Multimodal learning analytics: description of math data corpus for ICMI grand challenge workshop

Authors:
Sharon Oviatt;Adrienne Cohen;Nadir Weibel
Affiliations:
Incaad, Bainbridge Island, WA, USA;University of Washington, Seattle, WA, USA;University of California, San Diego, San Diego, CA, USA
Venue:
Proceedings of the 15th ACM on International conference on multimodal interaction
Year:
2013

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Abstract

This paper provides documentation on dataset resources for establishing a new research area called multimodal learning analytics (MMLA). Research on this topic has the potential to transform the future of educational practice and technology, as well as computational techniques for advancing data analytics. The Math Data Corpus includes high-fidelity time-synchronized multimodal data recordings (speech, digital pen, images) on collaborating groups of students as they work together to solve mathematics problems that vary in difficulty level. The Math Data Corpus resources include initial coding of problem segmentation, problem-solving correctness, and representational content of students' writing. These resources are made available to participants in the data-driven grand challenge for the Second International Workshop on Multimodal Learning Analytics. The primary goal of this event is to analyze coherent signal, activity, and lexical patterns that can identify domain expertise and change in domain expertise early, reliably, and objectively, as well as learning-oriented precursors. An additional aim is to build an international research community in the emerging area of multimodal learning analytics by organizing a series of workshops that bring together multidisciplinary scientists to work on MMLA topics.