TY - GEN
T1 - Automated approaches for detecting integration in student essays
AU - Hughes, Simon
AU - Hastings, Peter
AU - Magliano, Joseph
AU - Goldman, Susan
AU - Lawless, Kimberly
PY - 2012
Y1 - 2012
N2 - Integrating information across multiple sources is an important literacy skill, yet there has been little research into automated methods for measuring integration in written text. This study investigated the efficacy of three different algorithms at classifying student essays according to an expert model of the essay topic which categorized statements by argument function, including claims and integration. A novel classification algorithm is presented which uses multi-word regular expressions. Its performance is compared to that of Latent Semantic Analysis and several variants of the Support Vector Machine algorithm at the same classification task. One variant of the SVM approach worked best overall, but another proved more successful at detecting integration within and across texts. This research has important implications for systems that can gauge the level of integration in written essays.
AB - Integrating information across multiple sources is an important literacy skill, yet there has been little research into automated methods for measuring integration in written text. This study investigated the efficacy of three different algorithms at classifying student essays according to an expert model of the essay topic which categorized statements by argument function, including claims and integration. A novel classification algorithm is presented which uses multi-word regular expressions. Its performance is compared to that of Latent Semantic Analysis and several variants of the Support Vector Machine algorithm at the same classification task. One variant of the SVM approach worked best overall, but another proved more successful at detecting integration within and across texts. This research has important implications for systems that can gauge the level of integration in written essays.
UR - http://www.scopus.com/inward/record.url?scp=84862493453&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-30950-2_35
DO - 10.1007/978-3-642-30950-2_35
M3 - Conference contribution
AN - SCOPUS:84862493453
SN - 9783642309496
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 274
EP - 279
BT - Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
T2 - 11th International Conference on Intelligent Tutoring Systems, ITS 2012
Y2 - 14 June 2012 through 18 June 2012
ER -