Research

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UDL Guidelines - Version 2.0: Research Evidence

Checkpoint 2.3: Support decoding of text, mathematical notation, and symbols

I. Provide Multiple Means of Representation

Language, expressions, and symbols

Summary

Language and numbers must be encoded in visual symbols in order to render them accessible through print.  That raises a new demand - decoding the symbols - which is trivial for many students but raises significant barriers for some.  The majority of the experimental studies listed here focus upon the effectiveness of providing automatic text-to-speech for students who have especial difficulty decoding text. Studies find that students' lack of fluency acts as a barrier to comprehension and that decoding support can provide students access to content. Research on automatic text-to-speech continues to grow, and we hope to expand this list as more studies become available. Furthermore, there is limited research on the effectiveness of providing support for decoding mathematical notation (Mathematical Markup Language, or "Math ML") as this is an emerging area. Again, we hope to add to this list as more research is completed. The scholarly reviews and opinion pieces provide more classroom-based perspectives on proving decoding support to students.  The specifications defining MathML are also included in this listing.

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Experimental and Quantitative Evidence:

Dalton, B., & Strangman, N. (2006). Improving struggling readers' comprehension through scaffolded hypertexts and other computer-based literacy programs. In M. C. McKenna, L.D. Labbo, R.D. Kieffer and D. Reinking (Ed.), International handbook of literacy and technology volume II (pp. 75-92). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Dalton, B., Pisha, B., Eagleton, M., Coyne, P., & Deysher, S. (2002). Engaging the text: Final report to the U.S. department of education. Peabody: CAST.

Elbro, C., Rasmussen, I., & Spelling, B. (1996). Teaching reading to disabled readers with language disorders: A controlled evaluation of synthetic speech feedback. Scandinavian Journal of Psychology, 37(2), 140-155.

Elkind, J., Black, M. S., & Murray, C. (1996). Computer-based compensation of adult reading disabilities. Annals of Dyslexia, 46(1), 159-186.

Elkind, J., Cohen, K., & Murray, C. (1993). Using computer based readers to improve reading comprehension of students with dyslexia. Annals of Dyslexia, 43, 238-259.

Hecker, L., Burns, L., Katz, L., Elkind, J., & Elkind, K. (2002). Benefits of assistive reading software for students with attention disorders. Annals of Dyslexia, 52(1), 243-272.

Higgins, E. L., & Raskind, M. H. (1997). The compensatory effectiveness of optical character Recognition/Speech synthesis on reading comprehension of postsecondary students with learning disabilities. Learning Disabilities: A Multidisciplinary Journal, 8(2), 75-87.

Higgins, E. L., & Raskind, M. H. (2005). The compensatory effectiveness of the Quicktionary Reading Pen II on the reading comprehension of students with learning disabilities. Journal of Special Education Technology, 20(1), 29-38.

Lonigan, C. J., Driscoll, K., Phillips, B. M., Cantor, B. G., Anthony, J. L., & Goldstein, H. (2003). A computer-assisted instruction phonological sensitivity program for preschool children at-risk for reading problems. Journal of Early Intervention, 25(4), 248-262.

Mioduser, D., Tur-Kaspa, H., & Leitner, I. (2000). The learning value of computer-based instruction of early reading skills. Journal of Computer Assisted Learning, 16(1), 54-63.

Montali, J., & Lewandowski, L. (1996). Bimodal reading: Benefits of a talking computer for average and less skilled readers. Journal of Learning Disabilities, 29(3), 271-279.

Mostow, J., Aist, G., Burkhead, P., Corbett, A., Cuneo, A., Eitelman, S., et al. (2003). Evaluation of an automated reading tutor that listens: Comparison to human tutoring and classroom instruction. Journal of Educational Computing Research, 29(1), 61-117.

Papalewis, R. (2004). Struggling middle school readers: Successful, accelerating intervention: Read 180 program. Reading Improvement, 41(1), 24-38.

Torgesen, J. K. (1987). Using verbatim text recordings to enhance reading comprehension in learning disabled adolescents. Learning Disabilities Focus, 3(1), 30-38.

Scholarly reviews and expert opinions:

Ausbrooks, R., et al. Mathematical markup language (MathML) Version 2.0 (second edition). Retrieved February 4, 2008 from http://www.w3.org/TR/2003/REC-MathML2-20031021/.

Balajthy, E. (2005). Text-to-speech software for helping struggling readers. Reading Online, 8(4), 1-9.

Horney, M., & Anderson-Inman, L. (1999). Supported text in electronic reading environments. Reading & Writing Quarterly: Overcoming Learning Difficulties, 15(2), 127-168.

McKenna, M. C. (1997). Electronic texts and the transformation of beginning reading. In D. Reinking, M. McKenna, L. Labbo & R. D. Kieffer (Eds.), Literacy for the 21st century: Technological transformations in a post-typographical world (pp. 45-59). Mahwah, NJ: Erlbaum.

Mckenna, M. C., Reinking, D., Labbo, L. D., & Kieffer, R. D. (1999). The electronic transformation of literacy and its implications for the struggling reader. Reading and Writing Quarterly, 15(2), 111-126.

Pisha, B., & Coyne, P. (2001). Smart from the start: The promise of universal design for learning. Remedial and Special Education, 22(4), 197-203.

Rose, D. H., & Dalton, B. (2002). Using technology to individualize reading instruction. In C. C. Block, L. B. Gambrell & M. Pressley (Eds.), Improving comprehension instruction: Rethinking research, theory, and classroom practice (pp. 257-274). San Francisco, CA: Jossey Bass Publishers.

Strangman, N., & Dalton, B. (2005). Using technology to support struggling readers: A review of the research. In D. Edyburn, K. Higgins & R. Boone (Eds.), The handbook of special education technology research and practice (pp. 545-569). Whitefish Bay, WI: Knowledge by Design.

Strangman, N., & Hall, T. E. (2003). Text transformations. Wakefield, MA: National Center on Accessing the General Curriculum.

Last Updated: 02/01/2011

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