System, scholar or students? Which most influences online MBA course effectiveness?

Abstract

Considering the increasingly challenging resource environments in many business schools, this study examined whether course technologies, learner behaviors or instructor behaviors best predict online course outcomes so that administrators and support personnel can prioritize their efforts and investments. Based on reviewing prior online and blended management education literature, we hypothesized that instructor behaviors would be most predictive of online course outcomes. However, our study of 48 online Master of Business Administration courses found that although instructor behaviors (operationalized as teaching presence) was the strongest predictor of any of our three outcome variables (perceived learning), only student behaviors (operationalized as social presence) significantly predicted all three (course grades, perceived learning and delivery medium satisfaction). Technological characteristics, operationalized using variables from the technology acceptance model and media variety, predicted perceived learning (perceived ease of use) and delivery medium satisfaction (perceived usefulness). The paper provides recommendations for instructors and administrators based on the findings, specifically advocating for a balance between instructor and administrator involvement in course design, presentation and conduct. The paper concludes by describing research opportunities regarding the roles of social presence in and collaborative approaches to online learning in business schools.

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

“…While an increasing body of research is becoming available how students' usage and behaviour in LMS influences academic performance (e.g., Arbaugh, 2014;Macfadyen & Dawson, 2010;Marks et al, 2005;Wolff et al, 2013), how the use of e-tutorials or other formats of blended learning effects performance (e.g., Lajoie & Azevedo, 2006), and how feedback based on learning dispositions stimulates learning (Buckingham Shum and Deakin Crick (2012), to the best of our knowledge no study has looked at how all these factors can be combined into one research context, and what the relative contributions of LMSs, formative testing, e-tutorials, and applying dispositional learning analytics to student performance are. In our empirical contribution focusing on a large scale module in introductory mathematics and statistics followed by 922 students, we aim to provide a practical application of such an infrastructure based on combining longitudinal learning data from our LMS, the two e-tutorials, and (self-reported) learner data.…”

“…User behaviour data are frequently supplemented with background data retrieved from learning management systems (LMS) (Macfadyen & Dawson, 2010) and other student admission systems, such as accounts of prior education (Arbaugh, 2014;Richardson, 2012). For example, in one of the first learning analytics studies focused on 118 biology students, Macfadyen and Dawson (2010) found that some (# of discussion messages posted, # assessments finished, # mail messages sent) LMS variables but not all (e.g., time spent in the LMS) were useful predictors of student retention and academic performance.…”

Learner control, user characteristics, platform difference, and their role in adoption intention for MOOC learning in China

“…In the field of elearning, TAM could not only be used for predicting adoption behavior, but also for measuring the learning satisfaction, continuous intention to use e-learning (Pereira, Ramos, Gouvêa, & Costa, 2015), loyalty to elearning (Sánchez-Franco, Peral-Peral, & Villarejo-Ramos, 2014), course outcomes, and academic performance using e-learning (Arbaugh, 2014). The features of technology: interactivity, personalisation, accessibility, mobility, and the choice of media to present the contents (Agudo-Peregrina, Hernández-García, & Pascual-Miguel, 2014); individual characteristics such as personal innovativeness in information technology, computer self-efficacy, and demographic variables (Chow et al, 2013;Thatcher & Perrewe, 2002);the course characteristics of learning resources, course content, tutor quality, and course quality (Persico, Manca, & Pozzi, 2014;Teo, 2014); and other variables such as social influence (or social norm) and flow (Wu & Zhang, 2014), are incorporated into TAM to enhance the understanding of adopting.…”

Analysing engagement in an online management programme and implications for course design

“…A previous study [1] has focussed on learner engagement in the context of online management programmes. This study of 48 online MBA courses, from one institution, investigated the impact of course technologies, instructor behaviours and learner behaviours on perceived learning, learner performance and learner satisfaction.…”

“…Moreover, because Imperial's in-house LMS has been co-developed and evaluated with students, tutors and administrators over a number of years, acceptance of this technology by these cohorts of students is also not a primary issue in our context (c.f. [1]). However, there is still variability in students' performance and their levels of satisfaction with their courses.…”