Advertisement

Measures of Writing, Math, and General Academic Knowledge

  • Donna A. Morere
Chapter

Abstract

This chapter presents the data related to writing and math skills and general academic knowledge for the VL2 Psychometric Toolkit Project. The data were generated using three subtests from the Woodcock-Johnson III (WJ-III) Tests of Achievement: Writing Fluency, Math Fluency, and Academic Knowledge. The sample was comprised of deaf college students evaluated as part of the VL2 Psychometric Toolkit. In addition to providing descriptive statistics, correlations with a brief measure of nonverbal intelligence and measures of executive, visuospatial, memory, and linguistic functioning and other areas of academic achievement administered concurrently as part of the VL2 Toolkit are presented. The relationships observed are discussed in the context of both research in the general population and, when available, previous research with deaf individuals.

Keywords

Reading Comprehension Reading Fluency Item Difficulty Fluency Task Academic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adams, A., Simmons, F., Willis, C., & Pawling, R. (2010). Undergraduate students’ ability to revise text effectively: Relationships with topic knowledge and working memory. Journal of Research in Reading, 33(1), 54–76. doi: 10.1111/j.1467-9817.2009.01432.x.CrossRefGoogle Scholar
  2. Hoskyn, M., & Swanson, H. L. (2003). The relationship between working memory and writing in younger and older adults. Reading and Writing, 16(8), 759–784.CrossRefGoogle Scholar
  3. Kelly, R. R., & Gaustad, M. G. (2007). Deaf college student’ mathematical skills relative to ­morphological knowledge, reading level, and language proficiency. Journal of Deaf Studies and Deaf Education, 12(1), 25–37.PubMedCrossRefGoogle Scholar
  4. Kelly, R. R., Lang, H. G., & Pagliaro, C. M. (2003). Mathematics word problem solving for deaf students: A survey of practices in grades 6–12. Journal of Deaf Studies and Deaf Education, 8(2), 104–119.PubMedCrossRefGoogle Scholar
  5. Kritzer, K. L. (2009). Barely started and already left behind: A descriptive analysis of the mathematics ability demonstrated by young deaf children. Journal of Deaf Studies and Deaf Education, 14(4), 409–421.PubMedCrossRefGoogle Scholar
  6. Linacre, J. M. (1994). Sample size and item calibration stability. Rasch Measurement Transactions, 7(4), 328.Google Scholar
  7. McCutchen, D. (1996). A capacity theory of writing: Working memory in composition. Educational Psychology Review, 8, 299–324.CrossRefGoogle Scholar
  8. McCutchen, D. (2000). Knowledge acquisition, processing efficiency, and working memory: Implications for a theory of writing. Educational Psychologist, 35, 13–23.CrossRefGoogle Scholar
  9. McCutchen, D. (2011). From novice to expert: Implications of language skills and writing-relevant knowledge for memory during the development of writing skill. Journal of Writing Research, 3(1), 51–68.Google Scholar
  10. McGrew, K. S., Schrank, F. A., & Woodcock, R. W. (2007). Technical manual, Woodcock-Johnson III normative update. Rolling Meadows, IL: Riverside Publishing.Google Scholar
  11. Mousley, K., & Kelly, R. R. (1998). Problem-solving strategies for teaching mathematics to deaf students. American Annuals of the Deaf, 143(4), 325–337.CrossRefGoogle Scholar
  12. Nunes, T., & Moreno, C. (2002). An intervention program for promoting deaf pupils’ achievement in mathematics. Journal of Deaf Studies and Deaf Education, 7(2), 120–133.PubMedCrossRefGoogle Scholar
  13. Olive, T., & Kellogg, R. T. (2002). Concurrent activation of high- and low-level production processes in written composition. Memory and Cognition, 30(4), 594–600.CrossRefGoogle Scholar
  14. Olive, T., Kellogg, R. T., & Piolat, A. (2008). Verbal, visual, and spatial working memory demands during text composition. Applied PsychoLinguistics, 29, 669–687. doi: 10.1017/S0142716408080284.CrossRefGoogle Scholar
  15. Peverly, S. T. (2006). The importance of handwriting speed in adult writing. Developmental Neuropsychology, 29(1), 197–216.PubMedCrossRefGoogle Scholar
  16. Sasaki, M., & Hirose, K. (1996). Explanatory variables for EFL students’ expository writing. Language Learning, 4(1), 137–174.CrossRefGoogle Scholar
  17. Schoonen, R., Snellings, P., Stevenson, M., & van Gelderen, A. (2010). Towards a blueprint of the foreign language writer: The linguistic and cognitive demands of foreign language writing. In R. Manchon (Ed.), Writing in foreign language contexts: Learning, teaching, and research (pp. 77–101). Buffalo: Multilingual Matters.Google Scholar
  18. Schoonen, R., van Gelderen, A., de Glopper, K., Hulstijn, J., Simis, A., Snellings, P., et al. (2003). First language and second language writing: The role of linguistic knowledge, speed of processing, and metacognitive knowledge. Language Learning, 53(1), 165–202.CrossRefGoogle Scholar
  19. Schrank, F. A., & Woodcock, R. W. (2007). WJ III normative update compuscore and profiles program (Version 3.0) [Computer software].Woodcock-Johnson III. Rolling Meadows, IL: Riverside.Google Scholar
  20. Traxler, C. B. (2000). The Stanford Achievement Test, 9th Edition: National norming and performance standards for deaf and hard-of-hearing students. Journal of Deaf Studies and Deaf Education, 5, 337–348.PubMedCrossRefGoogle Scholar
  21. Wendling, B. J., Schrank, F. A., & Schmitt, A. J. (2007). Educational interventions related to the Woodcock-Johnson III Tests of Achievement (Assessment Service Bulletin No. 8). Rolling Meadows, IL: Riverside Publishing.Google Scholar
  22. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Examiner’s manual. Woodcock-Johnson III tests of achievement. Itasca, IL: Riverside Publishing.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of PsychologyGallaudet UniversityWashingtonUSA

Personalised recommendations