Using natural language processing tools to develop complex models of student engagement

Stefan Slater, Jaclyn Ocumpaugh, Ryan Baker, Ma Victoria Almeda, Laura Allen, Neil Heffernan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages542-547
Number of pages6
ISBN (Electronic)9781538605639
DOIs
StatePublished - Jul 2 2017
Externally publishedYes
Event7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017 - San Antonio, United States
Duration: Oct 23 2017Oct 26 2017

Publication series

Name2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Volume2018-January

Conference

Conference7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Country/TerritoryUnited States
CitySan Antonio
Period10/23/1710/26/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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