A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection

Murray, R. Charles; VanLehn, Kurt

doi:10.1007/11774303_12

Cited by 24 publications

(21 citation statements)

References 7 publications

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“…Other investigations into probabilistic models for teaching have also demonstrated the benefits of an approach that can plan under uncertainty in finding useful policies for teaching tasks (Rafferty et al 2016;Murray and VanLehn 2006). Our work is in agreement with this body of work, and we provide further evidence for the usefulness of a POMDP model used to plan under uncertainty in a long-term tutoring setting for children.…”

Section: Discussionsupporting

confidence: 87%

Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Ramachandran

Sebo

Scassellati

2019

AAAI

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Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.

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Section: Discussionsupporting

confidence: 87%

Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Ramachandran

Sebo

Scassellati

2019

AAAI

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“…(Wood, 2001) Whether to give an unsolicited hint or to respond with a hint when asked for one? (Beck, Woolf, & Beal, 2000;Murray & VanLehn, 2006;Murray, VanLehn, & Mostow, 2004) Which behavior an affective agent should perform? (Arroyo, Mehranian, & Woolf, 2010;Muñoz, Mc Kevitt, Lunney, Noguez, & Neri, 2010) Should the task be posed as an example, a problem with feedback or a problem without feedback?…”

Section: Prior Work On Adaptive Task Selectionmentioning

confidence: 98%

Adaptively selecting biology questions generated from a semantic network

Zhang

VanLehn

2016

Interactive Learning Environments

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The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a maladaptive order. ARTICLE HISTORY

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“…After the 1990s, the method of statistical natural language processing based on a large corpus were widely used in the field of natural language understanding and achieved good results. Andes Tutorial System is an intelligent tutoring system based on physical mechanics, through understanding the meaning of the problem, completing the interaction with the students [14]and providing problem-solving feedback.…”

Section: Related Workmentioning

confidence: 99%

Solving the Chinese Physical Problem Based on Deep Learning and Knowledge Graph

Li¹,

Zhou²,

Wang³

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

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In recent years, problem solving, automatic proof and human-like test-tasking have become a hot spot of research. This paper focus on the study of solving physical problem in Chinese. Based on the analysis of physical corpus, it is found that the physical problem are made up of ntuples which contain concepts and relations between concepts, and the n-tuples can be expressed in the form of UP-graph (The graph of understanding problem), which is the semantic expression of physical problem. UP-graph is the base of problem solving which is generated by using physical knowledge graph (PKG). However, current knowledge graph is hard to be used in problem solving, because it cannot store methods for solving problem. So this paper presents a model of PKG which contains concepts and relations, in the model, concepts and relations are split into terms and unique IDs, and methods can be easily stored in the PKG as concepts. Based on the PKG, DKP-solving is proposed which is a novel approach for solving physical problem. The approach combines rules, statistical methods and knowledge reasoning effectively by integrating the deep learning and knowledge graph. The experimental results over the data set of real physical text indicate that DKP-solving is effective in physical problem solving.

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A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection

Cited by 24 publications

References 7 publications

Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Adaptively selecting biology questions generated from a semantic network

Solving the Chinese Physical Problem Based on Deep Learning and Knowledge Graph

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