This document discusses using data and learning analytics to inform blended learning design and delivery. It describes learning design and curriculum design, as well as learning analytics and how they interact. Potential uses of data discussed include understanding what learning materials and activities students engage with the most, what students are learning, and tailoring learning design based on the characteristics of incoming student cohorts. Challenges around legal/ethical issues, data wrangling, and institutional culture are also addressed. The document encourages discussion of current and potential uses of curriculum analytics within institutions.

1. Sarah Davies, Head of higher education and student experience, Jisc
Data-informed blended learning design & delivery

2. Learning design and curriculum design
Image CC BY-SA 2.0 Aspire-edu
» The design of learning as a purposeful
activity with a planned outcome
» Course, modules, lessons, activities
» Totally informal to structured and
supported
A spectrum …

3. Learning analytics
» The application of big data
techniques such as machine based
learning and data mining to help
learners and institutions meet their
goals
– improve retention
– improve achievement
– reduce differential outcomes
Image CC BY-SA 2.0 Aspire-edu

4. How do they interact?
Curriculum
design
Data
Learning
analytics
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5. Data
Collection
Data
Storage
and Analysis
Presentation
and Action
Jisc Learning Analytics open architecture: core
Alert and Intervention
system
Other Staff
Dashboards
Consent Service
(tbc)
StudentApp:
StudyGoal
Jisc Learning
AnalyticsPredictor
Learning
Data Hub
StudentRecords VLE Library
Staff dashboardsin
Data Explorer
Self DeclaredData Attendance, Presence, Equipment use etc….
Data Aggregator
UDD TransformationToolkit Pluginsand/or Universal xAPITranslator

6. Challenges and enablers
Legal and ethical
issues
Data wrangling
Culture and
processes
Estates and
technology
Strategic vision and
appetite
Data capability
Data warehouse
Process redesign
Course redesign
Institutionalbarriers
Institutionalenablers
26/04/2018

7. Staff digital capabilities
ICT proficiency
Information,
data and
media literacies
Digital
learning and
development
Digital creation,
problem solving and
innovation
Communication,
collaboration and
participation
Digital identity
and wellbeing

8. Improving the student learning experience
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9. Designing learning and assessment…
» Based on interviews & case studies
» Aimed at FE and HE
» Freely available tools & techniques
» ji.sc/design-learn-assess
…in a digital age
Image CC BY-NC-ND Jisc

10. Where do you use data in this lifecycle?
Review
context
Explore
possibilities
Design
course/
module
Deliver,
assess,
feedback
Review
course
delivery
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When do you use data?
What for?
What data do you use?
How often?
How do you interpret it?
What would help make it
easier?

11. Potential use cases - curriculum analytics
»Understanding what works
»What are the learners using?
»What are they learning?
»What works for my incoming learners?
»Recommender for students
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12. Understanding what works
»Analysis of patterns of achievement across large cohorts
(faculty, institution) against recorded aspects of
curriculum design (assessment structure, online activities)
to derive good practice recommendations (in policies and
course design) for student success
› And assess impact of implementation of those recommendations
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13. What are the learners using?
»Cohort activity dashboard for course team showing hot
and cold spots in engagement with materials across
multiple sources (VLE, reading lists, specialist systems…)
– could be analysed against assessment marks.
› Enables lecturers to adjust signposting to key resources or re-
teach cold spots during current run of module
› Enables automatic recommendations for students on which key
resources to use next
› Enables adjustments/redesign between presentations of module
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14. What are they learning?
»Module-level analysis of which curriculum and academic
practice areas students are stronger and weaker in on
average, based on detailed assessment data (eg marks
against rubric, or against questions in tests, common
mistakes, automated analysis of assignments??)
› Used to adapt teaching to focus on key misunderstanding/gaps
› Signal support in key areas of academic practice
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15. What works for my incoming learners?
»Analysis of data on an incoming cohort, based on their
engagement patterns on previous modules, or for first
years, other characteristics eg incoming qualifications,
family history of HE, distance to study…
› Enabling adaptation of learning design for incoming cohort.
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16. Recommender for students
»Recommendations to students for key learning activities
to do or resources to read/use based on analysis of
patterns of activity of previous successful students.
› What do students want to see to help them steer a path through
their studies?
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17. Discuss in your groups:
»Are you already doing any of these?
»Would your institution be interested in exploring these
kinds of use cases? Which ones?
»Are there any that are totally implausible or infeasible?
»How do we best engage course teams and students in
this? Any other key stakeholders?
»What could Jisc do to support you in moving towards any
of these?
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18. jisc.ac.uk
Get in touch
https://analytics.jiscinvolve.org/
Sarah Davies
Head of HE and student experience
sarah.davies@jisc.ac.uk
26/04/2018