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Reading and Writing

, Volume 31, Issue 9, pp 2095–2113 | Cite as

Using latent transition analysis to identify effects of an intelligent tutoring system on reading comprehension of seventh-grade students

  • Xuejun Ryan Ji
  • Andrea Beerwinkle
  • Kausalai (Kay) Wijekumar
  • Puiwa Lei
  • R. Malatesha Joshi
  • Shuai Zhang
Article

Abstract

Latent transition analysis (LTA) was conducted on data from a recent cluster randomized controlled study of 1808 seventh-grade students’ use of a web-based intelligent tutoring system (ITSS). This analysis goes beyond traditional variable-centered methods to focus on profiles of learners and changes in reading class membership between pre- and post-tests for students with and without receiving ITSS intervention. A four-class model was obtained, consisting of poor readers (class 1), delayed readers (class 2), proficient readers (class 3), and readers with specific deficits in problem and solution (class 4). Analysis showed that students receiving the ITSS intervention were more likely than students without the intervention to transition into the proficient class regardless of their initial reading performance profiles. However, the odds ratio of transitioning into the proficient class (as opposed to staying in the same class) in the ITSS condition, compared to the control, was the highest (4.29) for initial readers with deficits in problem and solution, followed by initial poor readers (1.66) and initial delayed readers (1.50). Findings indicated that students in the ITSS condition had larger reading improvement than students in the control condition, particularly for readers with initial deficits in problem and solution.

Keywords

Text structure strategy Latent transition analysis Reading comprehension 

References

  1. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York, NY: Wiley.Google Scholar
  2. Dole, J. A., Duffy, G. G., Roehler, L. R., & Pearson, P. D. (1991). Moving from the old to the new: Research on reading comprehension instruction. Review of Educational Research, 61(2), 239–264.CrossRefGoogle Scholar
  3. Hebert, M., Bohaty, J. J., Nelson, J. R., & Brown, J. (2016). The effects of text structure instruction on expository reading comprehension: A meta-analysis. Journal of Educational Psychology, 108(5), 609–629.CrossRefGoogle Scholar
  4. Kaldenberg, E. R., Watt, S. J., & Therrien, W. J. (2015). Reading instruction in science forstudents with learning disabilities: A meta-analysis. Learning Disability Quarterly, 38(3), 160–173.CrossRefGoogle Scholar
  5. Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16(2), 191–225.CrossRefGoogle Scholar
  6. Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. Little (Ed.), Oxford handbook of quantitative methods. Oxford: Oxford University Press.Google Scholar
  7. Meyer, B. J. F. (1975). The organization of prose and its effects on memory, Amsterdam. The Netherlands: North-Holland.Google Scholar
  8. Meyer, B. J. F. (1987). Following the author's top-level structure: An important skill for reading comprehension. In R. Tierney, J. Mitchell, & P. Anders (Eds.), Understanding readers' understanding. Hillsdale, NJ: Erlbaum.Google Scholar
  9. Meyer, B. J. F., & Poon, L. W. (2001). Effects of structure strategy training and signaling on recall of text. Journal of Educational Psychology, 93, 141–159.CrossRefGoogle Scholar
  10. Meyer, B. J. F., Brandt, D. M., & Bluth, G. J. (1980). Use of the top-level structure in text: Key for reading comprehension of ninth-grade students. Reading Research Quarterly, 16, 72–103.CrossRefGoogle Scholar
  11. Meyer, B. J. F., Middlemiss, W., Theodorou, E., Brezinski, K. L., McDougall, J., & Bartlett, B. J. (2002). Effects of structure strategy instruction delivered to fifth-grade children using the Internet with and without the aid of older adult tutors. Journal of Educational Psychology, 94, 486–519.CrossRefGoogle Scholar
  12. Meyer, B. J. F., Wijekumar, K. K., & Lin, Y. (2011). Individualizing a web-based structure strategy intervention for fifth graders’ comprehension of nonfiction. Journal of Educational Psychology, 103(1), 140–168.CrossRefGoogle Scholar
  13. Meyer, B. J. F., Wijekumar, K., Middlemiss, W., Higley, K., Lei, P., Meier, C., et al. (2010). Web-based tutoring of the structure strategy with or without elaborated feedback or choice for fifth- and seventh-grade readers. Reading Research Quarterly, 45(1), 62–92.CrossRefGoogle Scholar
  14. Meyer, B. J. F., Young, C. J., & Bartlett, B. J. (1989). Memory improved: Reading and memory enhancement across the life span through strategic text structures. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  15. Morgan, G. B. (2015). Mixed mode latent class analysis: An examination of fit index performance for classification. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 76–86.CrossRefGoogle Scholar
  16. National Assessment of Educational Progress (NAEP). (2017). Retrieved April 24, 2018 from https://www.nationsreportcard.gov/.
  17. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.Google Scholar
  18. O’Reilly, T., & McNamara, D. S. (2007). The impact of science knowledge, reading skill, and reading strategy knowledge on more traditional “high-stakes” measures of high school students’ science achievement. American Educational Research Journal, 44(1), 161–196.CrossRefGoogle Scholar
  19. Vermunt, J. K., & Magidson, J. (2016). Technical guide for latent GOLD 5.1: basic, advanced, and syntax. Belmont: Statistical Innovations Inc.Google Scholar
  20. Wijekumar, K. K., Meyer, B. J., & Lei, P. (2012). Large-scale randomized controlled trial with 4th graders using intelligent tutoring of the structure strategy to improve nonfiction reading comprehension. Educational Technology Research and Development, 60(6), 987–1013.CrossRefGoogle Scholar
  21. Wijekumar, K. K., Meyer, B. J., & Lei, P. (2017). Web-based text structure strategy instruction improves seventh graders’ content area reading comprehension. Journal of Educational Psychology.  https://doi.org/10.1037/edu0000168.CrossRefGoogle Scholar
  22. Wijekumar, K., Meyer, B.J.F., Lei, P-W, Hernandez, A., August, D. (n.a.). Effects of web-based text structure instruction for 4–6th grade Spanish Els reading comprehension. Reading and Writing: An Interdisciplinary Journal (in press).Google Scholar
  23. Wijekumar, K., Meyer, B. J. F., Lei, P.-W., Lin, Y., Johnson, L. A., Shurmatz, K., et al. (2014). Improving reading comprehension for 5th grade readers in rural and suburban schools using web-based intelligent tutoring systems. Journal of Research in Educational Effectiveness, 7(4), 331–357.  https://doi.org/10.1080/19345747.2013.853333.CrossRefGoogle Scholar
  24. Williams, J. P., & Pao, L. S. (2011). Teaching narrative and expository text structure to improve comprehension. In R. O’Connor & P. Vadasy (Eds.), Handbook of reading interventions (pp. 254–278). New York: Guilford Press.Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Xuejun Ryan Ji
    • 1
  • Andrea Beerwinkle
    • 2
  • Kausalai (Kay) Wijekumar
    • 2
  • Puiwa Lei
    • 3
  • R. Malatesha Joshi
    • 2
  • Shuai Zhang
    • 2
  1. 1.The University of British ColumbiaVancouverCanada
  2. 2.Texas A&M UniversityCollege StationUSA
  3. 3.The Pennsylvania State UniversityUniversity ParkUSA

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