Navigating Through Our Journey

Constructing the Learning Log
  • Stephen C. Clark
  • Theodora Valvi
Chapter
Part of the Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth book series (DIG)

Abstract

The objective of this chapter is to navigate the reader through the research journey. This includes the purpose of the literature, focusing on the why, and respective relationships. The connections within the study will emerge, which will highlight actions, design, and implementation. A matrix will form and will encompass reoccurring actions as well as other factors such as device, location, use, and effects. The chapter will continue to provide the reader with the rest of the development of the learning log in addition to how the learning log was diffused, managed, and inspected. An analysis of all the research questions and factors will conclude the chapter.

References

  1. Bogdan, R., & Biklen, S. K. (2007). Qualitative research for education: An introduction to theory and methods. New York: Pearson/Allyn and Bacon.Google Scholar
  2. Friesner, T., & Hart, M. (2005). Learning logs: Assessment or research method. The Electronic Journal of Research Methodology, 3(2), 117–122.Google Scholar
  3. GAO. (1989). Content analysis: A methodology for structuring and analyzing written material. Washington, DC: GAO US Government Report.Google Scholar
  4. Hoover, L. A. (1994). Reflective writing as a window on preservice teachers’ thought processes. Teaching & Teacher Education, 10(1), 83–93.CrossRefGoogle Scholar
  5. Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.CrossRefGoogle Scholar
  6. Jackson, J. E. (1991). A user guide to principal components. New York: John Wiley & Sons.CrossRefGoogle Scholar
  7. Jolliffee, I. T. (1986). Springer series in statistics: Principal component analysis (2nd ed.). New York: Springer.CrossRefGoogle Scholar
  8. Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Beverly Hills, CA: Sage.Google Scholar
  9. Locantore, N., & Marron, J. S. (1999). Robust principal component analysis for functional data. Sociedad de Estadistica e. Investigacion Operativa, 8(1), 1–73.Google Scholar
  10. Takane, Y., & Shibayama, T. (1991). Principal component analysis with external information on both subjects and variables. Psychometrika, 56(1), 97–120.CrossRefGoogle Scholar
  11. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2, 37–52.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Stephen C. Clark
    • 1
  • Theodora Valvi
    • 2
  1. 1.California State University, SacramentoSan DiegoUSA
  2. 2.Independent ResearcherAthensGreece

Personalised recommendations