I was an invited speaker at the Higher Education Academy Big Data summit where I talked about our Journal of Learning Analytics (2014) paper.

1. Learning and analytics – where do the two
meet?
Simon Knight @sjgknight
Image from http://xkcd.com/903/ licensed under a Creative Commons Attribution-NonCommercial 2.5 License.

2. Interpretive flexibility
• The basic question is not
what can we measure? The
basic question is what does a
good education look like?
(Gardner Campbell)
• Do we value what we can
measure, or measure what
we really value? (Guy Claxton, BBC Radio 4
Education Debate, Nov. 2012) 84

3. Introduction to
Epistemology, Pedagogy & Assessment

4. The Triad: Bounding the middle space
Foreground relationships between:
• epistemology (the nature of knowledge)
• assessment (of learnt knowledge?)
• pedagogy (the nature of learning)

5. Key questions
• What does it mean to
know?
• How do we decide
(assess) if someone
knows or not?
• How do we get people
to come to know (to
learn)?

6. Bounding the middle space
1. LA ‘buy in’ to ways of
thinking about
epistemology, assessment
and pedagogy
2. For theoretical, practical,
ethical reasons we should
engage in these debates
3. These considerations have
practical implications – the
middle ground

7. Why does epistemology matter?
“…assessment is one area where notions
of truth, accuracy and fairness have a
very practical purchase in everyday life”
(Williams, 1998, p. 221).

8. Why does epistemology matter?
“…assessment is one area where notions of
truth, accuracy and fairness have a very
practical purchase in everyday life”
(Williams, 1998, p. 221).
• LA ‘buy in’ to particular ways of thinking
about these issues, & are embedded in
systems, but they might be flexible enough
to move beyond the current impasse

9. Learning Analytics
• Data mining
• Digital trace –
including linguistic
data
• Deployed for
pedagogic purposes
• Big and rich

10. Epistemology & Assessment
an example

11. Danish exams with internet access
http://news.bbc.co.uk/1/hi/education/8341589.stm

12. Danish exams with internet access
• Allows testing of problem-solving and analysis - sifting
information
• "if you allow communication, discussions, searches and
so on, you eliminate cheating because it's not cheating
any more. That is the way we should think."
Epistemological assumptions

13. Danish exams with internet access
• Potential for auto-grading – LA role, a new model?
http://www.timeshighereducation.co.uk/416090.article
A role for LA?

14. Epistemic assumptions
- It isn’t “knowledge” to recall facts
- Knowledge lies in the use

15. Assessment…
Formative or summative?
What is being assessed?

16. Pedagogic assumptions
• Assessment is used in teaching (but doesn’t drive it)
• Pedagogy should involve knowledge practices – not
assessment practices
• Discourse is fundamental

17. Technological determinism?
• Technology is situated
• Interpretive flexibility

18. The other side of the coin

19. Multiple perspectives
• One side: Direct technological affordances
• The other: The practices around technologies
– Educator practices
– Wider policy/accountability
context
– Student practices

20. Educator Practices: LA potential
• E.g. at LAK14: Wise, Piety & Hickey

21. Policy: LA potential
• Accountability systems, educator support,
investment in AfL, setting priorities (e.g.
Denmark)

22. Student Practices: LA potential
• Analytics give unprecedented (?) access to
“What students do” - & assumptions around
this
• Potential shift, from standardised assessments
– need for new (psychometric) models

23. LA in Structured Knowledge Building
• Strong CSCL tradition to ‘make explicit’ in
structured environments (Knowledge Forum,
Belvedere, etc.)

24. Natural Language Processing based LA
• Automated essay feedback
• Dialogue scaffolds and automated tutors
• Social functions – “if you’re interested in
x, you should talk to …”
• Tutor support “student a might need some
help on…”

25. Hot Topics
• Analytics for student ‘dispositions’
• ‘Personalisation’ through community building
– Social networks
– Discourse support
– Community knowledge
• Educator support, visualising data

26. Conclusions
1. Context of LA as assessment,
and pedagogic aid, in context
of policy considerations –
Danish example
2. Educator practices matter
3. Hope to improve student
practices
4. Use of data for particular
conversations/assessment/
AfL, is key
5. Design implications –
foreground particular facets of
data & activity

27. Thank you
@sjgknight
sjgknight@gmail.com
http://people.kmi.open.ac.uk/knight/
Our papers in this area:
• http://oro.open.ac.uk/39226/ Epistemology, assessment, pedagogy:
where learning meets analytics in the middle space (2014)
• http://events.kmi.open.ac.uk/icls-analytics/ ICLS workshop on
analytics for learning and becoming in practice (2014)
• Discourse, computation and context – sociocultural DCLA revisited (2013)
http://oro.open.ac.uk/36640/
• Tracking epistemic beliefs and sensemaking in collaborative information retrieval (2013)
http://oro.open.ac.uk/36553/

28. Acknowledgements
Thanks to my co-authors and supervisors Simon
Buckingham Shum and Karen Littleton for their
work on this and our workshop papers.
Thanks to Cindy Kerawalla and anonymous
reviewers for their helpful suggestions on the
earlier (LAK13) version of this paper.
Images – mostly from Wellcome Images
http://wellcomeimages.org/ under CC licence