This week I chose to visualise how often I accessed my university’s Moodle for work. This data is obtained through manually counting my access to Moodle from Google Chrome history. Each stroke on the drawing represents one instance of access to Moodle recorded by Google Chrome. This is exhaustive as I use Chrome as my sole web browser.
In an institute that has any form of blended learning initiative / strategy, a learning management system (LMS) tends become the official space for dissemination of learning resources.
For teachers, as more of learning takes place in the LMS, there could be an expectation for them to spend certain level of efforts in the LMS, such as putting together activities, moderating forums, answering questions etc. Such data visualisation can be utilised to monitor teachers’ engagement in the LMS. With a population level analysis, teachers who spend little to no time at LMS could be flagged by their institute as being resistant to teaching innovation or even disengaged from teaching duties. This could have very real implications for performance management or being used as evidence for considering promotions / renewals.
Likewise, students can also be monitored in similar manner, and who go to the LMS and watch lectures with their study group (rather than on their own) are likely not picked up by such data analytics, and may get wrongly labelled as “disengaged students”. Such labelling could have longer-term impact on the students’ welfare (e.g. special considerations for assessments, moderation of assessment marks etc.).