This paper sets out to examine and critically evaluate quantitative data generated via analytics reports available in Blackboard Learn, and qualitative student input in order to get a sense of online learning patterns. The students are staff members, undertaking a fully online Teaching and Learning in Higher Education professional development qualification. Over the course of semester, students are required to access text and video resources to scaffold participation in 6 discussion forums around various topics, and submit 3 assignments which are part of their teaching portfolio.
Underpinning this research is our belief that “learning analytics becomes most impactful when data is used to empower both instructor and student” (Dawson & Hubball, 2014, p. 70); particularly when our ‘students’ are also educators, across a variety of disciplines and teaching context. As facilitators of fully online programmes, we were interested in exploring the following research questions:
1. What can we as facilitators of the programme learn from ‘staff as student’ activity and usage while undertaking a fully online qualification?
2. What does this data tell us about staff as students and their online behaviour as learners?
3. Can staff apply this information about their learning behaviour to examine their role as students?
4. What implications might this have for their own teaching, and indeed our own delivery of this programme within the emerging state of supercomplexity, ‘where traditional concepts of the professional, professionalism, and professional life are being reconstituted’? (Barnett, 2000).
For this mixed methods research, we collected data from staff as students engaged in the fully online Certificate in Teaching and Learning in Higher Education Qualification. At the end of Semester 2, data was collected via the various reporting tools available in the Learner Management System (LMS): Blackboard, which included reports around statistics for the average number posts in a discussion forum, average time spent on tasks, average number of times resources were accessed, ratios of facilitator to student interactions via discussion boards. We then triangulated this data against 20 randomly selected, anonymous, student reflective journals, which detailed their learning as online students, in order to provide a rich picture of their overall experience.
Emergent results have indicated that time (or lack thereof) and timing of tasks can make it difficult for academics to fully engage in all elements of the course which restricts the creation of a robust online community of practice. We have also found a disconnect between our expectations of time spent online by students vs the reality. The paper will address these issues and focus on possible interventions for the future development of the programme in the pursuit of professional, scholarly development of staff/faculty.
Barnett, R. (2000). University knowledge in an age of supercomplexity. Higher education, 40(4), 409-422.
Dawson, S., and Hubball, H. (2014). Curriculum Analytics: Application of Social Network Analysis for Improving Strategic Curriculum Decision-Making in a Research-Intensive University. Teaching and Learning Inquiry: The ISSOTL Journal, 2(2), 59-74.