This document summarizes several projects and resources related to learning analytics. It discusses the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) project at the University of Edinburgh which aims to develop critical and participatory approaches to educational data analysis. It also mentions the Learning Analytics Report Card (LARC) project which explores critical awareness with report cards. Additionally, it provides an overview of the Supporting Higher Education to Integrate Learning Analytics (SHEILA) project which developed a learning analytics policy framework through interviews and surveys. The document also shares findings from the SHEILA project about the adoption of learning analytics in higher education and key challenges identified. It outlines the principles and purposes of the University of Edinburgh's

1. LERU: Learning Analytics
Anne-Marie Scott
Learning, Teaching and Web Services
17 March 2017

2. Learning Analytics Map of Activities, Research and
Roll-out (LAMARR)
http://www.ed.ac.uk/information-services/learning-technology/learning-analytics

7. • How can University teaching teams develop critical and
participatory approaches to educational data analysis?
• How can we develop ways of involving students as
research partners and active participants in their own data
collection and analysis, as well as foster critical
understanding of the use of computational analysis in
education?
Learning Analytics Report Card (LARC)
http://larc-project.com
Knox, J. (2017). Data Power in Education: exploring critical awareness with the
‘Learning Analytics Report Card’ (LARC). Special Issue: Data Power in Material
Contexts, Journal of Television and Media.
http://journals.sagepub.com/doi/full/10.1177/1527476417690029
Isard, A. and Knox, J. 2016. Automatic Generation of Student Report Cards. 9th
International Natural Language Generation conference. Edinburgh, Sept 5-8
http://www.macs.hw.ac.uk/InteractionLab/INLG2016/proceedings/pdf/INLG33.pdf

9. Supporting Higher Education to Integrate Learning Analytics
• Aim: Developing a LA policy development framework
• Activities: Interviews and survey with decision makers, group concept
mapping with experts, surveys and focus groups with students and
teaching staff, and 4 institutional policies.
• Project partners: The University of Edinburgh, Open University of the
Netherlands, Universidad Carlos III de Madrid, Tallinn University,
Brussels Educational Services, European Association for Quality
Assurance in Higher Education, Erasmus Student Network
http://sheilaproject.eu/

10. 64 institutional interviews with 51 HEIs
NO PLANS
UNDER PREPARATIONS
IMPLEMENTED
9 7 5
12
18
THE ADOPTION OF LEARNING
ANALYTICS
Institution-wide Partial/ Pilots Data exploration/cleaning N/A

11. Group Concept Mapping
importance ease
privacy & transparency
privacy & transparency
risks & challenges
risks & challenges
roles & responsibilities (of all stakeholders)
roles & responsibilities (of all stakeholders)
objectives of LA (learner and teacher support)
objectives of LA (learner and teacher support)
data management
data management
research & data analysis
research & data analysis
3.79 3.79
6.03 6.03
r = 0.66

12. Policy Challenges
Tsai, Y-S., Gašević, D. (2016). Adoption of Learning Analy+cs in Higher Education: State, Challenges, and Policies (Executive summary). SHEILA Project Report
http://sheilaproject.eu/wp-content/uploads/2016/06/Adoption-of-Learning-Analytics-in-Higher-Education_Executive-Summary.pdf

13. Edinburgh: Principles
1. As an institution we understand that data never provides the whole picture about students’ capacities or
likelihood of success, and it will therefore never be used to inform action at an individual level without
human intervention;
2. Where we use learning analytics to target support at students, we will do so in order to help all students
to achieve their learning goals rather than taking a deficit model targeted at supporting students at risk of
failure;
3. We will be transparent about how we collect and use data, where consent applies, and where
responsibilities for the ethical use of data lie;
4. We recognise that data and algorithms can contain and perpetuate bias, and will actively work to build
awareness of this and address it where it occurs;
5. Good governance will be core to our approach, to ensure learning analytics projects and implementations
are ethically conducted and align with organisational strategy, policy and values; and
6. Data generated from learning analytics will not be used to monitor staff performance, as learning
analytics is a field that seeks to understand and optimise learning and it does not aim to measure staff
performance.
http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy

14. Edinburgh: Purposes
• Quality – Learning analytics can be used as a form of feedback on the efficacy of pedagogical design. Academic teams can use
analytics about student activity (individual or cohort) as part of course review and re-design processes as well as potentially using
analytics as a form of in-course monitoring and feedback.
• Equity – Learning analytics approaches can allow us to see more nuanced views of our highly diverse student population,
challenge assumptions that we may be making, and allow supportive resource to be directed where it is most needed.
• Personalised feedback – Learning analytics can be used to tailor the messages and support that we offer to our students, providing
more personalised feedback to support student reflection and academic planning.
• Coping with scale – With the challenge of growing cohorts of students, learning analytics can help to strengthen the academic
relationship by doing some of the heavy lifting of identifying individuals or groups of individuals that might benefit from particular
interventions or information from staff.
• Student Experience – In addition to supporting a more personalised experience, learning analytics can improve progression and
retention, ensure that our academic offerings align with the needs and goals of students, and support satisfaction and wellbeing.
Analytics can also be used to promote critical reflection skills and enable our students to take responsibility for their own learning.
• Skills – Interactions with analytics as part of the University learning experience can help our students build 'digital savviness' and
prompt more critical reflection on how data about them is being used more generally, what consent might actually mean and how
algorithms work across datasets to define and profile individuals. Learning analytics approaches can also be used to promote the
development of key employability skills. Supporting staff to develop skills in working with learning analytics applications is also an
investment in institutional capacity and leadership.
• Efficiency – Learning analytics can be used to evaluate and demonstrate institutional efficiency through a) measuring the impact of
initiatives and validating that benefits are being realised and b) demonstrating that publically-funded resource is being deployed in
support of the best outcomes of all students.
http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy

15. 1. Co-responsibility in an
asymmetrical power and contractual
relationship
…obligation to act is a co-responsibility of students and
institution, tempered by the asymmetrical power and
contractual relationship in which the institution has very
specific moral and legal duties to respond
Image credit: https://pixabay.com/en/michelangelo-abstract-boy-child-71282/
Prinsloo, P & Slade S (2017) An elephant in the learning analytics room – the obligation to act, LAK17 presentation, https://www.slideshare.net/prinsp/an-elephant-in-the-learning-analytics-room-the-obligation-to-act

16. • What is the current state of analytics adoption in your institution? Barriers /
activities?
• Do the issues and challenges uncovered by the Sheila project sound familiar
to you?
• What do your students think?