Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

Chi, Min; VanLehn, Kurt; Litman, Diane; Jordan, Pamela W.

doi:10.1007/s11257-010-9093-1

Cited by 90 publications

(58 citation statements)

References 37 publications

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“…Another student, having correctly stated that acceleration was the time rate of change of velocity, incorrectly determined the resultant direction of the object's velocity. Such cases suggest the potential for targeted clarifying dialog, perhaps similar to the knowledge-construction-dialogs used elsewhere [1,4].…”

Section: Language Accuracy and Student Answer Patternsmentioning

confidence: 78%

“…Physics education, in particular, has an impressive pedigree of development -including ANDES/ATLAS [1], the AutoTutor series [2,3], Cordillera [4], and most recently Deep Tutor [5]. These computer tutors, using various natural language methodologies and to significant levels of success, have tackled physics topics such as forces, kinematics, Newton's laws, and energy conservation.…”

Section: Introductionmentioning

confidence: 99%

“…These computer tutors, using various natural language methodologies and to significant levels of success, have tackled physics topics such as forces, kinematics, Newton's laws, and energy conservation. The approaches vary from guided essay construction focused on the identification of misconceptions [2,3], to specific knowledge construction dialogues [1,4]. The common hope is that by engaging the student in a reflective and constructive dialog, students will achieve greater learning gains than by rote application of physical principals.…”

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of a Natural Language Tutor for Force and Motion

Badeau¹,

Heckler²

2015

2014 Physics Education Research Conference Proceedings

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Abstract:We report on the design and pilot evaluation of a simple natural language computer tutor that targets student difficulties with the concepts of force and motion. The tutor prompts students to respond in free-response natural language to questions that address the relationships between the directions of net force, velocity, and acceleration. To examine the effectiveness of the natural language format, we compared student performance on a previously validated force and motion assessment after tutoring via natural language and multiple choice formats. Natural language training with feedback, multiple choice training with feedback, and natural language training without feedback formats resulted in effect sizes of d = 0.60 (p = 0.07), d = 0.46 (p = 0.13), and d = 0.09 (p = 0.97) respectively versus a no-training control. In addition, a median split on course grades showed no significant aptitude-treatment interaction across training conditions. However, accounting for time spent on training, the multiple choice training was significantly more efficient. For the natural language format, an analysis of performance (62% identification of an initial student response), false positives, and typical student answer patterns suggest room for improvement and subsequent study.

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Section: Language Accuracy and Student Answer Patternsmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of a Natural Language Tutor for Force and Motion

Badeau¹,

Heckler²

2015

2014 Physics Education Research Conference Proceedings

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“…Chi and co-workers performed empirical evaluation on the application of RL to adaptive pedagogical strategies [8]. In [9], the researchers evaluated the learning performance of the educational system through three issues: The learning convergence, exploration/exploitation strategies, and reduction of training phase.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning in a POMDP Based Intelligent Tutoring System for Optimizing Teaching Strategies

Wang¹

2018

IJIET

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Abstract-The abilities to improve teaching strategies online is important for an intelligent tutoring system (ITS) to perform adaptive teaching. Reinforcement learning (RL) may help an ITS obtain the abilities. Conventionally, RL works in a Markov decision process (MDP) framework. However, to handle uncertainties in teaching/studying processes, we need to apply the partially observable Markov decision process (POMDP) model in building an ITS. In a POMDP framework, it is difficult to use the improvement algorithms of the conventional RL because the required state information is unavailable. In our research, we have developed a reinforcement learning technique, which enables a POMDP-based ITS to learn from its teaching experience and improve teaching strategies online.Index Terms-Computer supported education, intelligent tutoring system, reinforcement learning, partially observable Markov decision process.

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“…Reinforcement learning is also used for some more complex systems, such as learning negotiation policies (Georgila and Traum, 2011) and tutoring (Chi et al, 2011). Reinforcement learning is also used in question-answering systems (Misu et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

Cited by 90 publications

References 37 publications

Design and Evaluation of a Natural Language Tutor for Force and Motion

Design and Evaluation of a Natural Language Tutor for Force and Motion

Reinforcement Learning in a POMDP Based Intelligent Tutoring System for Optimizing Teaching Strategies

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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