Lak13 knight buckingham shum littleton paper. Paper available here http://oro.open.ac.uk/36635/

1. Epistemology, Pedagogy, Assessment –
Learning Analytics?
Simon Knight @sjgknight
Image from http://xkcd.com/903/ licensed under a Creative Commons Attribution-NonCommercial 2.5 License.

2. 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
Images – mostly from Wellcome Images
http://wellcomeimages.org/ under CC licence

3. LÆAP-ing the divide
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

4. Talk Structure
1. Introduce epistemology
2. Discuss implications for
assessment (& LA) – the
Danish example
3. The other side of the coin –
application & understanding
student’s processes
4. An epistemology for
knowledge building (tools)

5. Introduction to
Epistemology, Pedagogy & Assessment

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

7. 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)?

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).

9. 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, but they
might be flexible enough to move
beyond the current impasse

10. Learning Analytics
• Data mining
• Digital trace –
including linguistic
data
• Deployed for
pedagogic purposes

11. Epistemology & Assessment
an example

12. Danish exams with internet access
http://news.bbc.co.uk/1/hi/education/8341589.stm
Epistemological assumptions
• 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."
A role for LA?
• Potential for auto-grading – LA role, a new model?
http://www.timeshighereducation.co.uk/416090.article

13. This example illustrates…
Epistemology of assessment, through exploring student’s
judgements on epistemic concepts such as ‘credibility’

14. Epistemological assumptions
- Knowledge is communicative, and discursive;
language both represents, and creates knowledge
- Separating nuggets of information from their
contextualised uses may not assess knowledge

15. Assessment should nurture knowledge
development and practices (through AfL)

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. The other side of the coin

18. The other side of the coin…
• What we ask students to do
• What they do

19. Epistemology: In action
• “What they do”
• Analytics give unprecedented(?) access
• Marks a shift, from standardised assessments
to knowledge in action
• From seeing in a vacuum, to seeing “for”

20. The Lens of Epistemic Beliefs Behaviours
“epistemological beliefs are a lens for a learner's views on
what is to be learnt” (Bromme, 2009)
• Certainty, simplicity, source, justification – for knowing
• Search allows us to probe; Collaborative Information
Seeking is particularly salient

21. No ‘unveiling’ of beliefs
• “In action” is key; language “to do”
• Psychometrics - many issues of standardised
assessments (& current measures are poor)
• They mask variance, subtleties and contexts

22. Examples of epistemic behaviour
Explicitly or implicitly (behaviour not belief…)
people:
• select sources of information
• have standards by which to judge credibility
• collate information in meaningful ways
• build arguments and make claims
• decide when to start and stop looking for
information
• decide (implicitly or explicitly) the breadth and
depth of information required

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

24. Evidence Hub

25. Epistemic Behaviours & Tools
By sharing such maps we:
– Facilitate in-team information management by
making sensemaking explicit
– Can capture information about domain structures
for future users in distributed, asynchronous or
implicit information seeking
– Can feedback to users to support more advanced
epistemic perspectives (e.g. seeking evidence)

26. Next ‘class’ - LÆAP the divide
• LA built on well grounded EAP
• Importance of dialogue around knowledge
building
• The methods to analyse this meaningfully (see
also DCLA paper)

27. Dialogue as a frame
• Moving beyond ‘what do they access/click/add’
• Dialogue creates co-constructed frames for
knowledge building, and problem identification
• Pragmatic, sociocultural context

28. Natural Language Processing based LA
• Discursive context
• Discourse both as a context, and as a creator
of context – historic and fluid properties
• Importance of dialogue in knowledge creation
• Systems provide a target for chat, and a
means to mark sections of chat

31. Discourse Analysis
• Exploratory and accountable talk as social
mode of thinking or interthinking
• …with epistemic implications
• Foreground epistemic language used to
indicate the perspectives taken on content

32. Analytics questions addressed:
• How do users interthink to construct and solve
problems?
• How do users co-conceptualise their
information needs, and how good are they at
finding and mapping information to those
needs?
• How do users use each other, and information,
to build knowledge together

33. LÆAP into the middle space
1. LA implicate epistemologies
(Danish e.g.)
2. Use of data for particular
conversations/assessment/
AfL, is key
3. Design implications –
foreground particular facets
of data & activity

34. Thank you
@sjgknight
sjgknight@gmail.com
http://people.kmi.open.ac.uk/knight/
Tools:
• http://www.cs.waikato.ac.nz/ml/weka/index.html
• http://mallet.cs.umass.edu/
• http://solar.evidence-hub.net/
• http://cohere.open.ac.uk/
Our papers in this area:
• Discourse, computation and context – sociocultural DCLA revisited
http://oro.open.ac.uk/36640/
• Tracking epistemic beliefs and sensemaking in collaborative
information retrieval http://oro.open.ac.uk/36553/