Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching

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dc.contributor.authorChen, Lujie Karen
dc.contributor.authorRamsey, Joseph
dc.contributor.authorDubrawski, Artur
dc.date.accessioned2021-07-28T19:43:43Z
dc.date.available2021-07-28T19:43:43Z
dc.date.issued2021
dc.descriptionEducational Data Mining 2021en_US
dc.description.abstractHuman 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.en_US
dc.description.sponsorshipThe research reported here was supported, in whole or in part, by the Institute of Education Sciences, U.S. Department of Education, through grant R305B150008 to Carnegie Mellon University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education. In addition, the authors would like to thank Mononito Goswami, Qianou Ma and Eva Gjekmarkaj for their talented and dedicated research assistance.en_US
dc.description.urihttps://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_J506.pdfen_US
dc.format.extent35 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2cmmw-fiif
dc.identifier.citationChen, 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.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/22206
dc.language.isoen_USen_US
dc.publisherInternational Educational Data Mining Societyen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmultimodal learning analyticsen_US
dc.subjectcausal discoveryen_US
dc.subjectcausal inferenceen_US
dc.subjectparent coachingen_US
dc.subjectaffective and cognitive supporten_US
dc.titleAffect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coachingen_US
dc.typeTexten_US

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