Date of Award

5-2013

Degree Type

Dissertation

Embargo Date

5-24-2013

Degree Name

Doctor of Philosophy (PhD)

Department

Instructional Design, Development and Evaluation

Advisor(s)

Joseph B. Shedd

Keywords

computer-supported collaborative learning, distance education, knowledge sharing, virtual learning teams

Subject Categories

Education

Abstract

This study asserts that knowledge sharing (a component of knowledge management) in distance education virtual learning teams (VLTs) is important for successful collaborative learning and that various factors characterizing person and environment can impact VLT members' knowledge sharing behavior. Factors under the category of person are VLT members' competencies for working on VLTs, and their learning goal orientation and performance goal orientation. Factors under VLT environment are social presence in the VLT, the VLT learning community, satisfaction with the VLT, task type, and instructor strategies. Knowledge sharing is defined as a behavior in which VLT members impart their expertise, insight, or understanding to other members in the VLT or to the entire team, intending for the recipients to have that knowledge in common with themselves, the sharers. The study used Bandura's (1986) model of triadic reciprocal causation as a theoretical framework. The model is suitable for this research because it considers relationships between person, environment, and behavior. First, the study identified variables that are directly related to knowledge sharing. Next, the study validated those constructs. After the constructs had been validated, they were entered into a knowledge sharing measurement model. The study empirically tested a measurement model with five latent variables, taking into account the measurement error. Next, the study cross-validated the model with multiple groups drawn from the same sample. The sample consisted of data from 1,374 participants matriculated in graduate and undergraduate programs at an online university. The data were analyzed using split sample methodology, multiple regression analysis, and structural equation modeling techniques (factor analysis and latent variable structural equation modeling- SEM). The study's findings suggest that there is a direct predictive relationship between knowledge sharing and competencies for working on VLTs, learning environment, social presence, task type, and mediating relationships for learning community, social presence, and task type in the knowledge sharing model. This study contributes to research, theory, and practice. It concludes by presenting a knowledge sharing model that can be reevaluated with distance education student populations at various kinds of distance education institutions.

Access

Open Access

Included in

Education Commons

Share

COinS