TLCANZ18: Supporting Self-Regulated Learning Strategies through Learning Analytics

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Supporting Self-Regulated Learning Strategies through Learning Analytics: An Overview of the Student and Teacher Experience



Higher education institutions are operating in a continuously shifting context influenced by a variety of measures, including globalisation, changes in government funding to universities, and advances in technology. The emergence of a digital revolution and knowledge economy, have set the tone for integration of digital technology and platforms in higher education institutions. Following this, the application and implementation of learning analytics emerges as a digital frontier in education.

With a trend towards increased responsibility on the individual to direct their own learning, it was perceived to be important that students feel empowered and effective in taking control of their learning journey. Therefore a two-fold learning analytic pilot which was developed; firstly with the aim to support students in further developing self-regulated learning strategies with the use of learning analytics dashboards, and secondly to provide Unit Coordinators with in-time feedback regarding the cohort’s progress and unit health.

The setting up of the student and Unit Coordinator analytic reports involved a collaboration with Blackboard to construct a customised analytic dashboard in Blackboard Analytics for Learn package, which pulled on data primarily from the LMS and updated every 24 hours. The project team also established working relationships with faculties, from which nine units across disciplines participated in the pilot. The pilot also engaged the efforts of key stakeholders supporting the student experience with the reports, through the support of student services which provided academic, wellbeing, and library support to students.

As the pilot unfolded over Semester 1, 2017, a research team developed an action research approach to capture the effectiveness of the pilot, through collecting data from several points throughout the semester. Taking an agile, mixed-method approach, the data was reported back to the project team in a just-in-time feedback process, in which the project team would make the appropriate amendments.

The research evaluating the effectiveness of the analytic dashboards and its application in developing self-regulated learning strategies provided interesting insights and lessons learned for the project team. A variety of themes emerged in the pilot, for example the better understanding of transitional learners from secondary to tertiary schooling, and the process of supporting the development of student tactics to enhance self-regulated learning strategies such as self-efficacy and effort management. From the Unit Coordinator perspective, learning analytics provided teaching staff a window of insight into resources that students were accessing in the LMS, and also provided insight into user perception of the reports. Finally the data gave rise to a theme which captures learning analytics as an online ‘third space’, as an online tutor.

The pilot study revealed that the relationship between the data collection process and the data itself is reflective of the nature of both learning analytics and learning. It potentially presents a ‘response-ability’; for students to be self-directing and reflexive of their learning, as well as Unit Coordinators with maintaining the health of their unit.