TY - GEN
T1 - Machine classification of peer comments in physics
AU - Cho, Kwangsu
PY - 2008
Y1 - 2008
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=80052273105&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052273105&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80052273105
SN - 9780615306292
T3 - Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings
SP - 192
EP - 196
BT - Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings
T2 - 1st International Conference on Educational Data Mining, EDM 2008
Y2 - 20 June 2008 through 21 June 2008
ER -