Determining the Efficacy of Communications Technologies and Practices to Broaden Participation in Education: Insights from a Theory of Change

  • Nathan W. MoonEmail author
  • Robert L. Todd
  • Noel Gregg
  • Christopher L. Langston
  • Gerri Wolfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9177)


BreakThru is the core project of the Georgia STEM Accessibility Alliance (GSAA), which is supported by the Research in Disabilities Education (RDE) program of the National Science Foundation (NSF). Launched in 2010, GSAA is one of 10 RDE Alliances throughout the United States designed to broaden the participation and achievement of people with disabilities in STEM education and careers. The most distinctive feature of GSAA has been its use of virtual worlds and online communications platforms to support or implement most project activities. Empirical findings have informed the creation of a theory of change to explain how characteristics of technologically mediated mentoring practices may positively impact students’ internal characteristics across five indicators (intention to persist, increased self-advocacy, increased self-determination, decreased math anxiety, and decreased science anxiety). Successful internalization of these characteristics may be expected to increase students’ intention to persist in STEM education and support concrete steps to persist. This project seeks to fill a critical research gap and inform the field about the potential efficacy of e-mentoring programs and how they might be evaluated. It also seeks to determine appropriate methodologies and approaches for doing so.


STEM education Disability Accessibility Electronic mentoring Evaluation Theory of change 


