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Exploring learning analytics
The document outlines the implementation of learning analytics at the University of South Wales (USW) from 2017 to 2018, detailing use-cases such as data exploration for personal academic coaches and predictive analytics for student retention. It discusses the project's governance, ethical considerations, challenges faced, and successes achieved in improving student support and curriculum design. The document highlights the importance of data intelligence in driving institutional improvements and emphasizes ongoing developments for future enhancements.
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Exploring learning analytics
- 1. Learning Analytics Implementationat USW - 2017/18 activity Playing the hand you’re dealt Learning Analytics Implementation at USW 2017/18 Martin Lynch Learning Systems Manager, CELT
- 2. Learning Analytics Implementationat USW • What is happening regarding Learning Analytics implementation at USW; • The cards in our deck • Timeline of events • What are we doing in the project – 4 use-cases • Ethics and GDPR • Challenges and successes
- 3. Learning Analytics Implementationat USW The cards in the deck • JISC • Tribal • Blackboard • GDPR • PVC • Strategic alignment • Professional Services – Library, Registry, IT Services Development • Governance • Learning Technologist • Developer • Project manager • PACs • Students STUDENTS STUDENTS External InternalStakeholders
- 4. Learning Analytics Implementationat USW June 2015 PVC Student Retention and Success Dr Ben Calvert promotes internal conversation regarding Student Retention and Success – USW now looking for an analytics solution USW put forward for the last place on the JISC discovery phase - consultation stage begins with Bb Bb consulting run Discovery phase at USW in order to produce a “Readiness Assessment” Memorandum of understanding signed between USW and JISC Readiness assessment report published University agrees to set up Learning Analytics Project – 2 year duration 2 posts identified, with 0.6FTE project management Project board initiated at USW Recruitment process setting up for learning technologist and developer Begin extraction of historical data from Blackboard Assessment data coming from Bb Progression data from Student Record system now in UDD Appointment of learning technologist Student enrolment form amended in light of LA project Module marks for current session held in Student Record System showing in Data Explorer Decision taken that Data explorer does not meet minimum requirements for use as a pilot tool – data and usability issues Senior faculty staff and data users meet with Tribal to consider predictive modelling requirements – select 3 key models Staff questionnaire sent out to baseline expectation on functionality of Data explorer 50 staff agree to pilot use of Data Explorer from Jan Appointment of IT Developer RUHere attendance data surfacing in DX Tribal develop 3 new models for USW – data still has gaps USW invited to join the ‘group of five’ Workshop conducted for student-facing dashboards for 2019 All marks and grades from Bb and Quercus appearing in DX Library ‘recipe’ simplified by JISC – test data extract can begin Historical grade data from Student Record System and Blackboard (3 years) now in warehouse – Tribal can review their models Project timeline May 2015 - May 2018 PROJECT SET-UP = 18 MONTHS PROCESS AND PRODUCT DEVELOPMENT = CONTINUOUS DATA = 14 MONTHS
- 5. Learning Analytics Implementationat USW What we are doing in the project? Use-case#1 Data Explorer for PAC’s
- 6. Learning Analytics Implementationat USW • Personal Academic Coaches (PAC’s) 300+ at USW • Every student allocated a PAC on enrolment • Senior teaching staff from course team – understand delivery • Follows student through their studies – 3X20 min meetings every year, focus on academic progress • Need to document the event and have data to inform conversation • Data Explorer replaces accessing multiple data sources but more than the sum of the parts
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- 17. Learning Analytics Implementationat USW Use-case#2 Data Explorer for Curriculum Design
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- 24. Learning Analytics Implementationat USW Use-case#3 – the value of “Predictive Analytics” to the institution
- 25. Learning Analytics Implementationat USW • Predictions are made with very large data set (3 years) • Machine learning algorithm so refining factors (regression analysis) in each context (module or course) to establish significance • Applying pattern recognition from training set to current students to find close matches • Predictive models are questions you want to ask of your data • Tribal our current data science partner and models are almost there…
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- 27. Learning Analytics Implementationat USW • First models are retention-based and deal with risk • Intention is to use intelligence to better target support But.. • Are these models any good? • What services to build - how should we use this data? • What value is this to the institution?
