Learning analytics offers great opportunities but also can feel scary at times to educators and learners. Yet, learning analytics is not random, but is tightly coupled to the learning designs that generated the data. This slidedeck focuses on conceptual approaches for thinking about data during course planning and instructional design. This is the extended slidedeck of the "Design Thinking for Learning Analytics" Workshop at the Learning Analytics Innovation Room of the Centre for Education and Learning at TU Delft on 8 April 2019.

1. Design Thinking
for
Learning Analytics
Christian Glahn
@phish108
&
Marion R. Gruber
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2. Learning analytics is cool!
Dashboards
Adaptive scenarios
Personalisation
Gamification
Simulations
Contextualization
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3. Is learning analytics cool?
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4. What data on learning is available?
Where does data come from?
What does the data tell us about learning?
Who controls the data and analytics?
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5. Legal requirements from GDPR
Data must be functionally required
Data use must be transparent
Collected data must fit the purpose
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6. Learning analytics
can feel
overregulated and scary

7. You can’t get nothing out
that you didn’t put in!
Learning analytics is tightly coupled to Learning Design
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8. Learning
Activities
Activity
Outcomes
Learning
Analytics
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9. How do
learning designs
influence
learning analytics?
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11. Each pattern activity
has a characteristicdata footprint
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13. RoleRole
Learning
verbsTeaching
verbs
In-class methods
and tools
Online methods
and tools
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15. Outcome centric
constructive alignment
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16. Data that is characteristic
for generating learning
outcomes through activitiesProduct /
Knowledge
Experience /
Skill
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17. Tool centric
tracing
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18. Data that is provided by
learning resources and tools
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19. Intervention centric
monitoring
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20. Data that is relevant for
educational interventions
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21. Process centricorchestration
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22. Data that informs
completion and activation
rules of learning activities
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