Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Collective intel
1. Contested Collective Intelligence Resilience, Complexity & Sensemaking 1 Simon Buckingham Shum & Anna De Liddo Knowledge Media Institute, Open Learning Network Project Open University UK http://people.kmi.open.ac.uk/sbs http://people.kmi.open.ac.uk/anna PARC, Apr 1 st 2011
2. Acknowledgements Open Learning Network project (2009-12): olnet.org funded by the William & Flora Hewlett Foundation 2 OLnet Collective Intelligence workstream: http://olnet.org/collective-intelligence Developing conceptual foundations and infrastructure (people+proceses+tools) for Contested Collective Intelligence on the open social web. Example: Open Education Evidence Hub http://ci.olnet.org (alpha)
6. How do we augment this system’s capacity to sense, respond to, and shape its environment? 6 Through the lens of complex adaptive systems, resilience and network science... Through the lens of sensemaking and HCI... Hypermedia Discourse: social- semantic web + models of discourse
7. How do we augment this system’s capacity to sense, respond to, and shape its environment? 7 Through the lens of complex adaptive systems, resilience and network science... many interacting agents (human and software) many weak signals that can build up unexpectedly diversity and redundancy feedback loops visual analytics to reveal emergent patterns and network properties ability to withstand change and shock to the system
8. Resilience Walker, et al. (2004) define resilience as “ the capacity of a system to absorb disturbance and reorganize while undergoing change, so as to still retain essentially the same function, structure, identity, and feedbacks” 8
10. Resilience Platforms 10 http://www.futureofed.org/driver/Platforms-for-Resilience.aspx Creating flexibility and innovation amid system failures “ Platforms for resilience - enabling responsive flexibility, distributed collaboration, and transparency - will allow institutions to meet such challenges through innovation, adaptation, and openness.”
11. Resilience in knowledge-intensive ecosystems 11 When knowledge and understanding are key variables in the system, resilience depends on the capacity for learning e.g. awareness of discrepant evidence, critical practice, reflection and dialogue when confronted by challenges or shocks to the system.
12. How does this help? Working hypothesis: Confronted by overwhelming complexity... (e.g. incomplete, ambiguous data, complex adaptive systems, diverse perspectives, technical/social/political dimensions, time pressure…) … Personal and Collective Cognition break down in particular ways… We need Theories , Tools and Practices in order to create CI for tackling such dilemmas (and we need ways to teach these, both to our children, and the current workforce) 12
13. Augmenting human intellect (ack. Engelbart) 13 Sources include: Weick (1995); Kurtz & Snowden (2003); Browning, L. and Boudès, T. (2005); Hagel et al (2010) • Enable individuals to highlight important events and connections aggregate • Recommend connections based on different kinds of significant relationship Many small signals can build over time into a significant force/change • Scaffold the formation of significant inter- personal, learning relationships Much of the relevant knowledge is tacit, shared through discourse, not formal codifications • Generate gestalt views from the data evidenced in the platform, not from preconceptions Patterns are emergent • Stories and coherent pathways are important • Reflection and overlaying of interpretation(s) is critical Complex systems only seem to make sense retrospectively: narrative is an appropriately complex form of knowledge sharing and reflection for such domains • Pay particular attention to exceptions • Computer-supported argumentation • Make the system open to diverse perspectives ontologically, and in usability Dangers of entrained thinking from experts who fail to recognise a novel phenomenon Role for CI infrastructure? Phenomenon
14. Designing CI to embody resilience principles • model key coherence relations; explore narrative indexing 14 a stable state – however temporary – in epistemic terms is a plausible narrative • both technically (enabling innovation, interoperability and mashups) but also in how we represent interpretations (ideas as networks, not big chunks of text) use a decentralised, modular architecture • effective dissemination of findings in relation to key issues and what is already known enable experimentation • using social media to build learning relationships: trust, affirmation, challenge promote building of trust/social capital particularly for learning and sensemaking • shared awareness of dis/agreement amongst peers make tight feedback loops • manage diversity of worldviews, and the tensions this sets up build in the potential for diversity Role for CI infrastructure? Resilience principle
15. • a c a t t i h • ir • rg i in u t n g c l a m e it • h e n a on r tor ca • ss i l m v p s a um o es a o n gi l a c l • au c l th n s y u o t is a t • “ ki i ons a .. t ” nd arel How do we augment this system’s capacity to sense, respond to, and shape its environment? Through the lens of sensemaking and HCI... many plausible narratives: what was, is, or might be going on?... many representational artifacts being shared and annotated attention to the quality of conversation: how well are agents listening to each other and what kinds of contributions do they make? informal interaction mixed with stronger public claims many connections being made, both formal and fuzzy k a i t i k i ng • j x p tion ed. 15
16. Sensemaking: the search for plausible, narrative connections In their review of sensemaking, Klein, et al. conclude: “ By sensemaking, modern researchers seem to mean something different from creativity, comprehension, curiosity, mental modeling, explanation, or situational awareness, although all these factors or phenomena can be involved in or related to sensemaking. Sensemaking is a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively . […] A frame functions as a hypothesis about the connections among data.” 16
17. Sensemaking Weick proposes that: “ Sensemaking is about such things as placement of items into frameworks, comprehending, redressing surprise, constructing meaning, interacting in pursuit of mutual understanding, and patterning .” (Weick, [23], p.6) 17
