The document discusses designing social mechanisms to support informal learning in online communities. It proposes improving a list of social mechanisms from literature and designing and evaluating mechanisms for two case studies. The overall goal is to design incentives using appropriate mechanisms to motivate learning based on organizational objectives and user models. The research will analyze reputation systems to model expertise, focusing on sources, claims, targets and weights to better understand authority in communities.

1. A reputation system to model expertise in online communities Doctoral Consortium UMAP2011 + some social mechanisms

2. From reputation to social mechanisms Peer-based learning in online communities Challenges Motivation Quality Initial research direction: reputation

3. Reputationprinciples Linkedwith trust Reciprocity & Goodbehaviour Someoneelse’s story about me Linkedwithidentity; long-lived A currency, a resource Narrative, dynamic Based on claims, transactions, opinion, rating, endorsements, … Based on indirect information Context of community Contextual Trade-off between trust & privacy Regular assessment of reputationquality Windley, Phillip J., Kevin Tew, and Devlin Daley. A Framework for Building Reputation Systems. In WWW2007.

4. Main challenge What: Aggregating and interpreting meaningfulinteractions between & among objects* in online communities Why: to give insight in the value on the object level based on user feedback and usage. * Objects are people and information objects

5. So what did I do? Literature Learning theories, knowledge management Trust and reputation Look at different successful reputation systems/techniques Google PageRank, eBay, StackOverflow, Guru, etc. How? value flow context integration sustainability Hennis, T., Lukosch, S., & Veen, W. (2011). Reputation in peer-based learning environments. In O. C. Santos & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies. IGI.

6. Value flow Source objects People, organizations, … Target objects Blog posts, articles, … Claims (value statements) Rates, links, recommendations, etc. Implicit, explicit (Farmer & Glass, 2009)

7. Context integration

8. Sustainability, i.e. StackOverflow

11. Concept reputation model

12. Concept reputation model – 1/6claim weight claim weight ~ expressiveness of the claim type (i.e. rating versus click)

13. Concept reputation model – 2/6target object weight claim weight ~ expressiveness of the claim type (i.e. rating versus click) target object weight ~ importance of the contribution type (i.e. article versus comment)

14. Concept reputation model – 3/6affiliated keyword weight apples (0.8) pears (0.2) claim weight ~ expressiveness of the claim type (i.e. rating versus click) target object weight ~ importance of the contribution type (i.e. article versus comment) affiliated keyword weight ~ expressiveness of tag about the target object

15. Concept reputation model – 4/6source object weight = authority match keywords with reputation! apples (64) pears (33) kiwis (0) apples (0.6) pears (0.2) kiwis (0.2) claim weight ~ expressiveness of the claim type (i.e. rating versus click) target object weight ~ importance of the contribution type (i.e. article versus comment) affiliated keyword weight ~ expressiveness of tag about the target object source object weight = (source object’s reputation for keyword) / (global rep. value for that keyword) DIFFERENT WEIGHTS FOR DIFFERENT KEYWORDS

16. Concept reputation model – 5/6claim value apples (64) pears (33) kiwis (0) apples (0.6) pears (0.2) kiwis (0.2) claim weight ~ expressiveness of the claim type (i.e. rating versus click) target object weight ~ importance of the contribution type (i.e. article versus comment) affiliated keyword weight ~ expressiveness of tag about the target object source object weight = (source object’s reputation for keyword) / (global rep. value for that keyword) claim value = rating (implicit/explicit), i.e. 4/5 stars

17. Concept reputation model – 6/6claim value apples (64) pears (33) kiwis (0) apples (0.6) pears (0.2) kiwis (0.2) claim weight ~ expressiveness of the claim type (i.e. rating versus click) target object weight ~ importance of the contribution type (i.e. article versus comment) affiliated keyword weight ~ expressiveness of tag about the target object source object weight = (source object’s reputation for keyword) / (global rep. value for that keyword) claim value = rating (implicit/explicit)  3 claims (one for each affiliate keyword)

18. Why is this a useful approach? Target object weight Affiliate keyword weight Claim for keyword k Claim weight Authority Claim value (rating)

20. Extensible & configurable

21. including other weights and metrics (i.e. trust value, network centrality, etc.)

