Data sharing and analytics in research and learning

1. Data sharing and analytics in research and learning Chair: Professor Martin Hall 14/07/2016 1

2. Introduction Professor Martin Hall 14/07/2016

3. Learning analytics: progress and solutions Niall Sclater and MichaelWebb, Jisc 14/07/2016

4. Learning analytics Progess & Solutions Niall Sclater & MichaelWebb, Jisc @sclater @michaeldwebb 06/07/2016 Learning analytics: progress & solutions 4

5. “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” SoLAR – Society for Learning Analytics Research 06/07/2016 Learning analytics: progress & solutions 5

6. » Problems identified in 2nd week of semester » Interventions include: › Posting signal on student’s home page › Emailing or texting them › Arranging a meeting » Courses that deploy signals see consistently better grades » Students on Signals sought help earlier and more frequently Early alert and student success 06/07/2016 Learning analytics: progress & solutions 6

7. Recommender systems 06/07/2016 Learning analytics: progress & solutions 7 Desire2Learn Degree Compass

8. Adaptive learning 06/07/2016 Learning analytics: progress & solutions 8 The Brightspace LeaP adaptive learning engine

9. Curriculum design » A key piece of learning content is not being accessed by most students » Some students are not participating well in collaborative work » A particular minority group is underperforming in an aspect of the curriculum » Students across several discussion groups are making only minimal contributions to their forums 06/07/2016 Learning analytics: progress & solutions 9

10. » Total hits is strongest predictor of success » Assessment activity hits is second » Metrics relating to current effort (espVLE usage) are much better predictors of success than historical or demographic data. (John Whitmer) California State University - Chico 06/07/2016 Learning analytics: progress & solutions 10

11. “a student with average intelligence who works hard is just as likely to get a good grade as a student that has above-average intelligence but does not exert any effort” (Pistilli & Arnold, 2010) 06/07/2016 Learning analytics: progress & solutions 11

12. » Predictive early alert model transferred to different institutions » Around 75% of at-risk students were identified » Most significant predictors were: › Marks on course so far › GPA › Current academic standing (Jayaprakesh et al.) Marist College, NewYork 06/07/2016 Learning analytics: progress & solutions 12

13. Retention in England » 178,100 students aged 16-18 failed to finish post-secondary school qualifications they started in the 2012/13 academic year › costing £814 million a year - 12 per cent of all government spending on post-16 education and skills (Centre for Economic and Social Inclusion) » 8% of undergraduates drop out in their first year of study › This costs universities around £33,000 per student » students with 340 UCAS points or above were considerably less likely (4%) than those with fewer UCAS points (9%) to leave their courses without their award 06/07/2016 Learning analytics: progress & solutions 13

14. Attainment in England » 70% of students reporting a parent with HE qualifications achieved an upper degree, as against 64% of students reporting no parent with HE qualifications » Overall, 70% ofWhite students and 52% of BME students achieved an upper degree 06/07/2016 Learning analytics: progress & solutions 14

15. Jisc Effective Learning Analytics project 06/07/2016 Learning analytics: progress & solutions 15 » Expressions of interested: 85+ » Engaged in activity: 35 » Discovery to Sept 16: agreed (28), completed (18), reported (11) » Learning Analytics Pre-Implementation: (12) » Learning Analytics Implementation: (7)

16. Effective learning analytics programme 16 ECAR Analytics Maturity Index for Higher Education UK Learning Analytics Network analytics@jiscmail.ac.uk 06/07/2016 Learning analytics: progress & solutions

17. 06/07/2016 Learning analytics: progress & solutions 17

18. Group Name Question Main type Importance Responsibility 2 Consent Adverse impact of opting out on individual If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress? Ethical 1 Analytics Committee 7 Action Conflict with study goals What should a student do if the suggestions are in conflict with their study goals? Ethical 3 Student 8 Adverse impact Oversimplification How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances? Ethical 1 Educational researcher 86 issues in 9 groups Available from Effective learning analytics blog: analytics.jiscinvolve.org 06/07/2016 Learning analytics: progress & solutions 18