- 28. Learning Analytics Implementationat USW Use-case#4 – student-facing dashboards
- 29. Learning Analytics Implementationat USW • Unexplored in the sector so breaking new ground in the UK • Small-scale studies – ref. Dr Liz Bennett University of Huddersfield, Ed de Quincey Keele University • Understanding this as a type of feedback literacy – ref. Paul Sutton (2012) notions of feedback literacy Principles are; • Access should be mediated in the first instance • Motivation is personal so dashboards should be customisable to fit user needs • Dispositional dimension is critical
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- 33. Learning Analytics Implementationat USW Ethics and GDPR • USW implementation of Learning Analytics GDPR compliant • Enrolment process amended • Consent on protected characteristics • Clearly described data processing activity • Student-facing guide • Student involvement in project • Guided by ICO guidance and JISC • Using ‘legitimate business interest’ to process data
- 34. Learning Analytics Implementationat USW Successes • Good resourcing has driven success • Good appointments and effective project management • Effective governance – interested (but not too involved) executive sponsor and a good board that turn up and ask questions • Staff see the value already but important to wait until its ready before showing it Challenges • Supplier pilot processes – delayed deployment for a year • Some developments and data wrangling too slow (predictor, library recipe) • Developers are hard to find – took us 10 months • Unclear internal business processes – who looks after wobbling students? • Inconsistent use of data for decision making Project successes and challenges
- 35. Learning Analytics Implementationat USW • DX – full roll-out and use for Personal Academic Coaches (PAC’s) • Use of DX by other roles – faculty management, course and module teams to assess engagement and to contribute to course design, side-cases (eg to evidence non-payment of fees) • Study Goal – pilot use as a simple attendance monitoring tool • Use of predictive data to drive early-warning alerts (Tribal) • Assess value to institution of predictive business intelligence (Tribal) • Compare and contrast Tribal and JISC predictive offers • Student-facing dashboards – prototype mash-ups – refactor to something more useful • Attract research and development funding to incubate this • Full project evaluation and analysis of impact>options appraisal >recommendations Project Vision and plans for 2018/19
- 36. Learning Analytics Implementationat USW Questions? martin.lynch@southwales.ac.uk
Editor's Notes
- #3 At USW we seem to be playing a strong game
- #4 Jisc – main supplier for the project, providing technical infrastructure (Learning Data Hub), data schema, visualisation tools, network opportunities Tribal – data science provider – offering data science analysis and predictive models Blackboard – our VLE provider – provides a plug-in which is harvesting student activity data and passing in to JISC Learning data Hub currently the main proxy for engagement – no assessment data! Library – cooperation of Learning Services supplies us with data pertaining to library resource usage – books and e-resources Registry – providing profile data for student users being fed into the LDH, providing assessment data for formal assessments IT Services – providing development services – have managed to query Bb databases to extract ALL assessments – regardless of tool, both formative and summative including due dates and submission dates found in VLE Governance Students – the ultimate data owners and providers of the data need participation Learning Technologist – allocated to the project for 2 years – data wrangling and staff development Project Manager Pro-Vice Chancellor Teaching, Learning and Student Experience – executive sponsor and champion Strategic alignment – Learning Analytics finds itself woven into the university’s strategic plan for student success “The Student Experience Plan” GDPR – this has shaped the public declaration of data processing, the consent framework and the local interpretation of the legal definitions of processing and intervention
- #28 First models are retention-based and deal with risk – obvious human and monetary cost to retention but with TEF in our mirrors, should be looking at data science to evidence added value and learner stretch Intention is to use intelligence to better target support – “Barry’s Bell” But.. Are these models any good? Need models to be ‘finished’ and need time to assess the results What services to build - how should we use this data? – is the institution ready to consume this in a way that would improve business? What value is this to the institution? – these are expensive services so what is the business case
- #35 https://datax.jisc.ac.uk/#/login
- #36 https://datax.jisc.ac.uk/#/login