Predicting Math Identity Through Language and Click-Stream Patterns in a Blended Learning Mathematics Program for Elementary Students

This study builds on prior research by leveraging natural language processing (NLP), click-stream analyses, and survey data to predict students’ mathematics success and math identity (namely, self-concept, interest, and value of mathematics). Specifically, we combine NLP tools designed to measure lexical sophistication, text cohesion, and sentiment with analyses of student click-stream data within an online mathematics tutoring system. We combine these data sources to predict elementary students’ success within the system as well as components of their math identity as measured though a standardized survey. Data from 147 students was examined longitudinally over a year of study. The results indicated links between math success and non-cognitive measures of math identity. Additionally, the results indicate that math identity was strongly predicted by click-stream variables and the production of more lexically sophisticated and cohesive language. In addition, significant variance in math identity was explained by affective and cognitive variables. The results indicate that NLP and click-stream data can combine to provide insights into non-cognitive constructs such as math identity.