19. Group Name Question Main type Importance Responsibility 2 Consent Adverse impact of opting out on individual If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress? Ethical 1 Analytics Committee 7 Action Conflict with study goals What should a student do if the suggestions are in conflict with their study goals? Ethical 3 Student 8 Adverse impact Oversimplification How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances? Ethical 1 Educational researcher 86 issues jisc.ac.uk/guides/code-of-practice-for-learning-analytics 06/07/2016 Learning analytics: progress & solutions 19

20. Times Higher, 25 Feb. 2016 06/07/2016 Learning analytics: progress & solutions 20

21. 21 ECAR Analytics Maturity Index for Higher Education Discovery Phase 06/07/2016 Learning analytics: progress & solutions

22. Implementation process 06/07/2016 Learning analytics: progress & solutions 22 5. Implementation Support 4. Signed-up for Service 3. Institutional Readiness 2. Self- assessment 1. Workshop »2016 - 17

23. Discovery readiness questionnaire 06/07/2016 Learning analytics: progress & solutions 23 • Culture andVision • Strategy and Investment • Structure and governance • Technology and data • Skills

24. Guidelines / checklist 06/07/2016 Learning analytics: progress & solutions 24 Culture and Organisation Setup  Decide on institutional aims for learning analytics  Senior management approval and you have a nominated project lead  Undertake the readiness assessment  Decision on learning analytics products to pilot  Legal and ethical considerations in hand  Address readiness recommendations  Data processing agreement signed  Select student groups for the pilot and engage staff/students Technical setup  Learning records warehouse setup  Extract student data to UDD and upload to LRW  Historical data extracted from theVLE and SRS and uploaded to the LRW  VLE plugin installed and live data being uploaded  View in data explorer to check valid  Contact Jisc to start implementation

25. 25 ECAR Analytics Maturity Index for Higher Education Architecture 06/07/2016 Learning analytics: progress & solutions

26. Project partners 06/07/2016 Learning analytics: progress & solutions 26

27. Learning Analytics architecture 06/07/2016 Learning analytics: progress & solutions 27

28. Unified data definitions 06/07/2016 Learning analytics: progress & solutions 28

29. Service: Dashboards Visual tools to allow lecturers, module leaders, senior staff and support staff to view: » student engagement » cohort comparisons » etc… Based on either commercial tools from Tribal (Student Insight) or open source toolsfromUnicon/Marist(OpenDashBoard) 06/07/2016 Learning analytics: progress & solutions 29

30. Service: Alert and intervention system Tools to allow management of interactions with students once risk has been identified: » case management » intervention management » data fed back into model » etc… Based on open source tools from Unicon/Marist (Student Success Plan) 06/07/2016 Learning analytics: progress & solutions 30

31. Service: Student App » Comparative » Social » Gameified » Private by default » Usable standalone » Uncluttered 06/07/2016 Learning analytics: progress & solutions 31

39. jisc.ac.uk Michael Webb michael.webb@jisc.ac.uk @michaeldwebb analytics.jiscinvolve.org 06/07/2016 Learning analytics: progress & solutions 39 Niall Sclater niall.sclater@jisc.ac.uk @sclater

40. Reading analytics Clifford Lynch,CNI 14/07/2016

41. »AWAITING CONTENT 14/07/2016

42. Sharing data safely and its re-use for analytics David Fergusson,The Francis Crick Institute 14/07/2016

43. The Francis Crick Institute Sharing Data Safely and re-use for analytics David Fergusson

44. Introduction 44

45. Challenges for ”big data” science in the UK Distributed Data Sets Distributed computing resources Separate authentication/authorization mechanisms Researchers want to combine and synthesise data How do we do this? 45

46. Example Dr David Fergusson, Head of Scientific Computing, Francis Crick Institute Challenges of providing shared platforms for staff from existing institutes – CRUK London Research Institute – National Institute for Medical Research Compute and data requirements for 1,250 scientists working in biomed – In a central London building Direction of travel towards more and wider collaboration, requirement for controlled sharing of sensitive data 46 Photo credit: Francis Crick Institute

47. Example 47 Dr Jeremy Yates, STFC DiRAC & SKA: › The National e-Infrastructure for research & innovation – A 60,000 foot view – Democratisation & Aggiornamento › Moving to a more cloud-centric view of scientific computing › Scientific computing that is not just “HPC” › Changing the culture around Research Software Engineering › Making industrial access to facilities the norm › Inter-disciplinary science – blockers and enablers Image credit: Courtesy of EPSRC

