Knowing you’re there: analysing technological engagement to enhance retention and success Professor Jo Smedley & Professor Clive Mulholland March 2014

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.

Session Aims Using Big Data About Analytics Case study: Learner Journey Analytics

Value of Big Data Analytics 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 Predictive analytics Leverage past data to understand why something happened or to predict what will happen in the future across various scenarios Business intelligence Descriptive analytics Mine past data to report, visualize and understand what has already happened – after the fact or in real time Computational complexity 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

Case Study: Learner Journey Analytics Belonging and attachment Student life cycle Learning Analytics

Learning Analytics Learning Analytics Target Setting Induction Traffic Lights Data Mining Activity Monitoring Learning Analytics

Conclusions/Further Work Enhanced data transparency Wider engagement Links to:- Admissions data Achievement Credit scores

Webpage: http://celt.southwales.ac.uk/does/sa/ Questions/Followup Webpage: http://celt.southwales.ac.uk/does/sa/ Email: jo.smedley@southwales.ac.uk clive.mulholland@southwales.ac.uk

Student experience surveys x n Internal data Module surveys x n Student experience surveys x n Big Data Internal data Activity monitoring External data Managing Information

International Student barometer External data NSS PRES PTES HESA DLHE International Student barometer Big Data Internal data Activity monitoring External data Managing Information

Big Data Internal data Activity monitoring External data Return Blackboard Interactions GlamLife interactions Number of missed QMP Assignments Googlemail Interactions Logons from student area Tier 4 sign-ons (entry etc) Estates info Student Representation Library interactions Return Big Data Internal data Activity monitoring External data Managing Information

Activity Monitoring Technological interactions Predictive equation Return Activity Monitoring Technological interactions BlackBoard, Googlemail, PC login, GlamLife Predictive equation Bus./Comp./Music Tech/Drama/Graphics/Acc. Data visualisation Managing Information

2012-2013 Managing Information

Return 2012-2013 Managing Information

Return

Return Target setting 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% 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 Information

Return Subject groupings reflect NSS Comparison of satisfaction levels and retention/performance levels Most subjects fall in one faculty, others are cross faculty Vertical scales and the baselines differ depending on the indicator being reported. Managing Information

Induction Activities Impact Return Induction Activities Funded new induction activities to strengthen student sense of “belonging” Goal: improved student achievement, success and retention Impact “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 ”Definite bonding between them. And faster than in previous years... “ Geology Managing Information

Student Life Cycle Student Life Cycle Return Raising Aspirations Better Preparation First Steps in H.E. Moving Through Student Success 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? Student Life Cycle Reference: http://www.ulster.ac.uk/star/resources/Anagnostopoulou_Parmar.pdf Managing Information

Learning Analytics: Techniques and Methods Return 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 Ref: Cooper, Adam. A Brief History of Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, November 2012 Managing Information