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Can Blended Learning Close the Student Achievement Gap between Regions? The CCNA Case

, Gazi University, Turkey ; , Orta Dogu Teknik Universitesi, Turkey ; , , Indiana University, United States

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Las Vegas, Nevada, USA ISBN 978-1-880094-66-2 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA


This paper examines the student and school factors (especially geographic location) on student achievement in the Cisco Networking Academy, a technology enhanced learning environment. The instructional model in the Cisco Networking Academy combines face-to-face learning with online curriculum and instructional materials that are distributed over the Internet. Due to socioeconomic differences, student achievement differences between urban and suburban regions are a major issue in U.S. schools. We believe that using a technology enhanced learning environment may close this achievement gap. After accounting for ability and motivation factors, this study, conducted with 4670 high school students in 386 high schools, found that students located in different geographical locations achieve equally well in the networking program. The results concluded that this combination of technology-enhanced classroom learning environment help students in different regions equally well to achieve in the program.


Cakir, H., Delialioglu, O., Dennis, A. & Duffy, T. (2008). Can Blended Learning Close the Student Achievement Gap between Regions? The CCNA Case. In C. Bonk, M. Lee & T. Reynolds (Eds.), Proceedings of E-Learn 2008--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2529-2535). Las Vegas, Nevada, USA: Association for the Advancement of Computing in Education (AACE). Retrieved December 19, 2018 from .


View References & Citations Map


  1. Barker, B. (1985). Curricular offerings in small and large high schools: How broad is the disparity? Research in Rural Education, 3(1), 35-38.
  2. Brown, K.M., Anfara, V.A., & Roney, K. (2004). Student achievement in high performing, suburban middle schools and low performing, urban middle schools: Plausible explanations for the differences. Education and Urban Society, 36(4), 428-456.
  3. Cakir, H. (2006). Effects of teacher characteristics and practices on student achievement in high schools with standards-based curriculum. Unpublished doctoral dissertation, Indiana University, Bloomington.
  4. Crombie, G., & Abarbanel, T. (2000). Bridging the gender gap in high-technology education. NASSP Bulletin, 84(618), 64-73.
  5. Gray, J., & Jesson, D. (1990). Estimating differences in the examination of performances. Oxford Review of Education, 16(2), 137-158.
  6. Hakkinen, I., Kirjavainen, T., & Uusitalo, R. (2003). School resources and student achievement revisited: New evidence from panel data. Economics of Education Review, 22(3), 329-335.
  7. Lee, J. (2001). Interstate Variations in Rural Student Achievement and Schooling Conditions. ERIC Digest (ERIC Digests). Charleston, WV.
  8. Lee, J., & McIntire, W.G. (1999). Understanding Rural Student Achievement: Identifying Instructional and Organizational Differences between Rural and Nonrural Schools. Paper presented at the the Annual Meeting of the American Educational Research Association, Montreal, Quebec, Canada.
  9. Lee, V.E. (2000). Using hierarchical linear modeling to study social contexts: The case of school effects. Educational Psychologist, 35(2), 125-141.
  10. Osguthorpe, R.T., & Graham, C.R. (2003). Blended learning environments. Quarterly Review of Distance Education, 4(3), 227-233.
  11. Polin, L. (2004). Learning in Dialogue With a Practicing Community. In T.M. Duffy& J. Kirkley (Eds.), Designing Environments for Distributed Learning: Learning theory and practice. Mahwah, NJ: Lawrence Erlbaum.
  12. Raundenbush, S.W., & Bryk, A.S. (2002). Hierachical Linear Models: Application and Data Analysis Methods. Thousand Oaks, CA: Sage Publications.
  13. Rowe, K., & Hill, P. (1998). Modeling educational effectiveness in classrooms: The use of multi-level structural equations to model students' progress. Educational Research& Evaluation, 4(4), 233-285.
  14. Snijders, T.A.B., & Bosker, R.J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling.. Thousand Oaks CA: Sage Publications.
  15. Walberg, H.J. (1984). Improving the productivity of America's schools. Educational Leadership, 41(8), 19-27.
  16. Welsh, E.T., Wanberg, C.R., Brown, K.G., & Simmering, M.J. (2003). E-Learning: Emerging Uses, Empirical Results and Future Directions. International Journal of Training and Development, 7(4), 245-258.
  17. Young, D.J. (1998). Rural and Urban Differences in Student Achievement in Science and Mathematics: A Multilevel Analysis. School Effectiveness& School Improvement, 9(4), 386-418.

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