The impact of a social robot's attributions for success and failure in a teachable agent framework

Kasia Muldner, Victor Girotto, Cecil Lozano, Winslow Burleson, Erin Walker

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Teachable agents foster student learning by employing the learning by teaching paradigm. Since social factors influence learning from this paradigm, understanding which social behaviors a teachable agent should embody is an important first step for designing such an agent. Here, we focus on the impact of causal attributions made by a teachable agent. To obtain data on student perceptions of agent attributions, we conducted a study involving students interacting with a social robot that made attributions to ability and effort, and to the student, itself, or both. We analyzed data from semi-structured interviews to understand how different attributions influence student perceptions, and discuss design opportunities for manipulating these attributions to improve student motivation.

Original languageEnglish (US)
Pages (from-to)278-285
Number of pages8
JournalProceedings of International Conference of the Learning Sciences, ICLS
Volume1
Issue numberJanuary
StatePublished - 2014

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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