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Knowing you’re there: analysing technological engagement to enhance retention and success

This study explores the use of learning analytics on technological activity data to predict student success, retention, and achievement. It provides valuable insights into student behavior and informs student-centered initiatives.

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Knowing you’re there: analysing technological engagement to enhance retention and success

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

  2. 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.

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

  4. 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

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

  6. Learning Analytics Learning Analytics

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

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

  9. Big Data • Internal data • Activity monitoring • External data Managing Information

  10. Big Data • Internal data • Activity monitoring • External data Managing Information

  11. Return • Big Data • Internal data • Activity monitoring • External data Managing Information

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

  13. 2012-2013 Managing Information

  14. Return 2012-2013 Managing Information

  15. Return

  16. Return 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 Information

  17. Return Managing Information

  18. 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

  19. Student Life Cycle Return • 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

  20. 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

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