Relationships among transactional distance variables in asynchronous computer conferences : A correlational study. Master of Distance Education thesis, Athabasca University

Abstract

The purpose of this exploratory study with quantitative data was to examine the relationships, in the context of computer-mediated asynchronous conferences, among indicators for the main variables in Moore’s Theory of Transactional Distance. It also examined the relationships between indicators for transactional distance and students’ learning success in classes that utilized the conferences. Participants in the study were students enrolled in distance education courses at a major distance education university in Canada in the fall term of 2002. All participants were volunteers. They completed a questionnaire to describe their perceptions of dialogue, course structure, transactional distance, and their autonomy in their courses. Results of this study partially supported the predicted relationships between variables. Results inconsistent with theory were in the form of correlations too small to be statistically significant rather than being of opposite sign. There was a relatively high proportion of statistically significant correlations between dialogue and transactional distance; they showed high dialogue corresponded with low transactional distance. Structure variables separated into two groups; one appeared unrelated to transactional distance and the other showed positive correlations with it. There were few significant correlations between autonomy and transactional distance, autonomy and structure, or structure and dialogue indicators. One group of dialogue indicators showed a high proportion of significant correlations with autonomy indicators, all of which were positive; the rest showed very few. There were no significant correlations between transactional distance and student learning success indicators. The results of this study were consistent with Moore’s statement that dialogue, structure, autonomy and transactional distance refer to clusters of variables.