World's Best Scientists 2026 revealed!

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Computer Science

D-Index
62
Citations
20016
World Ranking
2851
National Ranking
1410

Overview

Kurt VanLehn is affiliated with Arizona State University in the United States. Their research primarily spans the fields of Computer Science and Psychology, with a focus on Artificial Intelligence, Developmental and Educational Psychology, Computer Science Applications, Education, and Signal Processing.

The scientist's main topics of work include Intelligent Tutoring Systems and Adaptive Learning, Innovative Teaching and Learning Methods, Teaching and Learning Programming, AI-based Problem Solving and Planning, Speech and Audio Processing, Mathematics, Computing, and Information Processing, as well as Online and Blended Learning.

Recent scholarly contributions by Kurt VanLehn consist of publications in several academic venues. These include:

  • Teaching Algebraic Model Construction: A Tutoring System, Lessons Learned and an Evaluation, 2020, International Journal of Artificial Intelligence in Education
  • A Step-Based Tutoring System to Teach Underachieving Students How to Construct Algebraic Models, 2023, International Journal of Artificial Intelligence in Education
  • WHAT DO STUDENTS DO WHEN USING A STEP-BASED TUTORING SYSTEM?, 2022, Research and Practice in Technology Enhanced Learning
  • Tracking the Teacher in the Classroom, a Case Study, 2023, Computer-supported collaborative learning/The "Computer-Supported Collaborative Learning Conference
  • Speech-based Automatic Classroom Activity Detection, 2024, Computer-supported collaborative learning/The "Computer-Supported Collaborative Learning Conference

Some of the frequent co-authors collaborating with Kurt VanLehn are Bahar Shahrokhian, Chandrani Banerjee, Fabio Milner, Jon Wetzel, and Avinash Sivaraman. The frequent publication venues include the International Journal of Artificial Intelligence in Education, Computer-supported collaborative learning/The "Computer-Supported Collaborative Learning Conference, and Research and Practice in Technology Enhanced Learning.

Best Publications

  • The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems

    Kurt VanLehn

  • The Behavior of Tutoring Systems

    Kurt Vanlehn

  • Repair theory: A generative theory of bugs in procedural skills

    John Seely Brown;Kurt VanLehn

  • The Andes Physics Tutoring System: Lessons Learned

    Kurt Vanlehn;Collin Lynch;Kay Schulze;Joel A. Shapiro

  • Using Bayesian Networks to Manage Uncertainty in Student Modeling

    Cristina Conati;Abigail Gertner;Kurt Vanlehn

  • Mind Bugs: The Origins of Procedural Misconceptions

    Kurt VanLehn

  • Problem solving and cognitive skill acquisition

    Kurt VanLehn

  • Intelligent tutoring systems with conversational dialogue

    Arthur C. Graesser;Kurt VanLehn;Carolyn P. Rosé;Pamela W. Jordan

  • Why Do Only Some Events Cause Learning During Human Tutoring

    Kurt VanLehn;Stephanie Siler;Charles Murray;Takashi Yamauchi

  • A model of the self-explanation effect.

    Kurt VanLehn;Randolph M. Jones;Michelene T.H. Chi

  • The Content of Physics Self-Explanations

    Michelene T.H. Chi;Kurt A. VanLehn

  • Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART

    Arthur C. Graesser;Danielle S. McNamara;Kurt VanLehn

  • On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

    Cristina Conati;Abigail S. Gertner;Kurt VanLehn;Marek J. Druzdzel

  • Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation

    Cristina Conati;Kurt Vanlehn

  • Andes: A Coached Problem Solving Environment for Physics

    Abigail S. Gertner;Kurt VanLehn

  • The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing

    Kurt VanLehn;Pamela W. Jordan;Carolyn Penstein Rosé;Dumisizwe Bhembe

  • Toward a theory of impasse-driven learning

    K. VanLehn

  • Student assessment using Bayesian nets

    Joel Martin;Kurt VanLehn

  • Procedural help in Andes: generating hints using a Bayesian network student model

    Abigail S. Gertner;Cristina Conati;Kurt VanLehn

  • Applications of simulated students: an exploration

    Kurt Vanlehn;Stellan Ohlsson;Rod Nason

  • Learning one subprocedure per lesson

    K Vanlehn

  • Rule acquisition events in the discovery of problem-solving strategies

    Kurt A. VanLehn

  • R. S. Michalski, J. G. Carbonell and T. M. Mitchell, Machine Learning: An Artificial Intelligence Approach.

    Kurt VanLehn

Frequent Co-Authors

Carolyn Penstein Rosé
Carolyn Penstein Rosé Carnegie Mellon University
Diane J. Litman
Diane J. Litman University of Pittsburgh
Cristina Conati
Cristina Conati University of British Columbia
Arthur C. Graesser
Arthur C. Graesser University of Memphis
Michelene T. H. Chi
Michelene T. H. Chi Arizona State University
Alan H. Schoenfeld
Alan H. Schoenfeld University of California, Berkeley
Kenneth R. Koedinger
Kenneth R. Koedinger Carnegie Mellon University
John Seely Brown
John Seely Brown Palo Alto Research Center
Danielle S. McNamara
Danielle S. McNamara Arizona State University
Christian D. Schunn
Christian D. Schunn University of Pittsburgh

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