Defining Quality Indicators for Special Education Technology Research

Journal of Special Education Technology

Volumn 22, Issue 3, 2007, Pages 3-18

  Gersten, Russell ^a   Edyburn, Dave ^b  

^a UNIVERSITY OF OREGON   (United States)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84871728259     PISSN: 01626434     EISSN: 23813121     Source Type: Journal    
DOI: 10.1177/016264340702200302     Document Type: Article

References (52)

1. American Education Research Association. (2006). Standards for reporting on empirical social science research. Retrieved on August 15, 2006, from http://www.aera.net/?id=1480.
2. Blackhurst A. E., (2005). Historical perspectives about technology applications for people with disabilities. In Edyburn D., Higgins K., Boone R., (Eds.), Handbook of special education technology research and practice (pp. 3–29). Whitefish Bay, WI: Knowledge by Design.
3. Boone R., Higgins K., (2005). Designing digital materials for students with disabilities. In Boruch D. R. F., (Ed.), (1997). Randomized experiments for planning and evaluation: A practical guide. Thousand Oaks, CA: Sage Publications.
4. Brantlinger E., Jimenez R., Klingner J., Pugach M., Richardson V., (2005). Qualitative studies in special education. Exceptional Children, 71(2), 195–207.
5. Browder D. M., Fallin K., Davis S., Karvonen M., (2003). Consideration of what may influence student outcomes on alternative assessment. Education and Training in Developmental Disabilities, 38(3), 255–270.
6. Bull G., Knezek G., Roblyer M. D., Schrum L., Thompson A., (2005). A proactive approach to a research agenda for educational technology. Journal of Research on Technology in Education, 37(3), 217–220.
7. DeRuyter F., (1997). The importance of outcome measures for assistive technology service delivery systems. Technology and Disability, 6, 89–104.
8. Edyburn D. L., (2004). 2003 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 19(4), 57–80.
9. Edyburn D. L., (2005). Assistive technology and students with mild disabilities: From consideration to outcome measurement. In Edyburn D., Higgins K., Boone R., (Eds.), Handbook of special education technology research and practice (pp. 239–269). Whitefish Bay, WI: Knowledge by Design.
10. Edyburn D. L., Fennema-Jansen S., Harihan P., Smith R., (2005). Assistive technology outcomes: Implementation strategies for collecting data in schools. Assistive Technology Benefits and Outcomes (e-journal). Available at: http://www.atia.org/atob/ATOBWeb/ATOBV2N1/Documents/EdyburnATOBV2N1.pdf.
11. Edyburn D., Higgins K., Boone R., (Eds.) (2005). Handbook of special education technology research and practice. Whitefish Bay, WI: Knowledge by Design.
12. Fein J., (1996). A history of legislative support for assistive technology. Journal of Special Education Technology, 13(1), 1–3.
13. Gall J. P., Borg W. R., (2002). Educational research: An introduction. White Plains, NY: Pearson Allyn & Bacon.
14. Gersten R., Baker S., Lloyd J. W., (2000). Designing high quality research in special education: Group experimental design. Journal of Special Education, 34, 2–18.
15. Gersten R., Fuchs L. S., Compton D., Coyne M., Greenwood C., Innocenti M. S., (2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71(2), 149–164.
16. Golden D. C., (2002). Instructional software accessibility: A status report (Guest Columnist). Journal of Special Education Technology, 17(1), 57–60.
17. Hannaford A. E., (1993). Computers and exceptional individuals. In Lindsey J. D., (Ed.), Computers and exceptional individuals (pp. 3–26). Austin, TX: Pro-Ed.
18. Hauser J., Malouf D. B., (1996). A federal perspective on special education technology. Journal of Learning Disabilities, 29(5), 504–511.
19. Horner R. H., Carr E. G., Halle J., McGee G., Odom S., Wolery M., (2005). The use of single-subject research to identify evidence-based practice in special education. Exceptional Children, 71(2), 165–180.
20. HyperStat Online. (n.d.). Retrieved December 13, 2006, from http://davidmlane.com/hyperstat/.
21. The Individuals with Disabilities Education Act Amendments of 1997, Public Law 105–17, 105th Congress, 1997.
22. Johnson E., Kimball K., Brown S. O., Anderson D., (2001). A statewide review of the use of accommodations in large-scale, high-stakes assessments. Exceptional Children, 67, 251–264.
23. Lindo E., (2006). The African American presence in reading intervention experiments. Remedial and Special Education, 27(3), 148–153.
24. Lovitt T. C., (1991). Preventing school dropouts: Tactics for at-risk, remedial and mildly handicapped adolescents. Austin, TX: Pro-Ed.
