Written and multimodal representations as predictors of expertise and problem-solving success in mathematics

One aim of multimodal learning analytics is to analyze rich natural communication modalities to identify domain expertise and learning rapidly and reliably. In this research, written and multimodal representations are analyzed from the Math Data Corpus, which involves multimodal data (digital pen, speech, images) on collaborating students as they solve math problems. Findings reveal that in 96-97% of cases the correctness of a group's solution was predictable in advance based on students' written work content. In addition, a linear regression revealed that 65% of the variance in individual students' domain expertise rankings could be accounted for based on their written work content. A multimodal content analysis based on both written and spoken input correctly predicted the dominant domain expert in a group 100% of the time, exceeding unimodal prediction rates. Further analysis revealed a reversal between experts and non-experts in the percentage of time that a match versus mismatch was present between their oral and written answer contributions, with non-experts demonstrating higher mismatches. Implications are discussed for developing reliable multimodal learning analytics systems that incorporate digital pen input to automatically track consolidation of domain expertise.