Assessing the use of multiple sources in student essays.
The present study explored different approaches for automatically scoring student essays that were written on the basis of multiple texts. Specifically, these approaches were developed to classify whether or not important elements of the texts were present in the essays. The first was a simple pattern-matching approach called "multi-word" that allowed for flexible matching of words and phrases in the sentences. The second technique was latent semantic analysis (LSA), which was used to compare student sentences to original source sentences using its high-dimensional vector-based representation. Finally, the third was a machine-learning technique, support vector machines, which learned a classification scheme from the corpus. The results of the study suggested that the LSA-based system was superior for detecting the presence of explicit content from the texts, but the multi-word pattern-matching approach was better for detecting inferences outside or across texts. These results suggest that the best approach for analyzing essays of this nature should draw upon multiple natural language processing approaches.
College of Computing and Digital Media, DePaul University, 243 South Wabash Avenue, Chicago, IL 60604, USA. peterh@cdm.depaul.edu
Peter
P
Hastings
Simon
S
Hughes
Joseph P
JP
Magliano
Susan R
SR
Goldman
Kimberly
K
Lawless
D001185:Artificial Intelligence; D003194:Computer-Assisted Instruction / Q000379:methods*; D004521:Educational Measurement / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010506:Periodicals as Topic; D012660:Semantics; D012984:Software; D060388:Support Vector Machine; D014956:Writing