A scale for measuring relative proximity of transactional distance

Abstract

Relative proximity theory contributes to the concept of distance in distance education by defining how far a current learning environment is from an ideal learning environment. As in Moore’s (1993) theory of transactional distance, it defines distance as a psychosocial phenomenon but provides specificity to Moore’s seminal theory for learning at a distance by providing a specific answer to the question “How close are we?” in our continual journey to provide better learning environments for our students and teachers. This paper reports on our development of a statistically valid and reliable scale of relative proximity of transactional distance to measure the remaining distance to ideal. The scale adapts and extends the work by Zhang (2003), Paul et al. (2015), and Weidlich and Bastiaens (2018) and, for the first time, explicitly accounts for differences due to gender and mode of delivery.

Keywords:

• transactional distance
• Scale of Transactional Distance
• relative proximity theory
• learning environments
• needs assessment

Disclosure statement

No potential conflict of interest was declared by the author(s).

Additional information

Notes on contributors

Ravi C. Paul

Ravi Paul is chair of the Department of MIS at East Carolina University. His current research interests focus on virtual teams, online learning, and cognitive approaches to enhance decision-making. His research has appeared in journals such as Information and Management, JITE, DSJIE, and IEEE Transactions on Professional Communications.

William Swart

William Swart is a professor of marketing and supply chain management at East Carolina University. He has held leadership positions in industry and in academia. He received a NASA/JFK Group Achievement Award, the IIE Operations Research Practice Award, and the Achievement in Operations Research Medal from tINFORMS.

Kenneth R. MacLeod

Kenneth MacLeod is an associate professor of management science in the College of Business at East Carolina University. He received his PhD in management science from the University of South Carolina. His research has been published in journals such as Decision Sciences, EJOR, and DSJIE.