Influencing the Learning Experience Through Affective Agent Feedback in a Real-World Treasure Hunt

Mary Ellen Foster, Amol Deshmukh, Srinivasan Janarthanam, Mei Yii Lim, Helen Hastie, Ruth Aylett

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

1 Citation (Scopus)

Abstract

We explore the effect of the behaviour of a virtual robot agent in the context of a real-world treasure-hunt activity earned out by children aged 11-12. We compare three conditions: a traditional paper-based treasure hunt, along with a virtual robot on a tablet which provides either neutral or affective feedback during the treasure hunt. The results of the study suggest that the use of the virtual robot increased the perceived difficulty of the instruction-following task, while the affective robot feedback in particular made the questions seem snore difficult to answer.
Original languageEnglish
Title of host publicationAamas 15 International Conference on Autonomous Agents and Multi Agent Solutions
PublisherAssociation for Computing Machinery
Pages1711-1712
Number of pages2
Volume3
ISBN (Print)9781450337717
Publication statusPublished - 7 Jul 2015
Event14th International Conference on Autonomous Agents and Multiagent Systems 2015 - Istanbul Congress Center, Istanbul, Turkey
Duration: 4 May 20158 May 2015

Conference

Conference14th International Conference on Autonomous Agents and Multiagent Systems 2015
Abbreviated titleAAMAS 2015
CountryTurkey
CityIstanbul
Period4/05/158/05/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Influencing the Learning Experience Through Affective Agent Feedback in a Real-World Treasure Hunt'. Together they form a unique fingerprint.

  • Cite this

    Foster, M. E., Deshmukh, A., Janarthanam, S., Lim, M. Y., Hastie, H., & Aylett, R. (2015). Influencing the Learning Experience Through Affective Agent Feedback in a Real-World Treasure Hunt. In Aamas 15 International Conference on Autonomous Agents and Multi Agent Solutions (Vol. 3, pp. 1711-1712). Association for Computing Machinery.