Machine classification of peer comments in physics

Kwangsu Cho

Machine classification of peer comments in physics

Kwangsu Cho

Graduate School of Information

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings
Pages	192-196
Number of pages	5
Publication status	Published - 2008
Event	1st International Conference on Educational Data Mining, EDM 2008 - Montreal, QC, Canada Duration: 2008 Jun 20 → 2008 Jun 21

Publication series

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

Other

Other	1st International Conference on Educational Data Mining, EDM 2008
Country	Canada
City	Montreal, QC
Period	08/6/20 → 08/6/21

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems

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title = "Machine classification of peer comments in physics",

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: Na{\"i}ve Bayes, Support Vector Machine, and a Decision Tree. It was found that Support Vector machine performed best in terms of Cohen's Kappa.",

author = "Kwangsu Cho",

year = "2008",

language = "English",

isbn = "9780615306292",

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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. Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings, pp. 192-196, 1st International Conference on Educational Data Mining, EDM 2008, Montreal, QC, Canada, 08/6/20.

Machine classification of peer comments in physics. / Cho, Kwangsu.

Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings. 2008. p. 192-196 (Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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