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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.

We show how the principles of flipped learning that have been successfully applied to analytics classes taught face-to-face (F2F) at the undergraduate and graduate levels were emulated in corresponding online classes. Student satisfaction in the online flipped analytics classes was compared to student satisfaction in the F2F flipped analytics classes. Data were collected between the Spring 2016 and Fall 2018 semesters and involved two instructors with a sample of 726 students. The results of an independent samples t-test showed that there was no significant difference in satisfaction between the online and F2F offerings. A binary logistics regression analysis on the data revealed that whether the flipped course was taught F2F or online had no significant effect on students recommending the course to their peers. The results suggest that flipped learning is transferrable to online analytics courses and yields student satisfaction at par with equivalent F2F flipped courses.

This article applies Deming's Plan-Do-Study-Act (PDSA) cycle of continual improvement to a course taught with both blended and online sections. It uses Relative Proximity Theory, an extension of the Theory of Transactional Distance, to measure the quality of online/blended learning as a function of the obstacles to a student's full engagement with a course. These obstacles can arise because of the student's transactions with fellow students, with the instructor, with the course content, or from the instructional technology used in the course. Using stepwise multiple regression, the most significant obstacles can be identified. Once the obstacles are known, strategies to reduce them are planned and implemented. Logistic regression is used to determine if the realized reductions are statistically significant and for setting the stage for further improvements.

Transactional distance has been defined as barriers to students' engagement with learning in the online environment. This research updates Zhang's (2003) scale of transactional distance, which quantified the extent of the barriers, in light of the massive changes in today's web-based learning environment. The resulting revised scale of transactional distance (RSTD) is a parsimonious version with 12 elements (compared to 31 original) that measure the transactional distance between student and teacher, student and student, and student and content. The RSTD has excellent factorial validity and reliability, yields better fit statistics, and is easier and less time-consuming to apply. The transactional distance values are unique predictors of student satisfaction, which is correlated to learning and persistence (a major concern with massive open online courses). This information can form the basis for a systematic approach to improving the design of today's contemporary open, flexible, distance education courses and thus serve as a valuable tool for researchers and educators alike.

A study was conducted to evaluate the AXIS (algorithms combined with knowledge systems in an interactive sequence) solution framework. The framework combines structured algorithms and knowledge-based expert systems for developing best and expert heuristic solutions. The study is based on aggregate production planning. Results show that the AXIS framework produce high quality solutions at less cost.