An Intelligent Tutoring System to Evaluate and Advise on Lexical Richness in Students Writings

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8095)


Lexical competence, the writer ability to use properly vocabulary, becomes a basic issue of a writing instructor when reviewing drafts. Here, we present the basic part of a web-based intelligent tutoring system to provide student guidance and evaluation in structuring research proposals. We elaborate a network-based model to follow the progress of each student in the development of the project, supply assignments and personalized feedback on each evaluation. This tutor includes for now a module for assessing the lexical richness, in terms of three measures: variety, density, and sophistication, that are described. We also explain the methodology for pilot testing with undergraduate students, whose results were encouraging, indicating that the tutor indeed helps students.


intelligent tutoring system lexical richness density variety sophistication 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McNamara, D., Raine, R., Roscoe, R., Crossley, S., Jackson, G., Dai, J., Cai, Z., Renner, A., Brandon, R., Weston, J., Dempsey, K., Carney, D., Sullivan, S., Kim, L., Rus, V., Floyd, R., McCarthy, P., Graesser, A.: The Writing-Pal: Natural language algorithms to support intelligent tutoring on writing strategies. In: Applied Natural Language Processing and Content Analysis, pp. 298–311. IGI Global, Hershey (2012)Google Scholar
  2. 2.
    Rospide, C.G., Puente, C.: Virtual Agent Oriented to e-learning Processes. In: Procs. International Conference on Artificial Intelligence, Las Vegas, NV (2012)Google Scholar
  3. 3.
    Olney, A.M., D’Mello, S., Person, N., Cade, W., Hays, P., Williams, C., Lehman, B., Graesser, A.: Guru: A Computer Tutor That Models Expert Human Tutors. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 256–261. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Graesser, A., D’Mello, S., Craig, S., Witherspoon, A., Sullins, J., McDaniel, B., Gholson, B.: The Relationship between Affective States and Dialog Patterns during Interactions with Autotutor. J. Interactive Learning Research 19(2), 293–312 (2008)Google Scholar
  5. 5.
    Grobe, C.: Syntactic Maturity, Mechanics, and Vocabulary as Predictors of Quality Ratings. Research in the Teaching of English 15(1), 75–85 (1981)Google Scholar
  6. 6.
    Roy, S.: Non-Native English Speaking Students at University: Lexical Richness and Academic Success. Doctoral Thesis, University of Calgary, Canada (2010)Google Scholar
  7. 7.
    Schwarm, S., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Procs of the 43rd Annual Meeting on Association for Computational Linguistics (ACL 2005), pp. 523–530 (2005)Google Scholar
  8. 8.
    González, S., López-López, A.: Supporting the Review of Student Proposal Drafts in Information Technologies. In: Procs. ACM SIGITE 2012 & RIIT, pp. 215–220 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Universidad de la SierraMoctezumaMéxico
  2. 2.Instituto Nacional de Astrofísica, Óptica y ElectrónicaMéxico
  3. 3.Facultad de Ciencias de la ComputaciónBUAPMéxico

Personalised recommendations