Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching
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2021
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Chen, Lujie Karen; Ramsey, Joseph; Dubrawski, Artur; Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching; Educational Data Mining 2021; https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_J506.pdf
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Abstract
Human one-on-one coaching involves complex multimodal interactions. Successful coaching
requires teachers to closely monitor students’ cognitive-affective states and provide support of
optimal type, timing, and amount. However, most of the existing human tutoring studies focus primarily on verbal interactions and have yet to incorporate the rich aspects of multimodal
cognitive-affective experiences. Meanwhile, the research community lacks principled methods to fully exploit the complex multimodal data to uncover the causal relationships between
coaching supports and students’ cognitive-affective experiences and their stable individual factors. We explore an analytical framework that is explainable and amenable to incorporating domain knowledge. The proposed framework combines statistical approaches in Sparse Multiple
Canonical Correlation, causal discovery and inference methods for observations. We demonstrate this framework using a multimodal one-on-one math problem-solving coaching dataset
collected at naturalist home environments involving parents and young children. The insights
derived from our analyses may inform the design of effective technology-inspired interventions
that are personalized and adaptive.