• DocumentCode
    1464037
  • Title

    Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support

  • Author

    Ming Liu ; Calvo, Rafael A. ; Aditomo, A. ; Pizzato, L.A.

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Univ. of Sydney, Sydney, NSW, Australia
  • Volume
    5
  • Issue
    3
  • fYear
    2012
  • Firstpage
    251
  • Lastpage
    263
  • Abstract
    In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students´ literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and Other. Using the content of the matched Wikipedia article, the system then constructs a conceptual graph structure representation for each key phrase and the questions are then generated based the structure. To evaluate the quality of the computer generated questions, we conducted a version of the Bystander Turing test, which involved 20 research students who had written literature reviews for an IT methods course. The pedagogical values of generated questions were evaluated using a semiautomated process. The results indicate that the students had difficulty distinguishing between computer-generated and supervisor-generated questions. Computer-generated questions were also rated as being as pedagogically useful as supervisor-generated questions, and more useful than generic questions. The findings also suggest that the computer-generated questions were more useful for the first-year students than for second or third-year students.
  • Keywords
    Web sites; educational administrative data processing; educational courses; natural language processing; Bystander Turing test; IT methods course; Wikipedia article; abstract concept; academic writing support; automatic question generation; computer-generated questions; conceptual graph structures; first-year students; key phrase classification; key phrase extraction; natural language processing; other category; pedagogical values; quality evaluation; research field category; research students; semiautomatic question generation; student literature review papers; supervisor-generated questions; system category; technology category; term category; Decision support systems; Electronic publishing; Encyclopedias; Internet; Natural language processing; Writing; Automatic question generation; Decision support systems; Electronic publishing; Encyclopedias; Internet; Natural language processing; Writing; natural language processing; writing support;
  • fLanguage
    English
  • Journal_Title
    Learning Technologies, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1939-1382
  • Type

    jour

  • DOI
    10.1109/TLT.2012.5
  • Filename
    6165258