Identification of effective learning behaviors

Paul Salvador Inventado, Roberto Legaspi, Rafael Cabredo, Koichi Moriyama, Ken Ichi Fukui, Satoshi Kurihara, Masayuki Numao

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


Self-regulated learners have been shown to learn more effectively. However, it is not easy to become self-regulated because learners have to be capable of observing and evaluating their thoughts, actions and behaviors while learning. In this work, we used Q-learning to reveal the effectiveness or ineffectiveness of a learning behavior that carries over learning episodes. We also showed different types of effective learning behavior discovered and how they were differentiated. Providing learners with knowledge about learning behavior effectiveness can help them observe how strategy selection affects their performance and will help them select more appropriate strategies in succeeding learning episodes for better future performance.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
PublisherSpringer Verlag
Number of pages4
ISBN (Print)9783642391118
Publication statusPublished - 2013
Externally publishedYes
Event16th International Conference on Artificial Intelligence in Education, AIED 2013 - Memphis, TN, United States
Duration: 2013 Jul 92013 Jul 13

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
Country/TerritoryUnited States
CityMemphis, TN

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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