Using natural language processing tools to develop complex models of student engagement
Stefan Slater, Jaclyn L. Ocumpaugh, Ryan S. Baker, Ma. Victoria Almeda, Laura Kristen Allen, Neil Thomas Heffernan · 2017
This paper examines the effect of different linguistic features (as identified through Natural Language Processing tools) on affective measures of student engagement using a discovery with models approach. We build on previous literature, using automated detectors that identify when a middle-school student using an online mathematics tutor is experiencing boredom, confusion, frustration, or engaged concentration, to identify which problems are most engaging (or not) at scale. We then apply previously validated NLP tools to determine the degree to which engagement findings may be related to the linguistic properties of word problems, contributing to a growing literature on the effects of language on mathematics learning.