lak13: epistemology, pedagogy, assessment and learning analytics
DESCRIPTION
Lak13 knight buckingham shum littleton paper. Available here: http://oro.open.ac.uk/36635/ Conference website: http://lakconference2013.wordpress.com/TRANSCRIPT
Epistemology, Pedagogy, Assessment – Learning Analytics?
Simon Knight @sjgknightImage from http://xkcd.com/903/ licensed under a Creative Commons Attribution-NonCommercial 2.5 License.
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
LÆAP-ing the divide1. 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
Talk Structure
1. Introduce epistemology2. 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)
Introduction to Epistemology, Pedagogy & Assessment
The TriadForeground relationships between:• epistemology (the nature of knowledge)• assessment (of learnt knowledge?)• pedagogy (the nature of learning)
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)?
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).
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
Learning Analytics
• Data mining• Digital trace –
including linguistic data
• Deployed for pedagogic purposes
Epistemology & Assessmentan example
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."
• Potential for auto-grading – LA role, a new model?http://www.timeshighereducation.co.uk/416090.article
http://news.bbc.co.uk/1/hi/education/8341589.stm
Epistemological assumptions
A role for LA?
This example illustrates…Epistemology of assessment, through exploring student’s judgements on epistemic concepts such as ‘credibility’
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
Assessment should nurture knowledge development and practices (through AfL)
Pedagogic assumptions
• Assessment is used in teaching (but doesn’t drive it)• Pedagogy should involve knowledge practices – not
assessment practices• Discourse is fundamental
The other side of the coin
The other side of the coin…
• What we ask students to do• What they do
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”
“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
The Lens of Epistemic Beliefs Behaviours
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
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
LA in Structured Knowledge Building
• Strong CSCL tradition to ‘make explicit’ in structured environments (Knowledge Forum, Belvedere, etc.)
Evidence Hub
Epistemic Behaviours & ToolsBy 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)
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)
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
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
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
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
LÆAP into the middle space1. 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
Thank you
@[email protected]://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/