LAK2011: 1st International Conference on Learning Analytics and Knowledge February 27-March 1, 2011 Banff, Alberta Anna De Liddo, Simon Buckingham Shum, Ivana Quinto, Michelle Bachler, Lorella Cannavacciuolo

1. LAK2011: 1st International Conference on Learning Analytics and Knowledge February 27-March 1, 2011 Banff, Alberta Discourse-Centric Learning Analytics Anna De Liddo, Simon Buckingham Shum, Ivana Quinto, Michelle Bachler, Lorella Cannavacciuolo Knowledge Media Institute, The Open University Business & Management Engineering, Universita’degli Studi Napoli Federico II, Italy cohere.open.ac.uk

2. A key indicator of meaningful learning is the quality of contribution to discourse Sociocultural perspective on learning “highlights the possibility that educational success and failure may be explained by the quality of educational dialogue, rather than simply in terms of the capability of individual students or the skill of their teachers.” We look at discourse as a key indicator for learning and explore discourse analysis as a method to identify where and how learning happens. (Mercer 2004) Discourse as Indicator of Learning

3. Discourse analysis focuses explicitly on language as social action Discourse and Argumentation are the tools through which people can compare their thinking, explore ideas, shape agreement, and identify or solve disagreements. If discourse is the tool through which learners think collectively, then discourse outcomes and discourse analysis can provide indicators to better understand the learning processes (Mercer 2000) Discourse as the Tool to Think Collectively

4. Discourse Analysis to Better understand the Learning Process What Discourse Environment?Chronologically VS LogicallyRendered Dialogue Environments The most established online dialogue environments render discourse chronologically, rather than logically, reflecting most strongly the sequence of contributions rather than their conceptual structure: for instance a Facebook dialogue….

7. Beyond threaded forums: tools for structuring and mapping issues, dialogue and argumentation 7 Online Deliberation: Emerging Tools Workshop Online Deliberation 2010, Leeds UK (30 June – 2 July) www.olnet.org/odet2010 ESSENCE: E-Science, Sensemaking & Climate Change ESSENCE workshop, KMI, Open University http://events.kmi.open.ac.uk/essence

8. disagrees with agrees with agrees with

9. disagrees with agrees with agrees with

11. Discourse Analysis to Better understand the Learning Process in Cohere we discuss what it mean to use Cohere’s online dialogue environment to monitor online learning activities and develop useful learning analytics, by starting on the analysis of the online discourse which learners are involved in. We demonstrate how discourse analytics can enable a deeper understanding of the online discourse, of the participants to the discourse and the social and learning dynamics.

12. Analytics Per Learner Analytics Per Learner

13. Table on Posts’ Type -> Learner Attention and Performance By looking at the post type table it is possible to evaluate learner’s performance connecting the discourse outcomes with the specific learning goal.

14. Table on Links’ Type -> Learners’ Attitudes Legend: Neutral link type Positive link type Negative link type

15. Comparing Users’ Usage of link types

16. Compare Thinking

17. Compare Thinking

18. Information Broker

19. Information Broker

20. Analytics Per Group: Discourse network statistics A Social-Semantic Network of Discourse Elements

21. Discourse network structure = Concept Network + Social Network

22. Concept Network

23. Concept Network Analysis and Visualization - links distributions enable to infer learning topics distribution

24. Concept Network Analysis and Visualization

25. Concept Network Analysis and Visualization

26. Social Network

27. Social Network Analysis and Visualization Outdegree= measure of users’ activity Indegree = indirect measure of relevance of a user’s posts.

28. Social Network Analysis and Visualization L1 L6

29. We have motivated a focus on learners’ discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. We are interested in the rhetorical role that a user’s contribution is making to a document or conversation and the nature of the connection to other contributions using semantic relationships. Using the Cohere system as an experimental vehicle, we have presented examples of learning analytics to better understand: learners’ attention learners’ rhetorical moves within the online discussion learning topics distribution learners’ social interactions Conclusions

32. References for Cohere Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on Computational Models of Argument, 28-30 May 2008, Toulouse, France. Available at:http://oro.open.ac.uk/10421/ De Liddo, Anna and Buckingham Shum, Simon (2010). Cohere: A prototype for contested collective intelligence. In: ACM Computer Supported Cooperative Work (CSCW 2010) - Workshop: Collective Intelligence In Organizations - Toward a Research Agenda, February 6-10, 2010, Savannah, Georgia, USA. Available at:http://oro.open.ac.uk/19554/ Buckingham Shum, Simon and De Liddo, Anna (2010). Collective intelligence for OER sustainability. In: OpenED2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona, Spain. Available at: http://oro.open.ac.uk/23352/ De Liddo, Anna (2010). From open content to open thinking. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications (Ed-Media 2010), 29 Jun, Toronto, Canada. Available at: http://oro.open.ac.uk/22283/ De Liddo, Anna and Alevizou, Panagiota (2010). A method and tool to support the analysis and enhance the understanding of peer-­to-­peer learning experiences. In: OpenED2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona, Spain. Available at: http://oro.open.ac.uk/23392/ Buckingham Shum, Simon (2007). Hypermedia Discourse: Contesting networks of ideas and arguments. In: Priss, U.; Polovina, S. and Hill, R. eds. Conceptual Structures: Knowledge Architectures for Smart Applications. Berlin: Springer, pp. 29–44.