EXP: Linguistic Analysis and a Hybrid Human-Automatic Coach for Improving Math Identity


PIs: Jaclyn Ocumpaugh, Matthew Labrum, Scott Crossley, Ryan Baker, Victor Kostyuk
University of Pennsylvania
Award Details

This project addresses the effect of students’ social identity on learning, an important factor in math and science education. Specifically, it will advance the scientific understanding of math identity (i.e. “I’m (not) a math person”) by studying the over 100,000 diverse students who use Reasoning Mind, a blended learning system for K-8 mathematics with demonstrated results. Reasoning Mind supports math identity with an innovative design that allows students to email an animated character (aka the Genie) and receive a human-crafted response. This study will show how math identity manifests and changes during students’ use of Reasoning Mind in order to inform software designers and classroom teachers on best practices for encouraging math identity. This will have the broader impact of strengthening our nation’s ability to supply science and technology fields with a well-trained workforce.

This study examines two components of math identity: self-efficacy and interest in mathematics. Both self-efficacy and interest can be enhanced by curricula that are individualized to appropriately challenge each student (a strength of educational technology), but social stereotypes (i.e. “Girls aren’t good in math”) may decrease math identity or otherwise interfere with its development. This study investigates how educational technology can reverse these trends. In the first phase of this study, a combination of survey methods, Natural Language Processing (NLP), and Educational Data Mining (EDM) techniques will be used to identify how poor and/or changing math identity emerges in the linguistic patterns of student interaction with GenieMail (as well as in other parts of Reasoning Mind). These findings will then be used to enhance GenieMail and other instructional interactions with the Reasoning Mind system by creating a hybrid human/AI system, with the goal of improving math identity across diverse populations of students.

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