Problem solving, domain expertise and learning: ground-truth performance results for math data corpus

Authors:
Sharon Oviatt
Affiliations:
Incaa Designs, Bainbridge Island, USA
Venue:
Proceedings of the 15th ACM on International conference on multimodal interaction
Year:
2013

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Abstract

Problem solving, domain expertise, and learning are analyzed for the Math Data Corpus, which involves multimodal data on collaborating student groups as they solve math problems together across sessions. Compared with non-expert students, domain experts contributed more group solutions, solved more problems correctly and took less time. These differences between experts and non-experts were accentuated on harder problems. A cumulative expertise metric validated that expert and non-expert students represented distinct non overlapping populations, a finding that replicated across sessions. Group performance also improved 9.4% across sessions, due mainly to learning by expert students. These findings satisfy ground-truth conditions for developing prediction techniques that aim to identify expertise based on multimodal communication and behavior patterns. Together with the Math Data Corpus, these results contribute valuable resources for supporting data-driven grand challenges on multimodal learning analytics, which aim to develop new techniques for predicting expertise early, reliably, and objectively. as well as learning-oriented precursors.