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ReaderBench, an Environment for Analyzing Text Complexity and Reading Strategies

  • Mihai Dascalu
  • Philippe Dessus
  • Ştefan Trausan-Matu
  • Maryse Bianco
  • Aurélie Nardy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

ReaderBench is a multi-purpose, multi-lingual and flexible environment that enables the assessment of a wide range of learners’ productions and their manipulation by the teacher. ReaderBench allows the assessment of three main textual features: cohesion-based assessment, reading strategies identification and textual complexity evaluation, which have been subject to empirical validations. ReaderBench covers a complete cycle, from the initial complexity assessment of reading materials, the assignment of texts to learners, the capture of metacognitions reflected in one’s textual verbalizations and comprehension evaluation, therefore fostering learner’s self-regulation process.

Keywords

Text Cohesion Reading Strategies Textual Complexity Latent Semantic Analysis Latent Dirichlet Allocation Support Vector Machines 

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References

  1. 1.
    McNamara, D.S., Magliano, J.P.: Self-explanation and metacognition. In: Hacher, J.D., Dunlosky, J., Graesser, A.C. (eds.) Handbook of Metacognition in Education, pp. 60–81. Erlbaum, Mahwah (2009)Google Scholar
  2. 2.
    McNamara, D.S.: SERT: Self-Explanation Reading Training. Discourse Processes 38, 1–30 (2004)CrossRefGoogle Scholar
  3. 3.
    Nelson, J., Perfetti, C., Liben, D., Liben, M.: Measures of text difficulty. Technical Report to the Gates Foundation (2011)Google Scholar
  4. 4.
    Tapiero, I.: Situation models and levels of coherence. Erlbaum, Mahwah (2007)Google Scholar
  5. 5.
    McNamara, D.S., Louwerse, M.M., McCarthy, P.M., Graesser, A.C.: Coh-Metrix: Capturing linguistic features of cohesion. Discourse Proc. 47(4), 292–330 (2010)CrossRefGoogle Scholar
  6. 6.
    McNamara, D., Boonthum, C., Levinstein, I.: Evaluating self-explanations in iSTART: Comparing word-based and LSA algorithms. In: Landauer, T.K., McNamara, D., Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis, pp. 227–241. Erlbaum, Mahwah (2007)Google Scholar
  7. 7.
    Trausan-Matu, S., Dascalu, M., Rebedea, T.: A system for automatic analysis of Computer-Supported Collaborative Learning chats. In: 12th Conf. ICALT, pp. 95–99. IEEE (2012)Google Scholar
  8. 8.
    Dascalu, M., Trausan-Matu, S., Dessus, P.: Towards an integrated approach for evaluating textual complexity for learning purposes. In: Popescu, E., Li, Q., Klamma, R., Leung, H., Specht, M. (eds.) ICWL 2012. LNCS, vol. 7558, pp. 268–278. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138. ACL, Las Cruces (1994)CrossRefGoogle Scholar
  10. 10.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for wordsense identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283. MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychol. Rev. 104(2), 211–240 (1997)CrossRefGoogle Scholar
  12. 12.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3(4-5), 993–1022 (2003)zbMATHGoogle Scholar
  13. 13.
    Manning, C., Schütze, H.: Foundations of statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  14. 14.
    Miller, G.A.: WordNet. A Lexical Database for English. Comm. ACM 38(11), 39–41 (1995)Google Scholar
  15. 15.
    Denhière, G., Lemaire, B., Bellissens, C., Jhean-Larose, S.: A semantic space for modeling children’s semantic memory. In: Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis, pp. 143–165. Erlbaum, Mahwah (2007)Google Scholar
  16. 16.
    McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002), http://mallet.cs.umass.edu
  17. 17.
    Trausan-Matu, S., Dascalu, M., Dessus, P.: Considering textual complexity and comprehension in Computer-Supported Collaborative Learning. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 352–357. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Graesser, A.C., Singer, M., Trabasso, T.: Constructing inferences during narrative text comprehension. Psychol. Rev. 101(3), 371–395 (1994)CrossRefGoogle Scholar
  19. 19.
    François, T., Miltsakaki, E.: Do NLP and machine learning improve traditional readability formulas? In: Proc. First Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR 2012), pp. 49–57. ACL, Montréal (2012)Google Scholar
  20. 20.
    Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining. In: 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco (2003)Google Scholar
  21. 21.
    Lee, H., Peirsman, Y., Chang, A., Chambers, N., Surdeanu, M., Jurafsky, D.: Stanford’s multi-pass sieve coreference resolution system at the CoNLL-2011 shared task. In: 15th Conference on Computational Natural Language Learning, pp. 28–34 (2011)Google Scholar
  22. 22.
    McNamara, D.S., Graesser, A.C., Louwerse, M.M.: Sources of text difficulty: Across the ages and genres. In: Sabatini, J.P., Albro, E. (eds.) Assessing Reading in the 21st Century. R&L Education, Lanham (in press)Google Scholar
  23. 23.
    Geisser, S.: Predictive Inference. Chapman and Hall, New York (1993)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mihai Dascalu
    • 1
    • 2
  • Philippe Dessus
    • 2
    • 3
  • Ştefan Trausan-Matu
    • 1
  • Maryse Bianco
    • 2
  • Aurélie Nardy
    • 2
  1. 1.Computer Science DepartmentPolitehnica University of BucharestRomania
  2. 2.LSEUniv. Grenoble AlpesFrance
  3. 3.LIG-MeTAHUniv. Grenoble AlpesFrance

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