DT Tutor: A Decision-TheoreticDynamic Approach for Optimal Selection of Tutorial Actions

Murray, R. Charles; VanLehn, Kurt

doi:10.1007/3-540-45108-0_19

Cited by 42 publications

(24 citation statements)

References 10 publications

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“…In the first educational systems, the students were responsible for their own learning, such as in PMS system [10]. Nowadays, artificial intelligence techniques are applied in order to solve the problems derived from choosing the pedagogical strategy; for instance, semantic nets have been applied in the MENO-TUTOR system [11], neural nets have been used in the UNIMEM system [2], bayesian networks have been applied in DT Tutor [3] and reinforcement learning model have been used to define the user model in the ADVISOR system [12].…”

Section: Pedagogical Strategiesmentioning

confidence: 99%

“…Several Machine Learning (ML) techniques are used in AIES in order to choose the best pedagogical strategy to be applied in each moment, like neural networks [2], Bayesian networks [3], etc. In a previous paper [4], we propose to use a knowledge representation based on Reinforcement Learning (RL) [5] that allows AIES to adapt tutoring to students' needs.…”

Section: Introductionmentioning

confidence: 99%

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Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

et al. 2008

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One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best pedagogical policy for teaching students. One of the main characteristics of this approach is its ability to improve the pedagogical policy based only on acquired experience with other students with similar learning characteristics. In this paper we study the learning performance of the educational system through three important issues. Firstly, the learning convergence towards accurate pedagogical policies. Secondly, the role of exploration/exploitation strategies in the application of RL to AIES. Finally, a method for reducing the training phase of the AIES.

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Section: Pedagogical Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

et al. 2008

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“…These variables include degree of control that the student likes to have on the learning situation, degree of challenge that the student likes to experience, degree of independence during the interaction and degree of fantasy based situations that the student likes the instructional interaction to include. Murray and VanLehn (2000) developed a decision theoretic tutor that takes into account both student learning and morale in deciding how to act. However the authors do not discuss how student morale is assessed in their system.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic assessment of user's emotions in educational games

Conati¹

2002

Applied Artificial Intelligence

389

215

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We present a probabilistic model to monitor a user's emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user's emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user's emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user's learning and engagement during the interaction with educational games.2

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“…Although student emotions play a key role in the learning process, very little work has been done on affective student modeling. Murray and Vanlehn [16] present a decision theoretic tutor that takes into account both student learning and morale in deciding how to act, but the authors do not discuss how student morale is assessed. Kaapor, Mota, and Picard [15] present preliminary results on how to monitor eyebrow movements and posture to provide evidence on students' engagement during the interaction with a computer based tutor, but these results were yet to be integrated in a computational student model.…”

Section: Introductionmentioning

confidence: 99%

Inferring user goals from personality and behavior in a causal model of user affect

Zhou¹,

Conati²

2003

Proceedings of the 8th International Conference on Intelligent User Interfaces - IUI '03

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We present a probabilistic model, based on Dynamic Decision Networks, to assess user affect from possible causes of emotional arousal. The model relies on the OCC cognitive theory of emotions and is designed to assess student affect during the interaction with an educational game. A key element of applying the OCC theory to assess user affect is knowledge of user goals. Thus, in this paper we focus on describing how our model infers these goals from user personality traits and interaction behavior. In particular, we illustrate how we iteratively defined the structure and parameters for this part of the model by using both empirical data collected through Wizard of Oz experiments and relevant psychological findings.

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DT Tutor: A Decision-TheoreticDynamic Approach for Optimal Selection of Tutorial Actions

Cited by 42 publications

References 10 publications

Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

Probabilistic assessment of user's emotions in educational games

Inferring user goals from personality and behavior in a causal model of user affect

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