skip to main content
10.1145/2339530.2339682acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Empowering authors to diagnose comprehension burden in textbooks

Published:12 August 2012Publication History

ABSTRACT

Good textbooks are organized in a systematically progressive fashion so that students acquire new knowledge and learn new concepts based on known items of information. We provide a diagnostic tool for quantitatively assessing the comprehension burden that a textbook imposes on the reader due to non-sequential presentation of concepts. We present a formal definition of comprehension burden and propose an algorithmic approach for computing it. We apply the tool to a corpus of high school textbooks from India and empirically examine its effectiveness in helping authors identify sections of textbooks that can benefit from reorganizing the material presented.

References

  1. R. Agrawal, S. Chakraborty, S. Gollapudi, A. Kannan, and K. Kenthapadi. Quality of textbooks: An empirical study. In ACM DEV, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. R. Agrawal, S. Gollapudi, A. Kannan, and K. Kenthapadi. Identifying enrichment candidates in textbooks. In WWW, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. R. Agrawal, S. Gollapudi, K. Kenthapadi, N. Srivastava, and R. Velu. Enriching textbooks through data mining. In ACM DEV, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. B. Armbruster and T. H. Anderson. Content area textbooks. Technical report, Reading Education Report No. 23, BBN, 1981.Google ScholarGoogle Scholar
  5. B. B. Armbruster and T. H. Anderson. Producing considerate expository text: Or easy reading is damned hard writing. Technical report, Reading Education Report No. 46, BBN, 1984.Google ScholarGoogle Scholar
  6. B. K. Britton, A. Woodward, and M. R. Binkley. Learning from Textbooks: Theory and Practice. Routledge, 1993.Google ScholarGoogle Scholar
  7. B. Bruce, A. Rubin, and K. Starr. Why readability formulas fail. IEEE Transactions on Professional Communication, PC-24, 1981.Google ScholarGoogle Scholar
  8. M. Chambliss and R. Calfee. Textbooks for Learning: Nurturing Children's Minds. Wiley-Blackwell, 1998.Google ScholarGoogle Scholar
  9. R. Clark, F. Nguyen, and J. Sweller. Efficiency in learning: Evidence-based guidelines to manage cognitive load. Pfeiffer, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  10. K. Collins-Thompson and J. P. Callan. A language modeling approach to predicting reading difficulty. In HLT-NAACL, 2004.Google ScholarGoogle Scholar
  11. E. K. Dishner. Reading in the Content Areas: Improving Classroom Instruction. Kendall/Hunt, 1992.Google ScholarGoogle Scholar
  12. W. DuBay. The principles of readability. Impact Information, 2004.Google ScholarGoogle Scholar
  13. C. Fellbaum. WordNet: An electronic lexical database. MIT Press, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Gillies and J. Quijada. Opportunity to learn: A high impact strategy for improving educational outcomes in developing countries. USAID Educational Quality Improvement Program (EQUIP2), 2008.Google ScholarGoogle Scholar
  15. S. R. Goldman. Learning from text: Reflections on the past and suggestions for the future. Discourse Processes, 23, 1997.Google ScholarGoogle Scholar
  16. W. Gray and B. Leary. What makes a book readable. University of Chicago Press, 1935.Google ScholarGoogle Scholar
  17. B. Grosz and C. Sidner. Attention, intentions, and the structure of discourse. Computational linguistics, 12(3), 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. R. Guillemette. Predicting readability of data processing written materials. ACM SIGMIS Database, 18(4), 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. A. Hanushek and L. Woessmann. The role of education quality for economic growth. Policy Research Department Working Paper 4122, World Bank, 2007.Google ScholarGoogle Scholar
  20. E. B. Johnsen. Textbooks in the Kaleidoscope: A Critical Survey of Literature and Research on Educational Texts. Scandinavian University Press, 1992.Google ScholarGoogle Scholar
  21. J. S. Justeson and S. M. Katz. Technical terminology: Some linguistic properties and an algorithm for indentification in text. Natural Language Engineering, 1(1), 1995.Google ScholarGoogle Scholar
  22. R. Kate, X. Luo, S. Patwardhan, M. Franz, R. Florian, R. Mooney, S. Roukos, and C. Welty. Learning to predict readability using diverse linguistic features. In COLING, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. Kieras and C. Dechert. Rules for comprehensible technical prose: A survey of the psycholinguistic literature. Technical Report TR-85/ONR-21, University of Michigan, 1985.Google ScholarGoogle Scholar
  24. B. Lively and S. Pressey. A method for measuring the vocabulary burden of textbooks. Educational Administration and Supervision, 9(73), 1923.Google ScholarGoogle Scholar
  25. D. Marcu. The theory and practice of discourse parsing and summarization. MIT Press, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. F. Paas, A. Renkl, and J. Sweller. Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 2003.Google ScholarGoogle Scholar
  27. J. M. Pawlowski, K. C. Barker, and T. Okamoto. Quality research for learning, education, and training. Reading & Writing Quarterly, 10(2), 2007.Google ScholarGoogle Scholar
  28. J. Plass, R. Moreno, and R. Brünken. Cognitive load theory. Cambridge University Press, 2010.Google ScholarGoogle Scholar
  29. L. Polanyi and R. Scha. A syntactic approach to discourse semantics. In COLING, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. E. Pollock, P. Chandler, and J. Sweller. Assimilating complex information. Learning and Instruction, 12(1), 2002.Google ScholarGoogle Scholar
  31. R. Seguin. The elaboration of school textbooks. Technical report, ED-90/WS-24, UNESCO, 1989.Google ScholarGoogle Scholar
  32. L. Sherman. Analytics of literature: A manual for the objective study of English prose and poetry. Ginn and Company, 1893.Google ScholarGoogle Scholar
  33. S. A. Thompson and W. C. Mann. Rhetorical structure theory: A framework for the analysis of texts. IPRA Papers in Pragmatics, 1(1), 1987.Google ScholarGoogle Scholar
  34. K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In NAACL-HLT, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. H. Tyson-Bernstein. A conspiracy of good intentions: America's textbook fiasco. Technical report, Council for Basic Education, Washington, 1989.Google ScholarGoogle Scholar
  36. A. Verspoor and K. B. Wu. Textbooks and educational development. Technical report, World Bank, 1990.Google ScholarGoogle Scholar
  37. K. Wang, C. Thrasher, E. Viegas, X. Li, and P. Hsu. An overview of Microsoft Web N-gram corpus and applications. In NAACL-HLT, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. A. N. Whitehead. The organisation of thought. Proceedings of the Aristotelian Society, 17, 1916--17.Google ScholarGoogle Scholar
  39. S. Witte and L. Faigley. Coherence, cohesion, and writing quality. College Composition and Communication, 32(2), 1981.Google ScholarGoogle Scholar
  40. A. Woodward, D. L. Elliott, and C. Nagel. Textbooks in School and Society: An Annotated Bibliography and Guide to Research. Garland, 1988.Google ScholarGoogle Scholar
  41. World-Bank. Knowledge for Development: World Development Report: 1998/99. Oxford University Press, 1999.Google ScholarGoogle Scholar
  42. J. Zhao and M. Kan. Domain-specific iterative readability computation. In ACM JCDL, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Empowering authors to diagnose comprehension burden in textbooks

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2012
        1616 pages
        ISBN:9781450314626
        DOI:10.1145/2339530

        Copyright © 2012 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 August 2012

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate1,133of8,635submissions,13%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader