{"id":558,"date":"2021-02-24T23:50:00","date_gmt":"2021-02-24T23:50:00","guid":{"rendered":"https:\/\/oes.public.onlineeducationservices.net\/?p=558"},"modified":"2025-12-06T02:04:30","modified_gmt":"2025-12-06T02:04:30","slug":"using-learning-analytics-to-improve-student-retention","status":"publish","type":"post","link":"https:\/\/oes.com\/uk\/using-learning-analytics-to-improve-student-retention\/","title":{"rendered":"Using learning analytics to improve student retention"},"content":{"rendered":"
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Underpinned by an OES Customised Learning Analytics solution, Swinburne Online observed improved student retention and revealed valuable insights to safeguard revenue. Here\u2019s how the partnership worked:<\/p>\n

Our client<\/h3>\n

OES delivers courses on behalf of Swinburne University of Technology \u2013 an internationally recognised university, ranked in the top 1.5 per cent of universities worldwide according to The Times Higher Education University World Rankings 2021 and QS World University Rankings 2021.<\/p>\n

Student retention is an important business objective for Swinburne Online, which delivers high calibre online programs to more than 10,000 students each year and receives extremely high student satisfaction scores (QILT SES) from its online student cohort.<\/p>\n

As Swinburne University\u2019s trusted partner in online education, OES is always pursuing innovation, in this case to help improve the retention rate among its online undergraduate students.<\/p>\n

The challenge<\/h3>\n

The majority of Swinburne Online\u2019s undergraduate students are mature-age and studying part time. Juggling a degree with other commitments \u2013 such as work and family responsibilities \u2013 means the experiences and needs of this cohort are unique.<\/p>\n

\u201cUnlike on-campus undergraduates, many of Swinburne Online\u2019s students are trying to fit university into an already busy schedule and it\u2019s been many years since they last completed study,\u201d said Sacha Nouwens, Executive Director of Student Experience and Insights at OES.<\/p>\n

\u201cBy effectively integrating data from across the Swinburne Online ecosystem, our aim was to better understand and meet the needs of this non-traditional undergraduate cohort, to help them stay engaged and realise their degree and study goals.\u201d<\/p>\n

Our solution and approach<\/h3>\n

The OES analytics team captured and combined approximately 5,000 data points per student from across Swinburne Online\u2019s many platforms and channels.<\/p>\n

\u201cBy collating, interrogating and analysing this single view of each student, we were able to establish which metrics were the most likely indicators of student engagement,\u201d explained Sacha. \u201cOur sophisticated analysis of large and diverse data sets was instrumental to the success of the actions we subsequently took to improve student retention.\u201d<\/p>\n

\u201cOnce we had the right data, our next step was to build a machine-learning propensity model to pinpoint the critical metrics impacting engagement and identify those students at risk of dropping out. This propensity model brought the right data points together to establish a view of each student\u2019s level of engagement, to accurately determine whether any intervention was necessary and to inform what that intervention should be.\u201d<\/p>\n

\u201cOur next step was to empower teaching and support staff to easily access the analytics and monitor student engagement week-to-week, using custom built automated trackers.\u201d<\/p>\n

Finally, OES developed best practice intervention guides to enable both teaching and central support staff to take the right type of action, at the right time. Different approaches were used for these two staff groups:<\/p>\n