SHEILA Project - Workshop Slides Online Educa Berlin 2016
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Learning Analytics (LA) is currently a very active topic in education, but its implementation is beset with potential pitfalls for an organisation wishing to develop extensive use of it. Building upon international experience and local knowledge, the EC-funded SHEILA Project (Jan 2016-June 2018) is creating a policy framework for higher education institutions to enable them to design and enact an LA policy for themselves, using an innovative concept mapping approach (ROMA) combined with interviews of key stakeholders in several European countries. It is a partnership of the Universities of Edinburgh (coordinator), Tallinn University, Open University NL and Carlos III Madrid, with Brussels Education Services, Erasmus Student Network and European Quality Assurance Network (ENQA). In this workshop we discussed with participants our interim data from: - interviews from senior HEI leaders charged with the implementation of learning analytics to understand the current processes, barriers, and opportunities; - group concept mapping by international expert panel to identify critical concerns for learning analytics policy; - benchmark of the learning analytics sophistication in the European HE sector by administering a survey to members of the EUA.
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SHEILA Project - Workshop Slides Online Educa Berlin 2016
- 1. Supporting Higher Education to Incorporate Learning Analytics OEB ‘16 SHEILA Workshop 30th November 2016 Jeff Haywood, Dragan Gašević, Adolfo Ruiz Calleja, Maren Scheffel
- 2. Supporting Higher Education to Incorporate Learning Analytics Programme for OEB ‘16 SHEILA Workshop, 30th November 2016 1000 – 1005 Welcome & introduc3ons – Prof Jeff Haywood (University of Edinburgh) 1005 – 1110 Three presenta3ons & discussion “Overview – Learning Analy3cs & SHEILA Project” Prof Dragan Gasevic (university of Edinburgh) “SHEILA Project – interview findings” Prof Adolfo Ruiz Calleja (University of Tallinn) “SHEILA Project – concept mapping” Maren Scheffel (Open University of the Netherlands) 1110 – 1130 Coffee break 1130 – 1300 Groupwork on applying the Rapid Outcome Mapping Approach (ROMA) to assessing ins3tu3onal readiness for deploying LA & plenary discussion 1300 Close
- 31. Interviews in SHEILA project Adolfo Ruiz-Calleja, Kairit Tammets, Yi-Shan Tsai, Jeff Haywood, Maren Scheffel, Pedro Muñoz Merino and Dragan Gasevic 30th, November 2016
- 32. Index 32 ■ Methodological aspects ■ Topics of the interviews ■ Results ■ Conclusions
- 33. Index 33 ■ Methodological aspects ■ Topics of the interviews ■ Results ■ Conclusions
- 34. Methodology: ROMA 34 Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperative and the sociotechnical challenge: Policy for complex systems. Research & Practice in Assessment, 9(Winter 2014), 17-28.
- 35. Methodology 35 ■ Purpose: ■ Sampling: To collect opinions from decision makers in higher education institutions regarding the adoption of Learning Analytics. • Policy makers • Vice-rectors • Study departments • Academic offices • IT departments
- 36. Methodology 36 ■ 65 interviews ■ 52 institutions ■ 12 countries
- 37. Index 37 ■ Methodological aspects ■ Topics of the interviews ■ Results ■ Conclusions
- 38. Topics 38 ■ LA projects and their goals ■ LA strategy ■ Progress and achieved goals ■ Challenges ■ Ethics and privacy
- 39. Index 39 ■ Methodological aspects ■ Topics of the interviews ■ Results ■ Conclusions
- 40. Results: LA projects and goals 40 ■ Exploratory objectives ■ Very often LA projects are related to research ■ Diversity of interests • Sense of uncertainty • What can LA do for my institution? • What can our data tell us? • UK: correlation between behavioural patterns and learning outcomes • Spain: support teachers and improve their performance • Baltics: improve students retention
- 41. Results: LA strategy 41 ■ For some institutions (UK, Latvia, Finland) LA is part of their wider strategy ■ Others follow a bottom-up approach: some projects without connecting backbone structure ■ Managers become more interested on LA
- 42. Results: Achieved goals 42 ■ Too early phase in many institutions ■ LA is considered useful ■ Awareness of LA, better understanding of challenges and improvement of data culture ■ In-house software is typically used • Vendors offer Dashboards or systems to detect drop-out
- 43. Results: Challenges 43 ■ Cultural barriers ■ Little technical experience ■ Human resources • Data-driven and not question-driven • Traditional institutional culture • Lack of awareness • Lack of data culture • Many institutions do not feel ready for maintaining a data management infrastructure • Little experience on how to deal with online environments • Lack of time • Priorities
- 44. Results: Ethics and privacy 44 ■ Ethical use of data remains a big challenge ■ Privacy laws are very diverse ■ Ethical issues open technical problems • Although not all of them understand it as a problem (Estonia) • Clear need of ethical guidelines and solutions • e.g. Germany and Austria are much more restrictive than UK or Estonia • It is very difficult to avoid processing the data of one specific student if he does not allow it
- 45. Index 45 ■ Methodological aspects ■ Topics of the interviews ■ Results ■ Conclusions
- 46. Conclusions 46
- 47. Conclusions 47 ■ European universities are in an early stage of adoption of LA ■ Bottom-up approach from researchers, and awareness of policy makers ■ Several barriers have been detected, including ethics ■ There are important differences between countries
- 48. Interviews in SHEILA project Adolfo Ruiz-Calleja, Kairit Tammets, Yi-Shan Tsai, Jeff Haywood, Maren Scheffel, Pedro Muñoz Merino and Dragan Gasevic 30th, November 2016
- 50. Group Concept Mapping Phase 1: Brainstorming Phase 2: Sor3ng Phase 3: Ra3ng An essen3al feature of a higher educa3on ins3tu3on's learning analy3cs policy should be ...
- 51. Point Map 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
- 59. Go Zone – Privacy & Transparency 1 7 20 31 43 86 2 10 17 24 45 64 65 88 92 9 15 56 60 74 87 34 69 96 6.08 5.44 3.12 ease 3.83 6.03 6.59 importance r = 0.45
- 60. Go Zone – Privacy & Transparency 1 7 20 31 43 86 2 10 17 24 45 64 65 88 92 9 15 56 60 74 87 34 69 96 6.08 5.44 3.12 ease 3.83 6.03 6.59 importance r = 0.45 2. transparency, i.e. clearly informing students of how their data is collected, used and protected 88. a clear descrip3on of data protec3on measures taken 10. a clear descrip3on of data usage 17. being clear about the purpose for collec3on certain types of data 24. aligned with data protec3on regula3ons (ins3tu3onal, na3onal, interna3onal) 34. to assure that the collected data is used only for the purpose of improving learning and instruc3on 96. an agreement between learners, teachers and policy makers on regula3ng a proper use of data
- 61. Go Zone – Roles & Responsibili3es 5 38 62 11 19 22 33 39 48 70 91 25 28 37 40 55 61 66 27 47 49 6.08 4.72 3.12 ease 3.83 5.48 6.59 importance r = 0.26
- 62. Go Zone – Roles & Responsibili3es 5 38 62 11 19 22 33 39 48 70 91 25 28 37 40 55 61 66 27 47 49 6.08 4.72 3.12 ease 3.83 5.48 6.59 importance r = 0.26 55. being clear about the purpose of learning analy3cs 61. a clear ar3cula3on of roles and responsibili3es when it comes to the use of ins3tu3onal data 39. to promote broad adop3on of learning analy3cs by specifying suppor3ve regula3ons and case law