Machine classification of peer comments in physics

Kwangsu Cho

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

19 Citations (Scopus)

Abstract

As part of an ongoing project where SWoRD, a Web-based reciprocal peer review system, is used to support disciplinary writing, this study reports machine learning classifications of student comments on peer writing collected in the SWoRD system. The student comments on technical lab reports were first manually decomposed and coded as praise, criticism, problem detection, solution suggestion, summary, or off-task. Then TagHelper 2.0 was used to classify the codes, using three frequently used algorithms: Naïve Bayes, Support Vector Machine, and a Decision Tree. It was found that Support Vector machine performed best in terms of Cohen's Kappa.

Original languageEnglish
Title of host publicationEducational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings
Pages192-196
Number of pages5
Publication statusPublished - 2008 Dec 1
Event1st International Conference on Educational Data Mining, EDM 2008 - Montreal, QC, Canada
Duration: 2008 Jun 202008 Jun 21

Publication series

NameEducational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings

Other

Other1st International Conference on Educational Data Mining, EDM 2008
CountryCanada
CityMontreal, QC
Period08/6/2008/6/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

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    Cho, K. (2008). Machine classification of peer comments in physics. In Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings (pp. 192-196). (Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings).