Abstract:

The speed of the development of online learning platforms has been excitingly fast. As with any new, burgeoning market, the earlier you enter, the greater the chance of success. However, in the rush to deliver these products to market, the investment in accompanying performance analytics has been cursory. Commonly, instructors have been left with bundled ‘performance analytics’ options that are often little more than a record of a student visiting a resource or downloading a file.

It is not surprising then that the literature in this area includes findings such as no significant relation between academic performance and lecture attendance or textbook usage. But what has not yet been clearly addressed, is the importance of using the right metric to measure the engagement of the student in the online activity. If the metric does not adequately reflect students’ purposeful learning effort, then modelling its relationship with students’ academic performance is likely not to be meaningful. Records of students downloading files or opening videos are at best, a poor proxy for engagement, and at worst, a misleading indicator of performance.

To investigate this proposition, we use digital technology that comprises two components - an advanced Student Response System (SRS) used during interactive lectures, and an interactive Statistics Platform which employs adaptive learning technology and provides unlimited question practice, coupled with interactive learning support and e-Textbook access. This technology provides dedicated measures of students’ purposeful, cognitive efforts as applied to specific, online statistical learning tasks, as superior indicators of their engagement. Specifically, we use two such indicators to investigate if this more sophisticated measure of online engagement has a positive impact on the overall performance of students enrolled in a large, first-year statistics unit at an Australian university. By contributing research in this area, we address Bertheussen’s concern that ‘the current research is rather limited when considering the impact of such a (digital) technology on students’ academic performance’ (Bertheussen et al, 2016).

Consistently, across all models fitted to the combined samples from four semesters (2017-2018; n=1,597), we are able to show that active engagement in online, statistical learning tasks, as captured by these improved, task-driven measures, has significant, positive impact on the overall performance of students. Consequently, the findings from these models become uniquely powerful when interpreted in context. What students need to do to optimise the likelihood of high performance when using digital technology in an online learning space, becomes transactionally explicit. Consequently, these findings can be successfully used to both direct the explicit guidance provided by the teacher, and objectively provide timely feedback to students which exemplifies the value of online engagement.

Keywords:

Online Engagement, Student Performance, Digital Technology, Logistic Regression.