Case Study A: Gauging Student Engagement in a Blended Programme

The Professional Master in Education Primary (PMEP) is a two-year blended education programme that prepares students to become primary school teachers. The online asynchronous components of the programme are delivered using the Moodle Learning Management system (LMS). The programme attracts a large number of students per cohort, making it imperative to have robust measures in place to ensure that students who are struggling are identified early and given the support they need. The first module students complete as part of the PMEP programme is Foundations of Education, a large module focusing on the psychology, philosophy, history and sociology of education. The assessment for the module draws on all four strands and requires students to have engaged diligently and consistently from the start of the programme in order to perform well. A significant aspect of the work for the module involves completing weekly tasks and activities in the Moodle learning management system (LMS). Tasks include watching presentations and videos, discussing questions on the forum, answering quiz questions, writing reflections and participating in live webinars. The programme team was interested in monitoring students’ participation in these activities with a view to identifying students who were not engaging to the required level and might require additional supports. We put in place procedures outlining how engagement data should be used and the followup steps that we should take to communicate informally with students regarding their engagement levels. High engagers would receive an e-mail congratulating them on their work and encouraging them to maintain their efforts, while low engagers would receive an e-mail informing them that they were not engaging at the required level, advising them that they needed to play a more active role in their studies and offering support in the event that they were experiencing difficulties. We were aware that Moodle has some built-in tools that makes it possible to see of record of what students were interacting with, which would allow us to compare individual students with the class in general. So we wanted to use that data to identify students who were not engaging as required.