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© University of South Wales Knowing you’re there: analysing technological engagement to enhance retention and success Professor Jo Smedley & Professor Clive Mulholland March 2014
© University of South Wales Abstract Student engagement is an important indicator of all types of academic attachment demonstrating active citizenship with their learning “world” (Barnett and Coate (2005), Krause and Coates, 2008). Learning analytics on technological activity data provide early predictors of change impacting on retention, achievement and success. From this learner behaviour “window”, outcomes are informing student-centred initiatives at various stages of their learner journeys. 2
© University of South Wales Session Aims Using Big Data About Analytics Case study: Learner Journey Analytics 3
© University of South Wales Page 4 Value of Big Data Analytics The goal of all organizations with access to large data collections should be to harness the most relevant data and use it for better decision making Descriptive analytics Mine past data to report, visualize and understand what has already happened – after the fact or in real time Computational complexity Predictive analytics Leverage past data to understand why something happened or to predict what will happen in the future across various scenarios Prescriptive analytics To determine which decision and/or action will produce the most effective result against a specific set of objectives and constraints Advanced analytics Business intelligence
© University of South Wales Case Study: Learner Journey Analytics Belonging and attachment Student life cycle Learning Analytics 5
© University of South Wales Learning Analytics 6 Target Setting Induction Traffic Lights Data Mining Activity Monitoring Learning Analytics
© University of South Wales Conclusions/Further Work Enhanced data transparency Wider engagement Links to:- – Admissions data – Achievement – Credit scores 7
© University of South Wales Questions/Followup Webpage: 8
© University of South Wales 9 Internal data Module surveys x n Student experience surveys x n Big Data Internal data Activity monitoring External data Managing Information
© University of South Wales 10 External data NSSPRESPTESHESADLHE International Student barometer Big Data Internal data Activity monitoring External data Managing Information
© University of South Wales Big Data Internal data Activity monitoring External data Activity monitoring Blackboard Interactions GlamLife interactions Number of missed QMP Assignments Googl Interactions Logons from student area Tier 4 sign-ons Estates info (entry etc) Student Representation Library interactions Managing Information11 Return
© University of South Wales Activity Monitoring Technological interactions – BlackBoard, Googl , PC login, GlamLife Predictive equation – Bus./Comp./Music Tech/Drama/Graphics/Acc. Data visualisation 12Managing Information Return
© University of South Wales Managing Information13
© University of South Wales Managing Information14 Return
© University of South Wales 15 Return
© University of South Wales Target setting Comparison of retention targets with actual performance in 2011/12 and 2012/13, based on agreed retention target formula Generation of new targets for 2013/14 Managing Information16 Status Quo Scale of improvement Actual Return Rate 90%+ Target return rate same Actual Return Rate 83% to 89% Target return rate 90% Actual Return Rate 80% to 82% Target return rate – increase actual return rate by 5% Actual Return Rate 70% to 79% Target return rate – increase actual return rate by 10% Actual Return Rate below 70%Target return rate – increase actual return rate by 20% Return
© University of South Wales 17Managing Information Return
© University of South Wales Induction Activities – Funded new induction activities to strengthen student sense of “belonging” – Goal: improved student achievement, success and retention Impact 18Managing Information ”Definite bonding between them. And faster than in previous years... “ Geology “The students have bonded particularly well, they have been much more willing to approach staff and confident in how they interact with us” Chemistry “Decrease in student withdrawals attributed to the induction activity” Forensic Science Return
© University of South Wales Student Life Cycle Raising Aspirations Better Preparation First Steps in H.E. Moving Through Student Success Managing Information19 Student Life Cycle Are you aware of the main reasons why students withdraw from your programme? Are you aware of the steps they have to take in order to officially withdraw? What advice would you give to a student contemplating withdrawal? Reference: Return
© University of South Wales Learning Analytics: Techniques and Methods Statistics: hypothesis testing Business Intelligence: effective reporting Web analytics: technological interactions Artificial intelligence/data mining: data patterns Operational research: statistical methods Social Network Analysis: online/offline links Information visualisation: making sense of data Managing Information20 Return Ref: Cooper, Adam. A Brief History of Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, November 2012
© University of South Wales Bending without breaking: developing quality flexible learning experiences Professor Jo Smedley & Mary Hulford April 2014.
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