TY - GEN
T1 - Automatic assessment of students' free-text answers with support vector machines
AU - Hou, Wen Juan
AU - Tsao, Jia Hao
AU - Li, Sheng Yang
AU - Chen, Li
PY - 2010
Y1 - 2010
N2 - For improving the interaction between students and teachers, it is fundamental for teachers to understand students' learning levels. An intelligent computer system should be able to automatically evaluate students' answers when the teacher asks some questions. We first built the assessment corpus. With the corpus, we applied the following procedures to extract the relevant information: (1) apply the part-of-speech tagging such that the syntactic information is extracted, (2) remove the punctuation and decimal numbers because it plays the noise roles, and (3) for grouping the information, apply the stemming and normalization procedure to sentences, (4) extract other features. In this study, we treated the assessment problem as the classifying problem, i.e., classifying students' scores as two classes such as above/below 6 out of 10. We got an average of 65.28% precision rate. The experiments with SVM show exhilarating results and some improving efforts will be further made in the future.
AB - For improving the interaction between students and teachers, it is fundamental for teachers to understand students' learning levels. An intelligent computer system should be able to automatically evaluate students' answers when the teacher asks some questions. We first built the assessment corpus. With the corpus, we applied the following procedures to extract the relevant information: (1) apply the part-of-speech tagging such that the syntactic information is extracted, (2) remove the punctuation and decimal numbers because it plays the noise roles, and (3) for grouping the information, apply the stemming and normalization procedure to sentences, (4) extract other features. In this study, we treated the assessment problem as the classifying problem, i.e., classifying students' scores as two classes such as above/below 6 out of 10. We got an average of 65.28% precision rate. The experiments with SVM show exhilarating results and some improving efforts will be further made in the future.
KW - Free-text assessment
KW - natural language processing
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=79551533163&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79551533163&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13022-9_24
DO - 10.1007/978-3-642-13022-9_24
M3 - Conference contribution
AN - SCOPUS:79551533163
SN - 3642130216
SN - 9783642130212
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 243
BT - Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings
T2 - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
Y2 - 1 June 2010 through 4 June 2010
ER -