48. Addressing the problem SafeShare – shared secure authorisation/authentication Shared Data Centre(s) – avoid costly/insecure moving of data eMedlab – collaborative science/shared operations model 48

49. UK e-Infrastructure A new bottom up approach 49

50. People’s National eInfrastructure Uganda Medical Bioinformati cs Business and local government ESRC £64M MRC £120M SECURE

51. What has worked? Consolidation through collaboration Swansea: One system supporting Farr Wales, ADRC Wales, MRC CLIMB, Dementia Platform UK Scotland: EPCC supporting Farr Scotland and ADRC Scotland, leveraging expertise from Archer, UK-RDF Leeds: ARC supporting Farr HeRC, Leeds Med Bio, Consumer Data RC Slough DC: eMedLab, Imperial Med Bio, KCL bio cluster Jisc network: Safe Share

52. JISC SafeShare 52

53. John Chapman, Deputy head, information security, Jisc The safe share project

54. About Jisc » Assent Assent: Single, unifying technology that enables you to effectively manage and control access to a wide range of web and non- web services and applications. These include cloud infrastructures, High Performance Computing, Grid Computing and commonly deployed services such as email, file store, remote access and instant messaging 54

55. About Jisc » Safe Share Safe Share: Providing and building services on encrypted VPN infrastructure between organisations Enhanced confidentiality and integrity requirements per ISO27001 Requirement to move electronic health data securely and support research collaboration Working with biomedical researchers at Farr Institute, MRC Medical Bioinformatics initiative, ESRC Administrative Data Centres 55

56. The safe share project The safe share project 56 • What: a pilot project enabling the secure exchange of data collected by Government and the NHS using an encrypted overlay over the Janet network to facilitate appropriate analysis between project sites • • AND reusing existing services to increase authentication for researchers • Why: easier, secure access to research data to further knowledge of diseases and ill health to improve medical treatments in the long-term • When: running from November 2014 – March 2017

57. The safe share project The safe share project 57 Background • Substantial investment in medical and administrative data research to generate benefits to society from the appropriate analysis of data collected by Government and the NHS • E.g. to further knowledge e.g. of disease and ill health to improve medical treatments Challenges • Health data, and other routinely collected data on people’s lives, are very personal and sensitive • Significant numbers of ethical, consensual and practical hurdles to making appropriate use of the sensitive data for research

58. The safe share project The safe share project 58 Drivers • Requirement for connectivity to move and access electronic health data securely • Challenge to give public confidence that data is appropriately protected • Provide economies of scale in secure connectivity The safe share project • Jisc management and funding of £960k to pilot potential solutions with the aim of developing a service in 2016/17

59. Partners The safe share project 59 University of Bristol Cardiff University University of Leeds Swansea University University of Edinburgh UCL Francis Crick Institute University of Oxford University of Southampton University of Manchester St Andrews University The Farr Institute The MRC Medical Bioinformatics initiative The Administrative Data Research Network University of Bristol Cardiff University University of Edinburgh Francis Crick Institute University of Leeds UCL University of Manchester University of Oxford University of St Andrews University of Southampton Swansea University

60. The safe share project The safe share project 60 Authentication, Authorisation and Accounting Infrastructure (AAAI) Use Cases: • HeRC, N8 HPC – access between facilities using home institution credentials • eMedLab – partners will be able to use a common AAAI to access this new system (for analysis of for instance human genome data, medical images, clinical, psychological and social data) • Swansea University Health Informatics Group – investigating Moonshot as an authentication mechanism to allow use of home institution credentials • University of Oxford: to enable researchers to use home institution credentials for authentication to request access to datasets for studies e.g.

