Automatic assessment of students' free-text answers with support vector machines

Wen-Juan Hou, Jia Hao Tsao, Sheng Yang Li, Li Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationTrends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings
Pages235-243
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2010 Dec 1
Event23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 - Cordoba, Spain
Duration: 2010 Jun 12010 Jun 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6096 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
CountrySpain
CityCordoba
Period10/6/110/6/4

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Keywords

  • Free-text assessment
  • natural language processing
  • support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hou, W-J., Tsao, J. H., Li, S. Y., & Chen, L. (2010). Automatic assessment of students' free-text answers with support vector machines. In Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings (PART 1 ed., pp. 235-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6096 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13022-9_24