Gender Differences in Perceived Equality and Personal Knowledge System Development on Personal Learning Network

Regina Juchun Chu^*

Center for Teacher Education, National Tsing Hua University, Hsinchu City, Taiwan.

DOI: 10.4236/jss.2014.212008                

Abstract

The purpose of this research is to investigate how personal learning network (PLN) facilitates individuals to build up their own epistemologies of the interpretation for their knowledge system. Based on three interviews and literature reviews, this study intends to develop and validate a personal epistemology development scale (PEDS) on PLNs to understand learner knowledge constructions. 561 valid data from the participants in two studies (exploratory and confirmatory factor analysis) were analyzed for the research purpose. The results of these studies supported an 18-item, 4-factor PEDS: description, analysis, vision, and strategy. The results also reveal that equality significantly influences the process of theory development especially for the stage of vision. The result moreover shows gender differences existing in the perception of equality on PLN environment, in the description and in the vision stages.

Keywords

Personal Learning Network, Adult Learning, Constructivism

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Amis, D. (2002) Web Logs: Online Navel Gazing? http://www.netfreedom.Org/news.asp?item=190
[2]	Technorati (2008) State of the PLN Osphere Report. http://technorati.com
[3]	Bunch, C. and Frost, S. (2000) “Empowerment,” and “Women’s Human Right”. Routledge International Encyclopedia of Women’s Studies, New York.
[4]	Tisdell, E.J. (1998) Poststructural Feminist Pedagogies: The Possibilities and Limitations of Feminist Emancipatory Adult Learning Theory and Practice. Adult Education Quarterly, 48, 139-156. http://dx.doi.org/10.1177/074171369804800302
[5]	Kerlinger, F.N. (1986) Foundations of Behavioral Research. 3rd Edition, Holt, Rinehart and Winston, New York.
[6]	Seley, H. (1964) From Dream to Discovery: On Being a Scientist. McGraw-Hill, New York.
[7]	Bunch, C. (1981) Building Feminist Theory: Essays from Quest. Edited with Flax, Freeman, Hartsock, and Mautner, Longman Inc., New York.
[8]	Hayes, E. (1989) Insight from Women’s Experiences for Teaching and Learning. New Directions for Continuing Education, 48, 55-66. http://dx.doi.org/10.1002/ace.36719894307
[9]	Kaiser, H.F. (1960) The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20, 141-151. http://dx.doi.org/10.1177/001316446002000116
[10]	Kline, R.B. (2005) Principles and Practice of Structural Equation Modeling. 2nd Edition, The Guilford Press, New York.
[11]	Hair, Jr., J.E., Black, W.C., Babin, B.J. and Anderson, E. (2010) Multivariate Data Analysis. 7th Edition, Prentice-Hall, Upper Saddle River.
[12]	Hu, L. and Bentler, P.M. (1999) Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6, 1-55. http://dx.doi.org/10.1080/10705519909540118
[13]	MacCallum, R.C., Browne, M.W. and Sugawara, H.M. (1996) Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods, 1, 130-149. http://dx.doi.org/10.1037/1082-989X.1.2.130
[14]	Maruyama, G.M. (1988) Basics of Structural Equation Modeling. Sage, Thousand Oaks.