The emerging field of learning analytics is presenting new ways to provide feedback to students on their academic engagement and performance. This feedback is increasingly being delivered to students via learning analytics dashboards incorporated in learning management systems. These dashboards visualise multiple sources of real-time data about students’ learning in a consolidated view. However, despite the growing popularity of this form of feedback delivery, researchers have questioned the ability of feedback delivered via dashboards to provide useful information to students (Corrin, Kennedy & Mulder, 2013; Elias, 2011). Currently there is limited research into the effectiveness of providing feedback to students via dashboards. In particular, we know little about students’ ability to meaningfully interpret visualisations of their data and the impact this has on their study strategies. In addition, a greater understanding of the interrelationship between feedback and dashboards and the related practical and ethical considerations is required.
The aim of this research is to develop a greater understanding of how students interpret and respond to feedback delivered through dashboards and the influence it has on self-regulated learning motivations and goals (Butler & Winne, 1995). The mixed-methods study was conducted during the first semester of the 2014 academic year at an Australian university. Participants first completed a survey to establish their study motivations and to explore the learning goals that form the foundation of their self-regulated learning. This was followed by two think-aloud interviews, at different points in semester, where participants were presented with a dashboard of their data for one subject and asked about how they interpreted and could respond to this feedback. At the end of the semester, once the participants had received their final grades, they completed a second survey asking them to reflect on the usefulness of the dashboard feedback.
In this session the findings of the research will be presented, providing new insights for teachers using learning analytics to deliver feedback to students. Practical recommendations will be given to inform more effective design of feedback in blended learning environments, with a focus on how learning analytics visualisations can be designed to provide feedback in a format that can be most beneficial to student learning. The barriers and strengths of dashboards for learner engagement will also be considered. This research addresses a gap in current understanding about the implementation of learning analytics in higher education and highlights the need for greater support for students in developing literacies to enhance their interpretation of feedback on their learning.
Butler, D. L. & Winne, P. H. (1995). Feedback and Self-Regulated Learning: A Theoretical Synthesis. Review of Education Research, 65(3), 245-281.
Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In H. Carter, M. Gosper & J. Hedberg (Eds.),Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).
Elias, T. (2011). Learning Analytics: Definitions, Processes, and Potential. Retrieved from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf.
|Affiliation||University of Melbourne|