INTERPRETING CRISIS OF ATTRITION AND SOCIAL ISOLATION IN UNDERGRADUATE DEGREE PROGRAMS USING ASYNCHRONOUS LEARNING SYSTEM

Richmond Adebiaye

This study evaluates hidden crisis of attrition prevalent amongst students and programs with descriptive interpretation for solving social isolation. A Descriptive research design was adopted for this study with a variable values covering three (3) undergraduate degree programs. A Simple Percentage Method, chi-square tests, Tables and weighted average were used to get clear picture of analysis, of the academic and social characteristics of newly admitted undergraduate degree seeking students (N = 45) in the College of Business and Technology at Parker University, Dallas, Texas, United States from Fall 2013 to Fall 2014. A binary logistic regression analysis is proposed and performed to predict the probability of a student dropping out. A principal component factor analysis with descriptive analysis was performed on the twelve (12) questionnaire items used for data analysis. Student outcome (persistence or dropout) was the criterion variable. Analysis was not conducted to raise the assumptions of (adequacy of sample size, presence of outliers, factorability, linearity, and multi collinearity) because of the low number sample size (N=45). However, the general structure matrix pattern was examined for item loadings of three (3) programs in the undergraduate degree plan, in order to determine the number of factors to retain for convergent validity. Methods include Eigen functions, eigen values greater than one rule, scree test and total variance utilized to interpret factor structure coherent to the variable values. Mitigating factor analysis includes pertinence of program delivery mode, course design, enrollment number, number of students attending at end of the first year, undergraduate grade point average at time of dropout or completion, admission requirement criteria if any/test scores and number of sections to degree completion or number of courses completed at time of dropout.

Landor-Ngemi, J. (2009). Student perceptions of an effective learning environment across the dimensions of synchronous, asynchronous, and face-to-face instruction (Order No. 3367178). Available from ProQuest Central; ProQuest Dissertations & Theses Global. (304998414). Retrieved from http://search.proquest.com/docview/304998414?accountid=28365

Barker, B. O. & Dickson. M. W. (1996). Distance learning technologies in K-12 schools: Past, present and future practice. Tech Trends. 41(61). 19-22.

Ali & Kohun (2006)

Freeman, M. L. (2007). Selected characteristics related to persistence to the Bachelor degree degree for vertical transfer students: Geography, age, gender, and risk. Ohio University). ProQuest Dissertations and Theses, Retrieved from http://search.proquest.com/docview/304835328?accountid=28365

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman. [6] Bandura, A. (1986). Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice-Hall.

Mumper (2010)

Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2014-15 Edition, Computer and Information Systems Managers, on the Internet at http://www.bls.gov/ooh/management/computer-and-information-systemsmanagers.htm (visited February 07, 2014). (College Board, 2006a).

Albright, M., Simonson, M., Smaldino, S., & Zvacek, S. (200-2012). Teaching and learning at a distance foundations of distance education. (5th ed., pp. 127-132). Boston, MA: Allyn & Bacon.

Allen, I. E., & Seaman, J. (2008). Online nation: Five years of growth in online learning. Retrieved August 27, 2008, from http://www.sloanc.org/resources/onlinenation.pdf

Russell, T. (2001). The no significant difference phenomenon. Raleigh, NC: North Carolina State University.

Bos, N., & Shami, N. S. (2006, October). Adapting a face-to-face role-playing simulation for online play. Educational Technology Research and Development, 54, 493. Washington: DC. Retrieved January 11, 2007, from ProQuest database

Diaz, D. P. (2002, May/June). Online drop rates revisited. The Technology Source. Retrieved December 6, 2006, from http://technologysource.org./article/online_drop_rates_revisited/

Diaz, D., & Cartnal, R. (2006). Term length as an indicator of attrition in online learning. Retrieved July 9, 2006, from http://www.innovateonline.info/index.php?view:article&id=196

Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1-16. Retrieved March 20, 2006, from the Science Direct Database.

Hawlery, P. (2003). Being bright is not enough. Springfield, IL: Charles C. Thomas.

Lewis, C. W., Ginsberg, R., Davies, T., & Smith, K. (2004). The experiences of African American Ph.D. students at a predominantly white Carnegie I – research institution. College Student Journal, 38(2), 231-245. [

Kohun, F., and Azad Ali. (2005). “Isolation feelings in doctoral programs: A case study”. Issues in Information Systems, VI (1), 379-385

Lovitts, B. E., & Nelson, C. (2000). The hidden crisis in graduate education: Attrition from PhD programs. Retrieved January 27, 2005 from http://www.aaup.org/publications/academe/2000/00nd/ND00LOVI.HTM