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International Journal of Artificial Intelligence in Education

2011 Volume 21, Number 1

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Table of Contents

Number of articles: 8

  1. Detecting Learning Moment-by-Moment

    Ryan S. J. D. Baker, Adam B. Goldstein & Neil T. Heffernan

    Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us... More

    pp. 5-25

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  2. How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis

    Yue Gong, Joseph E. Beck & Neil T. Heffernan

    Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models.... More

    pp. 27-46

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  3. Learning What Works in ITS from Non-Traditional Randomized Controlled Trial Data

    Zachary A. Pardos, Matthew D. Dailey & Neil T. Heffernan

    The well established, gold standard approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pre-test and post-test design. RCTs have ... More

    pp. 47-63

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  4. Investigating the Relationship between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach

    Kristy Elizabeth Boyer, Robert Phillips, Amy Ingram, Eun Young Ha, Michael Wallis, Mladen Vouk & James Lester

    Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial... More

    pp. 65-81

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  5. An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning Approach

    Min Chi, Kurt VanLehn, Diane Litman & Pamela Jordan

    Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions,... More

    pp. 83-113

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  6. Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments

    Jonathan P. Rowe, Lucy R. Shores, Bradford W. Mott & James C. Lester

    A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based... More

    pp. 115-133

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  7. Using an Intelligent Tutor and Math Fluency Training to Improve Math Performance

    Ivon Arroyo, James M. Royer & Beverly P. Woolf

    This article integrates research in intelligent tutors with psychology studies of memory and math fluency (the speed to retrieve or calculate answers to basic math operations). It describes the... More

    pp. 135-152

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  8. Enhancing the Automatic Generation of Hints with Expert Seeding

    John Stamper, Tiffany Barnes & Marvin Croy

    The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to... More

    pp. 153-167

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