Written Activity, Representations and Fluency as Predictors of Domain Expertise in Mathematics

Abstract

The emerging field of multimodal learning analytics evaluates natural communication modalities (digital pen, speech, images) to identify domain expertise, learning, and learning-oriented precursors. Using the Math Data Corpus, this research investigated students' digital pen input as small groups collaborated on solving math problems. Compared with non-experts, findings indicated that domain experts have an opposite pattern of accelerating total written activity as problem difficulty increases, a lower written and spoken disfluency rate, and they express different content--including a higher ratio of nonlinguistic symbolic representations and structured diagrams to elemental marks. Implications are discussed for developing reliable multimodal learning analytics systems that incorporate digital pen input to automatically track the consolidation of domain expertise. This includes prediction based on a combination of activity patterns, fluency, and content analysis. New MMLA systems are expected to have special utility on cell phones, which already have multimodal interfaces and are the dominant educational platform worldwide.