Automatic assessment of students' free-text answers with different levels

Wen Juan Hou*, Jia Hao Tsao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 from the course in the university. With the corpus, we applied the following procedures to extract the relevant information and then built the feature model: (1) remove the punctuation and decimal numbers because it plays the noise roles, (2) apply the part-of-speech tagging such that the syntactic information is extracted, (3) for grouping the information, take the stemming and normalization procedure to sentences, and (4) extract other features. In this study, we treated the assessment problem as the classifying problem, and tried two kinds of classification strategies: two and three classifying classes. For two classes, we got an average of 66.3% precision rate at first. When adding n-gram concept to the feature model, the system reached to the average of 71.9% precision rate which increased performance by 5.6%. The same tendency emerged for three-class experiment. The experiments with SVM show exhilarating results and some improving efforts will be further made in the future.

Original languageEnglish
Pages (from-to)327-347
Number of pages21
JournalInternational Journal on Artificial Intelligence Tools
Issue number2
Publication statusPublished - 2011 Apr


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

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Automatic assessment of students' free-text answers with different levels'. Together they form a unique fingerprint.

Cite this