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1. Pick the Moment: Identifying Crucial Pedagogical Decisions Using Long-Short Term Rewards.

   Ju, S.; Zhou, G.; Barnes, T.; Chi, M. (January 2020, In Proceedings of the 13th International Conference on Educational Data Mining (EDM))

   null (Ed.)

   Abstract: Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerned with inducing effective decision-making policies, following which result in the maximum cumulative "reward." Many RL algorithms find the optimal policy via estimating the optimal Q-values, which specify the maximum cumulative reward the agent can receive. In our LSTR framework, the "long term" rewards are defined as "Q-values" and the "short term" rewards are determined by the "reward function." Experiments on a synthetic GridWorld game and real-world Intelligent Tutoring System datasets show that the proposed LSTR framework indeed identifies the critical decisions in the sequences. Furthermore, our results show that carrying out the critical decisions alone is as effective as a fully-executed policy. 

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2. 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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3. 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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4. 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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5. Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System

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

   Deep Reinforcement Learning (DRL) has been shown to be a very powerful technique in recent years on a wide range of applications. Much of the prior DRL work took the online learning approach. However, given the challenges of building accurate simulations for modeling student learning, we investigated applying DRL to induce a pedagogical policy through an offiine approach. In this work, we explored the effectiveness of offiine DRL for pedagogical policy induction in an Intelligent Tutoring System. Generally speaking, when applying offiine DRL, we face two major challenges: one is limited training data and the other is the credit assignment problem caused by delayed rewards. In this work, we used Gaussian Processes to solve the credit assignment problem by estimating the inferred immediate rewards from the final delayed rewards. We then applied the DQN and Double-DQN algorithms to induce adaptive pedagogical strategies tailored to individual students. Our empirical results show that without solving the credit assignment problem, the DQN policy, although better than Double-DQN, was no better than a random policy. However, when combining DQN with the inferred rewards, our best DQN policy can outperform the random yet reasonable policy, especially for students with high pre-test scores. 

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