License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2019.25
URN: urn:nbn:de:0030-drops-103895
URL: https://drops.dagstuhl.de/opus/volltexte/2019/10389/
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Crossley, Scott ; Karumbaiah, Shamya ; Ocumpaugh, Jaclyn ; Labrum, Matthew J. ; Baker, Ryan S.

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

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OASIcs-LDK-2019-25.pdf (0.3 MB)


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.

BibTeX - Entry

@InProceedings{crossley_et_al:OASIcs:2019:10389,
  author =	{Scott Crossley and Shamya Karumbaiah and Jaclyn Ocumpaugh and Matthew J. Labrum and Ryan S. Baker},
  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 =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Maria Eskevich and Gerard de Melo and Christian F{\"a}th and John P. McCrae and Paul Buitelaar and Christian Chiarcos and Bettina Klimek and Milan Dojchinovski},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10389},
  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}
}

Keywords: Natural language processing, math education, online tutoring systems, text analytics, click-stream variables
Collection: 2nd Conference on Language, Data and Knowledge (LDK 2019)
Issue Date: 2019
Date of publication: 16.05.2019


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