Adapting exercise selection to learner self-esteem and performance

Juliet Okpo, Matt Dennis, Kirsten Smith, Judith Masthoff, Nigel Beacham

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

4 Citations (Scopus)

Abstract

Adapting tasks to learner characteristics is essential when selecting appropriate tasks for learners. This paper investigates how humans adapt exercise selection to learner self-esteem (SE) and performance, to allow a future Intelligent Tutoring System (ITS) to use these adaptations. Self esteem is an important factor in learning as it is a significant predictor of academic performance. Previous research adapts task selection to other characteristics e.g. past performance, but little work focuses on task selection based on learner personality
Original languageEnglish
Title of host publicationIntelligent Tutoring Systems
Subtitle of host publication13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings
EditorsAlessandro Micarelli, John Stamper, Kitty Panourgia
PublisherSpringer-Verlag
Pages517-518
Number of pages2
Volume9684
ISBN (Print)9783319395821
DOIs
Publication statusPublished - 2016
Event13th International Conference on Intelligent Tutoring Systems, ITS 2016 - Zagreb, Croatia
Duration: 7 Jun 201610 Jun 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9684
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference13th International Conference on Intelligent Tutoring Systems, ITS 2016
CountryCroatia
CityZagreb
Period7/06/1610/06/16

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  • Cite this

    Okpo, J., Dennis, M., Smith, K., Masthoff, J., & Beacham, N. (2016). Adapting exercise selection to learner self-esteem and performance. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings (Vol. 9684, pp. 517-518). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9684). Springer-Verlag. https://doi.org/10.1007/978-3-319-39583-8