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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, and Ryan S. Baker

Published in: OASIcs, Volume 70, 2nd Conference on Language, Data and Knowledge (LDK 2019)


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.

Cite as

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)


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@InProceedings{crossley_et_al:OASIcs.LDK.2019.25,
  author =	{Crossley, Scott and Karumbaiah, Shamya and Ocumpaugh, Jaclyn and Labrum, Matthew J. and Baker, Ryan S.},
  title =	{{Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{25:1--25:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Eskevich, Maria and de Melo, Gerard and F\"{a}th, Christian and McCrae, John P. and Buitelaar, Paul and Chiarcos, Christian and Klimek, Bettina and Dojchinovski, Milan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2019.25},
  URN =		{urn:nbn:de:0030-drops-103895},
  doi =		{10.4230/OASIcs.LDK.2019.25},
  annote =	{Keywords: Natural language processing, math education, online tutoring systems, text analytics, click-stream variables}
}
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