18. Sensemaking Weick: “ The point we want to make here is that sensemaking is about plausibility, coherence, and reasonableness . Sensemaking is about accounts that are socially acceptable and credible” ([23] p.61) 18
19. contested collective intelligence... conversations are critical to sensemaking there is no master worldview we need CI infrastructures to pool awareness of how people are reading small signals, and amplify important connections 19
21. Where our tools fit… Given a wealth of documents, and tools to detect and render potentially significant patterns… 21
22. Where our tools fit… Given a wealth of documents, and tools to detect and render potentially significant patterns… 22
23. Where our tools fit: making meaningful connections between information elements… 23
24. Where our tools fit: making meaningful connections between interpretations 24 interpretation interpretation interpretation interpretation
25. 25 Where our tools fit: making meaningful connections between interpretations interpretation interpretation interpretation interpretation (a hunch – no grounding evidence yet) interpretation interpretation
26. 26 interpretation causes interpretation (a hunch – no grounding evidence yet) interpretation predicts interpretation interpretation Where our tools fit: making meaningful connections between information elements interpretation Is pre-requisite for
27. predicts Where our tools fit: making meaningful connections between information elements interpretation prevents interpretation interpretation 27 causes interpretation (a hunch – no grounding evidence yet) Is inconsistent with challenges interpretation Is pre-requisite for interpretation
28. 28 Challenging Argument… challenges supports Supporting Argument… Where our tools fit: building the story that makes sense of the evidence… i.e. plausible arguments Question responds to motivates Answer Assumption
29. 29 Challenging Argument… challenges supports Supporting Argument… Where our tools fit: building the story that makes sense of the evidence… i.e. plausible arguments Question responds to motivates Answer Hunch
30. 30 Challenging Argument… challenges supports Supporting Argument… Where our tools fit: building the story that makes sense of the evidence… i.e. plausible arguments Question responds to motivates Answer Data
31. 31 structured debate a prototype infrastructure for collective intelligence/social learning http://cohere.open.ac.uk Convergence of… web annotation social bookmarking concept mapping
32. Structured deliberation and debate in which Questions, Evidence and Connections are first class entities (linkable, addressable, embeddable, contestable…) 32
33. 33 Structured deliberation and debate in which Questions, Evidence and Connections are first class entities (linkable, addressable, embeddable, contestable…)
36. 36 Structured deliberation and debate in which Questions, Evidence and Connections are first class entities (linkable, addressable, embeddable, contestable…)
38. By looking at the post type table it is possible to evaluate learner’s performance connecting the discourse outcomes with the specific learning goal. Cohere analytics
39. Legend: Neutral link type Positive link type Negative link type Cohere analytics
41. Comparison of one’s own ideas to others De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L.(2011). Discourse-Centric Learning Analytics . Proc. 1 st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff Does the learner compare his/her own ideas to that of peers, and if so, in what ways?
42. De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L.(2011). Discourse-Centric Learning Analytics . Proc. 1 st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff Does the learner act as a broker, connecting the ideas of his/her peers, and if so, in what ways? Link broker: connecting other people’s ideas
43. seeing the connections people make as they annotate the web using Cohere Visualizing all the connections that a set of analysts have made — but unfiltered, this may not be very helpful
44. Visualizing multiple learners’ interpretations of global warming sources Connections have been filtered by a set of semantic relationships grouped as Consistency — semantic filtering of connections De Liddo, A. and Buckingham Shum, S. (2010). Cohere: A prototype for contested collective intelligence. In: ACM Computer Supported Cooperative Work (CSCW 2010) - Workshop: Collective Intelligence In Organizations , February 6-10, 2010, Savannah, Georgia, USA. http://oro.open.ac.uk/19554
47. ! Moving from document annotation to connection-making for sensemaking
48. … role … has been elusive BACKGROUND KNOWLEDGE: Recent studies indicate … … the previously proposed … … is universally accepted ... NOVELTY: OPEN QUESTION: ... new insights provide direct … little is known … evidence ... ... we suggest a new ... approach ... Current data is insufficient … ... results define a novel role ... CONRASTING IDEAS: … unorthodox view resolves … paradoxes … In contrast with previous hypotheses ... ... inconsistent with past findings ... GENERALIZING: ... emerging as a promising approach Our understanding ... has grown exponentially ... ... growing recognition of the importance ... SIGNIFICANCE: studies ... have provided important advances Knowledge ... is crucial for ... understanding valuable information ... from studies SURPRISE: We have recently observed ... surprisingly We have identified ... unusual The recent discovery ... suggests intriguing roles Discourse analysis with Xerox Incremental Parser Detection of salient sentences based on rhetorical markers: SUMMARIZING: The goal of this study ... Here, we show ... Altogether, our results ... indicate Ágnes Sándor & OLnet Project: http://olnet.org/node/512
53. XIP/Cohere integration: conclusions from analysis of the corpus (ack: Ágnes Sándor, XRCE) Machine annotation can effectively draw attention to key issues and contrasting ideas , in a cost effective and timely manner Human annotation adds higher-level cognitive activities such as abstracting, contextualizing and summarizing . An appropriate combination of both machine and human annotation can augment and enhance both human and machine analysis.
