Diagnosing errors from off-path steps in model-tracing tutors

Luc Paquette, Jean François Lebeau, André Mayers

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


Model-tracing tutors were shown to be effective for the tutoring of problem solving tasks, but they usually lack the capability to provide feedback on learners' off-path steps. In this paper, we define a method, inspired by Sierra, to diagnose many of the learners' errors from their off-path steps. This method is implemented in Astus, a model-tracing tutor authoring framework. We show how Astus diagnose errors from off-path steps and use the resulting diagnostic to generate negative feedback.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
PublisherSpringer-Verlag Berlin Heidelberg
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


Other16th International Conference on Artificial Intelligence in Education, AIED 2013
CountryUnited States
CityMemphis, TN


  • Error diagnosis
  • Model-tracing
  • Negative feedback
  • Off-path steps

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Diagnosing errors from off-path steps in model-tracing tutors'. Together they form a unique fingerprint.

Cite this