Document Type

Dissertation

Degree

Doctor of Philosophy

Major

Education, Educational Leadership & Policy Studies

Date of Defense

12-11-2008

Graduate Advisor

Thomas R. Schnell, Ph.D.

Committee

Lloyd Richardson Jr. Ph.D.

John Henschke Ed.D.

Pi-Chi Han Ed.D.

Abstract

E-learning has become a major delivery platform for higher education, continuing education and corporate training. The majority of e-learning research to date has taken place in academic environments using survey or qualitative research. This study used an experimental design (N = 99) to view three different models for supporting asynchronous e-learning in a corporate setting where learners are geographically distributed. The support interventions were rooted in andragogical principles of learning. Two treatment groups were provided socially engaging proactive models while the control group used a learner directed reactive authoritarian model. The purpose of this study was to see if different variables influenced trainee completion time and retention at six months of employment for new female branch administrators in a financial services company. The goal was to produce a predictive model for employee training completion and retention based upon type of e-learning support and other demographic and observed variables. Data analysis used multiple regression to determine if training completion time could be predicted. There was no significant relationship between any of the variables and time to completion. Logistic regression was used to model prediction of trainees most likely to stay on the job at six months. The only variable approaching significance from that analysis was gender of supervisor. Neither regression analysis resulted in a valid predictive model. This study used an available voluntary sample randomly assigned to treatment groups and tracked through training by dedicated support specialists trained in the different interventions. A larger sample size and different methods of treatment implementation should be studied with this population in the future.

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