22. integrating formal ontologies

23. taking into account all relevant interactions

24. Starting point for the design of such a system

26. Rather large distributed online network or communityClaim for keyword k Claim weight Authority Claim value (rating)

27. Reputation clouds (object model)

28. Peer Support Community Trying to get funding (50k) for first prototype Context Blackboard apps & Tags Comment & Rating functionality Application of reputation model Source: Teacher or Student Target: comment, answer Claims: like, page visit, follow Reuse of reputation Award, status, social comparison, gaming mechanisms (competition)

29. Contextualized support: content and user support

30. But… Not smart to bet on 1 horse, when 3 already dropped out of the race…

31. So.. New scope (since 2 weeks) Social mechanisms to design incentive structures to support informal learning in online communities Hennis, T. A., & Kolfschoten, G. L. (2010). Understanding Social Mechanisms in Online Communities. In G. D. Vreede (Ed.), Group Decision and Negotiation 2010. Delft, the Netherlands. Hennis, T. A., & Lukosch, H. (2011). Social Mechanisms to Motivate Learning with Remote Experiments - Design choices to foster online peer-based learning. CSEDU 2011. Veen, W., Staalduinen, J.-P. V., & Hennis, T. A. (2010). Informal self-regulated learning in corporate organizations. In G. Dettori & D. Persico (Eds.), Fostering Self-regulated learning through ICTs. Genova, Italy: Institute for Educational Technologies Italyʼs National Research Council.

32. Bouwman et al. (2007) We argue that social software systems should trigger mechanisms that allow us to associate with or form social groups, whether online or in the real world. Such mechanisms would acknowledge human motivations, like eagerness for exploration, curiosity, inquisitiveness, civilization, valuation of belonging, achieving self-realization, enjoying one-self. Bouman, W., Hoogenboom, T., Jansen, R., Schoondorp, M., Bruin, B. de, & Huizing, A. (2007). The realm of sociality: notes on the design of social software. Amsterdam.

33. Research objectives Designing incentives: which mechanism to apply when and how? Design of processes Supportive technologies Focused on informal learning in organizations during initial phase – startup phase Cases Philips Lighting Mediamatic (various communities)

34. Step 1 – improve list of mechanisms (literature) Matching objectives Organizational objectives User models Fit / Embedding in practice Rhythm Leadership and roles Heterogeneity & Diversity Learning & Networking Reputation & Identity Reciprocity & Feedback Common Ground & Privacy Self-efficacy & Social comparison Autonomy? Empowerment? Curiosity & Provocation IMPROVE

35. Step 2 – Design & Evaluate Philips Lighting 3 communities by December Mediamatic Design team Design & Test new things Evaluate existing communities using the Anymeta platform Qualitative Quantitative  50+ small-medium sized online communities Blogging communities Storytelling Event communities & Professional networks

36. Rating & Reputation Reciprocity & Feedback Matching online and offline networks through RFID Notifications & Activity

37. Profiling & Identity Interaction types ~ Motivation? Personalization & Recommendations

38. Keyword based Recommend content, people, projects, …

40. Definitions A social mechanism is a plausible hypothesis, or set of plausible hypotheses, that could be the explanation of some social phenomenon. An incentive is any factor (financial or non-financial) that enables or motivates a particular course of action, or counts as a reason for preferring one choice to the alternatives.

41. Overall picture Typicalvaluejudgements/claims Context parameters  aggregateandinherit/inference repute value judgements: use/rate/recommend context parameters: tag contribute

42. 1: Contribution“I write a blog post” Knowledge (topic) What kind of contribution? What kind of topic? contributions Competencies (process) What kind of action? Which competencies involved?

45. Example: researcher Value statements: . IF (Impact Factor) . citing . rating/reviews . linking Context: . author & user profiles . citing article or blog . journal . content analysis . keywords --------------- -------------- ----------------- ---------------- ------------- --------------- -------------- ----------------- ---------------- ------------- DOI IF ORCID reputation citations published_in write papers --------------- -------------- ----------------- ---------------- ------------- link ratings BlogRank

46. StackOverflow dump Analyze the concept of authority Steps: Define the sources, targets, claims, and weights Sources: SO-users / Targets: Answers, Questions, Users / Claims: votes up/down Context: keywords Conduct analysis and compare the results with the traditional reputation Improve algorithm

47. <foods> <pizza title=“Deluxe Pizza”> <name>The Deluxe</name> <toppings> <topping>peppers</topping> <topping>pepperoni</topping> <topping>mushrooms</topping> <topping>cheese</topping> <topping>tomato sauce</topping> </toppings> <price>7.99</price> </pizza> </foods> Research challenges Reputationontologyforp2p learning Formalize common “Sources”, “Claims”, and “Targets” in these communities Combine formalontologiestodescribe topics and skills, anddynamicfolksonomiestodescribecontextual parameters Reuse of reputation information Open Standards & Search Tool development implement, test andimprovereputation system