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Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis

Authors Scott Crossley , Shamya Karumbaiah, Jaclyn Ocumpaugh, Matthew J. Labrum , Ryan S. Baker

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Author Details

Scott Crossley
  • Georgia State University, Applied Linguistics/ESL, Atlanta, GA, USA
Shamya Karumbaiah
  • The University of Pennsylvania, Philadelphia, PA, USA
Jaclyn Ocumpaugh
  • The University of Pennsylvania, Philadelphia, PA, USA
Matthew J. Labrum
  • Imagine Learning, Provo, UT, USA
Ryan S. Baker
  • The University of Pennsylvania, Philadelphia, PA, USA

Funding

This research was supported in part by NSF 1623730. Opinions, conclusions, or recommendations do not necessarily reflect the views of the NSF.

Cite AsGet BibTex

Scott Crossley, Shamya Karumbaiah, Jaclyn Ocumpaugh, Matthew J. Labrum, and Ryan S. Baker. Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 25:1-25:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.25

Abstract

Previous studies have demonstrated strong links between students' linguistic knowledge, their affective language patterns and their success in math. Other studies have shown that demographic and click-stream variables in online learning environments are important predictors of math success. This study builds on this research in two ways. First, it combines linguistics and click-stream variables along with demographic information to increase prediction rates for math success. Second, it examines how random variance, as found in repeated participant data, can explain math success beyond linguistic, demographic, and click-stream variables. The findings indicate that linguistic, demographic, and click-stream factors explained about 14% of the variance in math scores. These variables mixed with random factors explained about 44% of the variance.

Subject Classification

ACM Subject Classification
  • Applied computing → Computer-assisted instruction
  • Applied computing → Mathematics and statistics
  • Computing methodologies → Natural language processing
Keywords
  • Natural language processing
  • math education
  • online tutoring systems
  • text analytics
  • click-stream variables

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