  1. 1.
    National Science Foundation, Advisory Committee to the NSF Directorate for Education and Human Resources. Shaping the future: New expectations for undergraduate education in science, mathematics, engineering, and technology. National Science Foundation. Washington (1996)Google Scholar
  2. 2.
    National Science Foundation, Congressional Commission on the Advancement of Women and Minorities in Science, Engineering and Technology Development. Land of plenty: Diversity as America’s competitive edge in science, engineering and technology. National Science Foundation. Washington (2000)Google Scholar
  3. 3.
    National Science Foundation, Division of Science Resources Statistics. Women, minorities, and persons with disabilities in science and engineering (No. NSF-04–317). National Science Foundation. Arlington (2004)Google Scholar
  4. 4.
    National Science Board. (2010). Preparing the Next Generation of STEM Innovators: Identifying and Developing Our Nation’s Human Capital. Arlington, National Science Foundation from Accessed 29 December 2011
  5. 5.
    AccessSTEM and DO-IT. (2011). Alliances to promote the participation of students with disabilities in science, technology, engineering, and mathematics. Seattle, WA: RDE Collaborative Dissemination, University of Washington from Accessed 27 January 2015
  6. 6.
    Coates, H.: Excellent measures precede measures of excellence. J. High. Educ. Policy Manage. 29(1), 87–94 (2007)CrossRefGoogle Scholar
  7. 7.
    McDaniel, O.: The theoretical and practical use of performance indicators. High. Educ. Manage. 8(3), 125–139 (1996)Google Scholar
  8. 8.
    Kells, H. (ed.): The Development of Performance Indicators in Higher Education: A Compendium of Twelve Countries. OECD, Paris (1993)Google Scholar
  9. 9.
    Taylor, J.: Improving performance indicators in higher education: the academics perspective. J. Further High. Educ. 25(3), 379–393 (2001)CrossRefGoogle Scholar
  10. 10.
    Shin, J.C.: Impacts of performance-based accountability on institutional performance in the U.S. High. Educ. 60(1), 47–68 (2010)CrossRefGoogle Scholar
  11. 11.
    Harvey, L., Williams, J.: Fifteen years of quality in higher education. Qual. High. Educ. 16(1), 3–36 (2010)CrossRefGoogle Scholar
  12. 12.
    Donaldson, S.I., Lipsey, M.W.: Roles for theory in contemporary evaluation practice developing practical knowledge. In: Shaw, I., Greene, J.C., Mark, M.M. (eds.) The Handbook of Evaluation Policies, Programs, and Practices, pp. 56–75. Sage, Washington (2006)Google Scholar
  13. 13.
    Aune, E.: A transitional model for postsecondary-bound students with learning disabilities. Learn. Disabil. Res. Pract. 6(3), 177–187 (1991)Google Scholar
  14. 14.
    Izzo, M.V., Lamb, P.: Developing self-determination through career development activities: Implications for vocational rehabilitation counselors. J. Vocat. Rehabil. 19(2), 71–78 (2003)Google Scholar
  15. 15.
    Wehmeyer, M.: Self-determination: critical skills for outcome-oriented transition services. J. Vocat. Spec. Needs Educ. 15(1), 3–7 (1992)Google Scholar
  16. 16.
    Test, D.W., Fowler, C.H., Wood, W.M., Brewer, D.M., Eddy, S.: A conceptual framework of self-advocacy for students with disabilities. Remedial Spec. Educ. 26(1), 43–54 (2005)CrossRefGoogle Scholar
  17. 17.
    Jerusalem, M., Schwarzer, R.: Self-efficacy as a resource factor in stress appraisal processes. In: Schwarzer, R. (ed.) Self-Efficacy: Thought Control of Action, pp. 195–213. Routledge, New York (2014)Google Scholar
  18. 18.
    Schwarzer, R., Jerusalem, M.: Generalized self-efficacy scale. In: Weinman, J., Wright, S., Johnston, M. (eds.) Measures in Health Psychology A user’s portfolio Causal and Control Beliefs, pp. 35–37. NFER-Nelson, Windsor (1995)Google Scholar
  19. 19.
    VanReusen, A.K., Bos, C.S., Schumaker, J.B., Deshler, D.D.: Facilitating student participation in individualized education programs through strategy instruction. Except. Child. 60, 466–475 (1994)Google Scholar
  20. 20.
    Miller, R.J., Lombard, R.C., Corbey, S.A.: Transition Assessment: Planning Transition and IEP Development for Youth with Mild Disabilities. Pearson Education, Boston (2007)Google Scholar
  21. 21.
    Algozzine, B., Browder, D., Karvonen, M., Test, D.W., Wood, W.M.: Effects of interventions to promote self-determination for individuals with disabilities. Rev. Educ. Res. 71(1), 219–277 (2001)CrossRefGoogle Scholar
  22. 22.
    Cobb, B., Lehmann, J., Newman-Gonchar, R., Alwell, M.: Self-determination for students with disabilities a narrative metasynthesis. Career Dev. Except. Individuals 32(2), 108–114 (2009)CrossRefGoogle Scholar
  23. 23.
    Hoffman, A., Field, S., Sawilowsky, S.: Self-determination assessment battery user’s guide. Wayne State University College of Education. Detroit (2004).
  24. 24.
    Wolman, J., Campeau, P., Dubois, P., Mithaug, D., Stolarski, V.: AIR Self-Determination Scale and User Guide, p. 26. American Institute for Research, Palo Alto (1994)Google Scholar
  25. 25.
    Field, S., Hoffman, A.: Development of a model for self-determination. Career Dev. Except. Individuals 17(2), 159–169 (1994)CrossRefGoogle Scholar
  26. 26.
    Glynn, S.M., Koballa Jr, T.R.: Motivation to learn college science. In: Mintzes, J.J., Leonard, W.H. (eds.) Handbook of College Science Teaching, pp. 25–32. National Science Teachers Association Press, Arlington (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nathan W. Moon
    • 1
    Email author
  • Robert L. Todd
    • 2
  • Noel Gregg
    • 3
  • Christopher L. Langston
    • 2
  • Gerri Wolfe
    • 3
  1. 1.Center for Advanced Communications Policy (CACP)Georgia Institute of TechnologyAtlantaUSA
  2. 2.Center for Assistive Technology and Environmental Access (CATEA)Georgia Institute of TechnologyAtlantaUSA
  3. 3.University of GeorgiaAthensUSA

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