Identifying Students’ Writing Styles by Using Computational Linguistic Approach

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 182)

Abstract

The purpose of this study was to identify stable writing styles of students for implementing the cognitive learning system of writing education. According to the previous research, writing styles of writers could be reflected by the linguistic features of writing such as ambiguity, cohesion, lexical diversity, and syntactic complexity. This study explored the method of capturing students’ writing styles by investigating the similarity of linguistic features between drafts and comments written by the same students in a peer review system. The computational linguistic tool, Coh-Merix, was used for analyzing the linguistic features of drafts and comments produced by 41 undergraduate students. The results of this study showed that there was similarity between drafts and comments in the measures of ambiguity, lexical diversity, and syntactic complexity, whereas there was difference in the measures of cohesion.

Keywords

cognitive learning computational linguistic linguistic feature writing style peer review 

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References

  1. 1.
    Yoshizawa, S., Terano, T., Yoshikawa, A.: Assessing the Impact of Student Peer Review in Writing Instruction by Using the Normalized Compression Distance. IEEE Transactions on Professional Communication 55, 85–96 (2012)CrossRefGoogle Scholar
  2. 2.
    Cho, K., MacArthur, C.: Learning by Reviewing. Journal of Educational Psychology 103, 73–84 (2011)CrossRefGoogle Scholar
  3. 3.
    Barbeiro, L.: What Happens When I Write? Pupils Writing about Writing. Reading and Writing 24, 813–834 (2011)CrossRefGoogle Scholar
  4. 4.
    Cho, K., MacArthur, C.: Student Revision with Peer and Expert Reviewing. Learning and Instruction 20, 328–338 (2010)CrossRefGoogle Scholar
  5. 5.
    Beers, S.F., Nagy, W.E.: Syntactic Complexity as a Predictor of Adolescent Writing Quality: Which measures? Which genre? Reading and Writing 22, 185–200 (2009)CrossRefGoogle Scholar
  6. 6.
    Jarvis, S., Grant, L., Bikowski, D., Ferris, D.: Exploring Multiple Profiles of Highly Rated Learner Compositions. Journal of Second Language Writing 12, 377–403 (2003)CrossRefGoogle Scholar
  7. 7.
    Mampadi, F., Chen, S.Y., Ghinea, G., Chen, M.P.: Design of Adaptive Hypermedia Learning Systems: A cognitive style approach. Computers & Education 56, 1003–1011 (2011)CrossRefGoogle Scholar
  8. 8.
    Liaw, S.S.: Investigating Students’ Perceived Satisfaction, Behavioral Intention and Effectiveness of e-learning: A case study of the Blackboard system. Computers & Education 51, 864–873 (2008)CrossRefGoogle Scholar
  9. 9.
    McCarthy, P.M., Lewis, G.A., Dufty, D.F., McNamara, D.S.: Analyzing Writing Styles with Coh-Metrix. In: Proceedings of the 19th Annual Florida Artificial Intelligence Research Society International Conference, pp. 764–770. AAAI Press, Florida (2006)Google Scholar
  10. 10.
    Crossley, S.A., McNamara, D.S.: Understanding Expert Ratings of Essay Quality: Coh-Metrix Analyses of First and Second Language Writing. International Journal of Continuing Engineering Education and Life Long Learning 21, 170–191 (2011)CrossRefGoogle Scholar
  11. 11.
    McNamara, D.S., Crossley, S.A., McCarthy, P.M.: Linguistic Features of Writing Quality. Written Communication 27, 57–86 (2010)CrossRefGoogle Scholar
  12. 12.
    Duijnhouwer, H., Prins, F.J., Stokking, K.M.: Feedback Providing Improvement Strategies and Reflection on Feedback Use: Effects on students’ writing motivation, process and performance. Learning and Instruction 22, 171–184 (2011)CrossRefGoogle Scholar
  13. 13.
    Smits, M.H.S.B., Boon, J., Sluijsmans, D.M.A., Van Gog, T.: Content and Timing of Feedback in a Web-based Learning Environment: Effects on learning as a function of prior knowledge. Interactive Learning Environments 16, 183–193 (2008)CrossRefGoogle Scholar
  14. 14.
    Moats, L., Foorman, B., Taylor, P.: How Quality of Writing Instruction Impacts High-risk Fourth Graders’ Writing. Reading and Writing 19, 363–391 (2006)CrossRefGoogle Scholar
  15. 15.
    Cho, K., Schunn, C.D.: Scaffolded Writing and Rewriting in the Discipline: A web-based reciprocal peer review system. Computers & Education 48, 409–426 (2007)CrossRefGoogle Scholar
  16. 16.
    Crossley, S.A., McNamara, D.S.: Computational Assessment of Lexical Differences in L1 and L2 writing. Journal of Second Language Writing 18, 119–135 (2009)CrossRefGoogle Scholar
  17. 17.
    Liang, T.P., Lai, H.J., Ku, Y.C.: Personalized Content Recommendation and User Satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems 23, 45–70 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of Interaction ScienceSungkyunkwan UniversitySeoulKorea

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