Evaluation of a meta-tutor for constructing models of dynamic systems

Lishan Zhang, Winslow Burleson, Maria Elena Chavez-Echeagaray, Sylvie Girard, Javier Gonzalez-Sanchez, Yoalli Hidalgo-Pontet, Kurt Vanlehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


While modeling dynamic systems in an efficient manner is an important skill to acquire for a scientist, it is a difficult skill to acquire. A simple step-based tutoring system, called AMT, was designed to help students learn how to construct models of dynamic systems using deep modeling practices. In order to increase the frequency of deep modeling and reduce the amount of guessing/gaming, a meta-tutor coaching students to follow a deep modeling strategy was added to the original modeling tool. This paper presents the results of two experiments investigating the effectiveness of the meta-tutor when compared to the original software. The results indicate that students who studied with the meta-tutor did indeed engage more in deep modeling practices.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
Number of pages4
ISBN (Print)9783642391118
StatePublished - 2013
Externally publishedYes
Event16th International Conference on Artificial Intelligence in Education, AIED 2013 - Memphis, TN, United States
Duration: Jul 9 2013Jul 13 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7926 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Artificial Intelligence in Education, AIED 2013
Country/TerritoryUnited States
CityMemphis, TN


  • Empirical evaluation
  • Intelligent tutoring systems
  • Meta-tutor

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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