How Social Robots can Influence Motivation as Motivators in Learning: A Scoping Review
PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS, PETRA 2023(2023)
摘要
Earlier research has investigated how educational social robots can influence learner motivation and learning outcomes as motivators instead of learning materials. This paper presents a scoping literature review of this body of work, focusing on the educational strategies used, and describing the range of approaches used to influence motivation and learning through social robots, not as learning tools but as motivators. Nineteen advanced studies are identified and described according to the components of the ARCS model (a motivation model dominant in robotics research): Attention, Relevance, Confidence, and Satisfaction. We summarized the measures used for motivation in the studies and relate these measures to the four ARCS model components. Finally, we analyzed the studies from the perspectives of sample groups, study type, and domain or subject. Our analyses suggested that beyond focusing on persuasive (educational) strategies that educational social robots can use to keep learners' attention, researchers should also focus on the satisfaction component of motivation. Furthermore, future studies should examine long-term interactions, apply more rigor in using validated questionnaires, and combine qualitative and quantitative methods to understand not only the effects of these different approaches but also the reasons behind them.
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关键词
Motivation measurement,The ARCS model,Educational robots,Learning/teaching strategies,Literature review
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