students following CAPOMDP-Time indeed spent significantly less time than those using the two baseline policies and students following CAPOMDP-LG significantly outperform their peers on both learning gain and learning efficiency. 

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