VII Jornadas eMadrid "Education in exponential times". "Supporting higher education in integrating learning analytics". Dragan Gasevic. U Edinburgh, UK. 05/07/2017.
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1. Supporting higher education in
integrating learning analytics
Dragan Gašević
@dgasevic
July 5, 2017
LASI Spain
Madrid, Spain
http://sheilaproject.eu/
3. Learning analytics is about learning
Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
4. Field of research and practice
Gašević, D., Kovanović, V., & Joksimović, S. (2017). Piecing the Learning Analytics Puzzle: A Consolidated Model of a Field of Research and Practice. Learning:
Research and Practice, 3(2), 63-78. doi:10.1080/23735082.2017.1286142
7. Current state – Oz and Europe
http://sheilaproject.eu/http://he-analytics.com
8. Adoption challenge
Leadership for strategic
implementation & monitoring
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the
Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
9. Adoption challenge
Equal engagement with
different stakeholders
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the
Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
10. Adoption challenge
Training to cultivate data literacy
among primary stakeholders
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the
Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
11. Adoption challenge
Policies for learning analytics practice
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the
Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
14. Data – Model – Transformation
Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and
Distance Education. http://bit.ly/icde_la_16
17. Inclusive adoption process
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.
20. SHEILA policy making framework
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.
21. Step 1 – Map political context
Internal and external drivers for
learning analytics adoption
22.
23. Step 1 – Map political context
One size fits all does not work in
learning analytics
24. Step 1 – Map political context
Opportunities to build learning
analytics on existing projects/practice
25. SHEILA policy making framework
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.
27. Step 2 – Identify key stakeholders
The project sponsor on
the senior management team
28. Step 2 – Identify key stakeholders
Other critical stakeholders to consider
Internal – professional and academic teams
External – service providers/vendors and collaborators
Champions of learning analytics (bottom up)
34. SHEILA policy making framework
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. Step 3 – Identify desired behavior changes
Identify areas where decisions will be
informed by learning analytics
36. Step 3 – Identify desired behavior changes
Define responsibilities and
implications for primary users
37. Step 3 – Identify desired behavior changes
Identification of possible
inadvertent consequences
38. SHEILA policy making framework
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.
39. Step 4 – Develop engagement strategy
Alignment of learning analytics with
the wider institutional strategies
40. Step 4 – Develop engagement strategy
Secure funding, establish a working
group, and raise awareness
41. Step 4 – Develop engagement strategy
Select data that will be fed back to users
42. Step 4 – Develop engagement strategy
How interventions will be triggered
and who is responsible?
43. SHEILA policy making framework
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.
44. Step 5 – Analyze internal capacity
Data storage, disposal, and
security evaluation
46. Step 5 – Analyze internal capacity
Evaluate institutional culture
Trust in data
Decision-making based on data
Openness to changes and innovation
47. SHEILA policy making framework
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.
48. Step 6 – Establish monitoring & learning frameworks
Establish qualitative and quantitative
indicators of success
Stage the process to recognize institutional development
49. Step 6 – Establish monitoring & learning frameworks
Seek feedback from primary users
through various channels
51. Systemic Adoption Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office
for Learning and Teaching.
52. Solution-focused Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office
for Learning and Teaching.
53. Process-focused Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office
for Learning and Teaching.
56. Learning analytics principles
Data incompleteness, bias perpetuation,
avoidance of deficit models, facilitation of training,
not used for performance assessment
The University of Edinburgh (2017). Learning Analytics Policy, http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy
57. Learning analytics purposes
Quality, equity, personalized feedback, coping with scale,
student experience, skills, and efficiency
The University of Edinburgh (2017). Learning Analytics Policy, http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy
62. Supporting higher education in
integrating learning analytics
Dragan Gašević
@dgasevic
June 20, 2017
Sydney, NSW, Australia
Notas do Editor
Methodology
Institutions feel pressure to adopt learning analytics without having identified the needs first.
Wrongly assume that learning analytics can provide all answers without having identified a question first – data driven.
Learning analytics does not generate new knowledge.
Infrastructure
Existing solutions in the market focus on addressing retention problems.
There is no one-size-fits-all model, even within one institution (different disciplines and learning modes).
Management
Learning analytics needs to compete with other institutional priorities.
Management
Define ownership and responsibilities among professional groups within the university
Privacy
Sharing data with third parties requires a careful check of security issues and breaches of privacy.
Management
Students may be prone to choose subjects where they are likely to perform well.
Users may game a system.
People mistrust the result of an analysis if the process is not transparent or if the analytical model is too complicated to understand.
Those who need support may not necessarily seek information from learning analytics.
.
Management
Learning analytics may induce fear and discomfort about surveillance.
Universities overload students with too many e-mails.
Strict data protection laws could hamper the adoption of learning analytics
Infrastructure
Some useful data remains inaccessible.
Data is held in silos.
Setting up a learning analytics environment is costly.
Capabilities
The maturity of data literacy varies among stakeholders.
The lack of critical self-reflection skills reduces the chance to benefit from learning analytics.
Gaps exist in the understanding/ interpretation of data protection regulations between legal officers and researchers or practitioners.
Digital capabilities affect the desire to opt into a learning analytics service.
Culture
Institution-wide buy-in is hard to reach.
Instructors are more interested in establishing a research profile than enhancing teaching and learning.
Senior managers are more interested in financial benefits to the institution.
There is unequal engagement/ interest in learning analytics among primary users (e.g., gender, age, disciplines).
There is no common understanding of learning analytics among stakeholders at different levels.
Concerns of data protection impede buy-in.
Management
2018 GDPR requires changes in existing practice and system (e.g., coping with individual opt-outs).
Central steering groups and individual project groups do not coordinate.
The Difficulty of engaging students with institutional policies in an informed way.
Culture
Low participation of primary stakeholders in top-down consultations (e.g., survey and meetings).
Management
Manage expectations (e.g., deliverables and impact).
Methodology
It could be hard to isolate learning analytics from parallel projects that support the same goals (e.g., enhance learning and teaching).
Fail to recognise and address limitations of data and analytics models (e.g., uncapturable factors of learning, ineffective metrics, existing bias, inaccuracy of predictions).
Overly depend on data that is conveniently available to justify a learning phenomenon.
Fail to contextualise data.
Wrongly assume causal relationship between certain learning outcomes and interventions.
Interventions introduced to one course may have negative impact on student engagement in another course.
These findings informed the development of a model.
As per slide. In the following both the solutions focused and process focused models are displayed.
Solutions focused. – The goal is to quickly deploy analytics hence the initial emphasis in on implementation. For example deploying BlackBoard analytics.
At this stage there is limited attention on dimensions such as innovation (interested staff) and developing staff capacity.
In contrast the process focused model has all dimensions however, these are not fully developed. The diagrqam here is generous as the dominant focus has been on innovation through interested staff. There has been very little uptake to for broader implementation. This can be due to the perceived lack of an articulated institutional problem or pressure for LA to address.
The difficulty remains in how to transition innovations to mainstream