61. The safe share project The safe share project 61 Example “service slice”: Farr Institution LAN Safe share core Janet, internet or other network Farr trusted environments safe share router at edge

62. The safe share project The safe share project 62 Example “service slice”: Farr Institution LAN Farr trusted environments Janet, internet or other network safe share router at edge Safe share core

63. UK Academic Shared Data Centre 63

64. Shared data centre £900K investment from HEFCE Anchor tenants: – Francis Crick Institute – King’s College London – London School of Economics – Queen Mary University of London – Wellcome Trust Sanger Institute – University College London 64

65. Potential cost-saving/resource benefits Jisc Shared Datacentre is already a cost saving eMedLab award, and need for quick spend, gave impetus to UCL, KCL, QMUL, Sanger, LSE and Crick to identify off-site datacentre hosting (Slough) – Anchor tenants get price reduction based on volume of space used Procurement led by Jisc Datacentre connected to Janet network (Jisc investment) Improved PUE; Slough 1.25 cf ~2 for HEI datacentre (UCL save ~£2M p.a.)

66. Datacentre Connection Topology N3/PSNH/PSN

67. eMedLab Collaborative science Shared Operation 67

68. Objectives - Flexibility • To help generate new insights and clinical outcomes by combining data from diverse sources and disciplines • Bring computing workloads to the data, minimising the need for costly data movements • To allow customised use of resources • To enable innovative ways of working collaboratively • To allow a distributed support model 68

69. Institutional Collaboration

70. Supportteam eMedLab academy • Training via CDFs and courses • Promote collaborations via “Labs” eMedLab infrastructure • Shared computer cluster • Integrate exchange heterogeneous data • Methods and insights across diseases

71. eMedLabis a hub 6+1 partners 3 data types electronic health records genomic images 3 expertises clinician scientists analytics basic science 3 disease areas rare cancer cardio >6M patients

72. What is eMedLab?

73. Distributed/Federated support (What has worked/savings ..) eMedLab Ops team (shared team) Knowledge sharing/transfer (inc. developing UK industrial capacity – Support Support Support Support Support Support

74. Many projects, same challenges Information governance Secure data transfer User management AAAI Working with Janet to explore how to support most/all projects

75. Cultural Barriers Challenges Finance – government funding with spend window of 1 year only +Mitigated by use of efficient procurement teams and framework agreements +Working closely with vendors to ensure tight time targets met - Drain on (unfunded) project management and finance team resources Regulatory challenge +Mitigated by clear policies, governance, supported by training +Changing EU data protection legislation - Risk of bad PR and/or data leaks People +Everyone is open, collaborative, generous with time and knowledge

76. eMedLab production service  Projects • UCL & WTSI - Enabling Collaborative Medical Genomics Analysis Using Arvados – Javier Herrero • Crick KCL UCL - A scalable and flexible collaborative eMedLab cancer genomics cluster to share large-scale datasets and computational resources – Peter van Loo • UCL QMUL Farr - Creating and exploiting research datamart using i2b2 and novel data-driven methods - Spiros Denaxas • LSHTM & QMUL - An evaluation of a genomic analysis tools VM on the EMedLab, applied to infectious disease projects at the LSHTM using data from EBI and Sanger & Genetic Analysis of UK Biobank Data - Taane Clark & Helen Warren • UCL & ICH - The HIGH-5 Programme - High definition, in-depth phenotyping at GOSH, plus related projects - Phil Beales & Hywel Williams & Chela James

77. eMedLabenables projects eMedLab brings data and expertise together across diseases (potential) • Mechanisms of cancer diversity and genome instability • Better understanding of biomarkers • DARWIN Clinical Trial to target clonal drivers Cancer evolution and heterogeneity (Swanton & Van Loo) • Cancers evolve heterogeneously • Diverse driver mutations and instability mechanisms • TracerX: Track lung cancer evolution • Data: genomes, MRI, molecular pathology • Who: clinicians, statisticians, evolutionary biologists

78. People Alan Real, Bob Day, Bruno Silva, Clare Gryce, David Fergusson, Emily Jefferson, Jacky Pallas, Jeremy Sharp, John Ainsworth, John Chapman, Jonathan Monk, Mark Parsons, Ric Passey, Richard Christie, Rhys Smith, Simon Thompson, Simon Thompson, Spiros Denaxas, Stephen Newhouse, Steve Pavis, Tanvi Desai, Tim Cutts and others …........

79. Thank you for reading the information within this document; you have now reached the end. 79

80. Data sharing and analytics in research and learning Chair: Phil Richards, Jisc 14/07/2016 80

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