Show simple item record

FieldValueLanguage
dc.contributor.authorArthars, Natasha
dc.contributor.authorDollinger, Mollie
dc.contributor.authorVigentini, Lorenzo
dc.contributor.authorLiu, Danny Yen-Ting
dc.contributor.authorKondo, Elsuida
dc.contributor.authorKing, Deborah M.
dc.date.accessioned2021-05-10T02:19:32Z
dc.date.available2021-05-10T02:19:32Z
dc.date.issued2019en_AU
dc.identifier.urihttps://hdl.handle.net/2123/25035
dc.description.abstractFrom 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.en_AU
dc.language.isoenen_AU
dc.publisherSpringeren_AU
dc.relation.ispartofUtilizing Learning Analytics to Support Study Successen_AU
dc.rightsCopyright All Rights Reserveden_AU
dc.subjectlearning analyticsen_AU
dc.subjectpersonalizationen_AU
dc.subjectrelational pedagogyen_AU
dc.subjectteacher adoptionen_AU
dc.subjectstudent supporten_AU
dc.subjectlearning analytics lifecycleen_AU
dc.titleEmpowering Teachers to Personalize Learning Supporten_AU
dc.typeBook chapteren_AU
dc.subject.asrc0899 Other Information and Computing Sciencesen_AU
dc.subject.asrc1399 Other Educationen_AU
dc.identifier.doi10.1007/978-3-319-64792-0_13
dc.rights.otherArthars 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 JK. (eds) Utilizing Learning Analytics to Support Study Success. Springer, Cham. https://doi.org/10.1007/978-3-319-64792-0_13en_AU
usyd.facultySeS faculties schools::Education Portfolioen_AU
usyd.citation.spage223en_AU
usyd.citation.epage248en_AU
workflow.metadata.onlyNoen_AU


Show simple item record

Associated file/s

Associated collections

Show simple item record

There are no previous versions of the item available.