Abstract
From its inception, learning analytics (LA) offered the potential to be a game changer for higher education. However, accounts of its widespread implementation, especially by teachers, within institutions are rare which raises questions about its ability to scale and limits its potential to impact student success. Additionally, amidst the backdrop of higher education’s contemporary challenges including massification and diversification, entire cohorts (not just those identified as “at risk” by traditional LA) feel disconnected and unsupported in their learning journey. Increasing pressures on teachers are also diminishing their ability to provide meaningful support and personal attention to students. For LA, related adoption barriers have been identified including workload pressures, lack of suitable or customizable tools, and unavailability of meaningful data. In this chapter, we present a teacher-friendly ’LA lifecycle’ that seeks to address these challenges and critically assess the adoption and impact of a unique solution in the form of an LA platform that is designed to be adaptable by teachers to diverse contexts. In this chapter, these contexts span three universities and over 72,000 students and 1,500 teachers. This platform, the Student Relationship Engagement System (SRES), allows teachers to collect, curate, analyze, and act on data of their choosing that aligns to their specific contexts. It also provides the ability to close the loop on support actions and guide reflective practice. In contrast to other platforms that focus on data visualization or algorithmic predictions, the SRES directly helps teachers to act on data to provide at-scale personalized support for study success. This way, the nuances of learning designs and teaching contexts can be directly applied to data-informed support actions. In our case studies, we highlight how this practical approach to LA directly addressed teachers’ and students’ needs of timely and personalized support and how the platform has impacted student and teacher outcomes. Through this, we develop implications for integrating teachers’ specific needs into LA, the forms of tools that may yield impact, and perspectives on authentic LA adoption.
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- 1.
We use “teachers” in this chapter to refer to educators who design and deliver learning experiences for students. This includes coordinators who have additional responsibilities such as broader curriculum design and ownership, as well as tutors (or teaching assistants) who work under coordinators.
- 2.
“Course” is defined in this chapter as an individual component of an academic program that a student takes, usually lasting a semester. For example, it is referred to as a “unit of study” at the University of Sydney, a “subject” at the University of Melbourne, and a “course” at the University of New South Wales.
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Acknowledgments
The authors wish to thank the designers, developers, and directors who help to build and assist academics with the SRES, including but certainly not limited to Kevin Samnick, Melissa Makin, Joshua Lilly, Melanie Keep, Adam Bridgeman, Ruth Weeks, and Uli Felzmann.
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Arthars, N., Dollinger, M., Vigentini, L., Liu, D.YT., Kondo, E., King, D.M. (2019). Empowering Teachers to Personalize Learning Support. In: Ifenthaler, D., Mah, DK., Yau, J.YK. (eds) Utilizing Learning Analytics to Support Study Success. Springer, Cham. https://doi.org/10.1007/978-3-319-64792-0_13
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