25. Malouf D. B., Schiller E. P., (1995). Practice and research in special education. Exceptional Children, 61(5), 414–424.
26. Mosteller F., Boruch R., (Eds.). (2002). Evidence matters: Randomized trials in education research. Washington, DC: The Brookings Institution.
27. National Center for Technology Innovation. (2005). Moving toward solutions: Assistive and learning technology for all students. Washington: DC: American Institutes for Research.
28. National Research Council. (1999). How people learn: Brain, mind, experience, and school. Bransford J., Brown A., Cocking R., (Eds.), Committee on Developments in the Science of Learning, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
29. Odom S. L., Brantlinger E., Gersten R., Horner R. H., Thompson B., Harris K. R., (2005). Research in special education scientific methods and evidence-based practices. Exceptional Children, 71(2), 137–148.
30. Okolo C. M., Bahr C. M., Rieth H. J., (1988). A retrospective review of computer-based instruction. Journal of Special Education, 12(1), 1–27.
31. Office of Technology Assessment. (1982). Technology and handicapped people. Washington, DC: U. S. Government Printing Office.
32. Porter C. L., Christian L., Poling A., (2003). Research brief: Some data concerning the reporting of participants' gender in the mental retardation literature. Mental Retardation, 41(2), 75–77.
33. QIAT Consortium. (2001). Quality indicators for assistive technology services. Special Education Technology Practice, 3(1), 14–15.
34. Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume I: RESNA resource guide for assistive technology outcomes: Measurement tools. Arlington, VA: Author.
35. Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume II: RESNA resource guide for assistive technology outcomes: Assessment instruments, tools, & checklists from the field. Arlington, VA: Author.
36. Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume III: RESNA resource guide for assistive technology outcomes: Developing domains of need and criteria of services. Arlington, VA: Author.
37. Research Methods Knowledge Base. (n.d.). Retrieved December 13, 2006, from http://www.socialresearchmethods.net/kb/.
38. Rose D. H., Meyer A., (2002). Teaching every student in the digital age. Alexandria, VA: Association for Supervision and Curriculum Development.
39. Shadish W. R., Cook T. D., Campbell D. T., (2002). Experimental and quasi-experimental designs for general causal inference. Boston, MA: Houghton Mifflin.
40. Shavelson R. J., Towne L., (Eds.). (2002). Scientific research in education. Washington, DC: National Academy Press.
41. Shriner J. G., Destefano L., (2003). Participation and accommodations in state assessment: The role of individualized education programs. Exceptional Children, 69(2), 147–161.
42. Smith R. O., (Ed.). (1996). Measuring assistive technology outcomes: Theoretical and practical considerations. Assistive Technology, 8(2), 71–130.
43. Smith R. O., (2000). Measuring assistive technology outcomes in education. Diagnostique, 25(4), 273–290.
44. The Technology-Related Assistance for Individuals with Disabilities Act of 1988, Public Law 100–407, 100th Congress, 1988.
45. Thompson B., Diamond K. E., McWilliam R., Snyder P., Snyder S. W., (2005). Evaluating the quality of evidence from correlation research for evidence-based practice. Exceptional Children, 71(2), 181–194.
46. Warby D. B., Greene M. T., Higgins K., Lovitt T. C., (1999). Suggestions for translating research into classroom practice. Intervention in School and Clinic, 34, 205–211, 223.
47. What Works Clearinghouse Technical Working Papers. (n.d.). Retrieved December 13, 2006, from http://www.whatworks.ed.gov/reviewprocess/workpapers.html.
48. Whitehurst G. J., (2003). The Institute of Education Sciences: New wine, new bottles. Paper presented at the annual conference of the American Educational Research Association, Los Angeles. Retrieved January 17, 2005, from http://www.ed.gov/rschstat/research/pubs/ies.html.
49. Willis J., (2003). Instructional technologies in schools: Are we there yet? Computers in the Schools, 20(1/2), 11–33.
50. Woodward J., (1993). The technology of technology-based instruction: Comments on the research, development, and dissemination approach to innovation. Education and Treatment of Children, 16, 345–360.
51. Woodward J., Rieth H., (1997). A historical review of technology research in special education. Review of Educational Research, 67(4), 503–536.
52. Ysseldyke J., Thurlow M., Bielinski J., House A., Moody M., Haigh J., (2001). The relationship between instructional and assessment accommodations in an inclusive state accountability system. Journal of Learning Disabilities, 34, 212–220.