Impact of Interaction Context on the Student Affect-Learning Relationship in Child-Robot Interaction

Honorable Mention
Special Education TechnologyRobots in Education & HealthcareSpeech-Language Pathologists & AudiologistsSpecial Education TeachersEarly Childhood Educators

Prior work in affect-aware educational robots has often relied on a common belief that the relationship between student affect and learning is independent of agent behaviors (child’s/robot’s) or unidirectional (positive/negative but not both) throughout the entire student-robot interaction. We argue that the student affect-learning relationship should be interpreted in two contexts: (1) social learning paradigm and (2) sub-events within child-robot interaction. In our paper, we examine two different social learning paradigms where children interact with a robot that acts either as a tutor or a tutee. Sub-events within child-robot interaction are defined as task-related events occurring in specific phases of an interaction (e.g., when the child/robot gets a wrong answer). We examine subevents at a macro level (entire interaction) and a micro level (within specific sub-events). In this paper, we provide an in-depth correlation analysis of children’s facial affect and vocabulary learning. We found that children’s affective displays became more predictive of their vocabulary learning when children interacted with a tutee robot who did not scaffold their learning. Additionally, children’s affect displayed during micro-level events was more predictive of their learning than during macro-level events. Last, we found that the affect-learning relationship is not unidirectional, but rather is modulated by context, i.e., several affective states facilitated student learning when displayed in some sub-events but inhibited learning when displayed in others. These findings indicate that both social learning paradigm and sub-events within interaction modulate student affect-learning relationship.

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https://hci.top/en/papers/hri/38288/2020

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DOI: https://doi.org/10.1145/3319502.3374822
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Source
HRI
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Year
2020
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Award
Honorable Mention
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Authors
4 authors
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Subtopics
Special Education Technology, Robots in Education & Healthcare
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Professions
Speech-Language Pathologists & Audiologists, Special Education Teachers, Early Childhood Educators
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Content Status
Abstract only
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Related Papers
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