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CIC Networked Learning Practices Workshop - Caroline Haythornthwaite

  2. Introduction to Workshop 2 Part 1 (90 minutes)  Presentation (45 minutes)  Social Network Perspective  Some contemporary trends in connectivity  A social network perspective on connectivity  Principles  Complexity  SN & information exchange, knowledge co-construction, learning  Exercise (30 minutes)  Exploration of SN focus on learning : What constitutes a learning tie?  BREAK Part 2 (60 minutes)  Presentation (30 minutes)  New Media and Learning  Network building role of Media  Exploring the attributes of communication channels  Exploring the place of different modes ina a multiplex interaction framework  Exercise (30 minutes)  Discussion/brainstorming on effects of new media on learning And/or  Design exercise re socio-technical balance of pedagogical intent and media use  WRAP-UP
  3. Goals of the Workshop 3  Part 1  To familiarize you with Social Network concepts and gain an understanding of a Relational Perspective for research  Warning – networks are addictive!  To show how network perspective can be applied to questions about learning and knowledge building – online, offline & blended, formal, informal & non-formal  Part 2  To introduce how new media disrupt traditional network connectivity, open up new opportunities, and forge new connections  Consider how new media change learning practices
  4. A bit about me My Background and Interests  How do people work, learn and socialize together at a distance and through computer media?  Communication, Collaboration, Community  Studies : Online Learning Networks  Social networks / virtual communities  Distributed learners / e-learning  Collaborative research teams / distributed knowledge  Information sharing and learning / ubiquitous learning  New directions  Crowds and communities  Social media and learning  Learning analytics A few theoretical orientations  Relational perspective – who does what with whom as the unit of analysis  Sociotechnical perspective – practice, observed behaviour, technology use, etc. arises from the interplay of people and technology  social informatics, organizational informatics, community informatics
  6. A Mosaic of Trends
  7. Transformative Trends • Technology enabled • Socially maintained • Media facilitated Social Networks E-learning • Networked learning • New literacies • Distributed Knowledge • Contributory behaviour • Collaborative practices • Crowds and Communities Participatory Culture Big Data • Analytics • Visualization
  8. (1) Social Networks More than just media A transformation in work and social organization Networks, communities, crowds =============== Social Network Analysis - an approach, method and vocabulary for addressing societal structures  Actors such as people, groups or organizations, tied by relations that form networks, analyzed and displayed as graphs Rainie & Wellman, 2012, Networked: The new social operating system.
  9. (2) E-Learning More than a transfer of learning to an online stage Learning unbound from institutional structures, embracing flow across physical, geographical, disciplinary boundaries Sustained over a lifetime, enacted in multiple, daily instances Mobile, learning from and in new and different locations as needed and on the devices at hand. Engaged act created through both technical and social decisions A transformative movement for learning in a networked world Haythornthwaite & Andrews, 2011, E-learning Theory and Practice
  10. Use of Social Networking Sites: • Adults: 60% • Non-students 18-24: 88% • Undergrads: 86% • Graduate Students: 82% • Community College: 72% Net Generation College Students and Technology (data US 2010)
  11. Learning in a Networked World  Educational Institutions: Formal  Degree based, online learning environments  Structured curriculum, resources, roles  Textbooks, instructors, tutors  Informal and non-formal  Personal, interest based, community of interest from casual to serious leisure to non-degree based learning  Emergent configurations and roles  E-Learning, Networked learning, Ubiquitous learning  Learning on and through the web  Embedded in home, work, travel contexts  Contributing as well as retrieving  Collaboratively determining learning trajectories  Working like experts rather than novices, entrepreneurial
  12. (3) Participatory Culture Personal but shared need • Creative Commons Changes in authority structures • Peer production, Peer evaluation Differing by enterprise • Crowds, Communities Motivations • Public Good, Career Outcomes • Social Capital, Community Resilience, Knowledge distribution An opportunity to draw on the power of crowds and the support of communities Jenkins et al, (2006). Confronting the Challenges of Participatory Culture: Media Education for the 21st Century.
  13. (4) Big Data Proliferation of data and information streams Dynamic, Small to Huge Geo-located Needing collection, management, analysis, presentation, validation Ethical, intelligent use Data, information, analytics and visualization literacy When you automate, you informate (Zuboff)
  14. Learning Analytics 14  Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. cs  Journal of Learning Analytics (@UTS)  Special issues:  Journal of Educational Technology & Society (2012)  American Behavioral Scientist (2013)  Australian representatives for SoLAR:  Simon Buckingham Shum, University of Technology, Sydney  Shane Dawson, University of Southern Australia  Grace Lynch, University of New England, Australia  Phillip Long (University of Queensland, Australia)
  16. Networks and Learning Questions Today …  How can network perspectives be used to examine learning and education processes?  What needs to be done to build a network analytic base for learning?  How can what is known in social network research be used to jumpstart learning networks research?  Social network analytic views of learning  Connecting this to aspects of learning and networks that lend themselves to a research agenda for learning  Interwoven with examples related to learning and examples from studies of learning networks
  17. Social Network Building Blocks  Actors tied by relations that form networks, analyzed and displayed as graphs
  18. Networks are revealed in our interactions  Personal or Egocentric view  Bird’s eye, helicopter or Whole Network view Eddie Fran Fred Ginger Ego Child at home Child at college Spouse Parents Pete Classmates Pat Boss Pam Co-workers Online learners Science research team
  19. Network Perspective Personal  Does the individual have in their network access to sufficient resources?  How is the individual engaging with their network? Communal  Are there sufficient ties and resources within the network to support communal awareness, action, solidarity?  Are there sufficient external connections to support access to new Answer person, and Discussion Person info. ? (Fig 3a&3b from Welser et al, 2007)
  20. Map of science derived from clickstream data “Knowledge Map” based on probability of clicking between journals. (Figure 5 in Bollen et al, 2009) Networks formed by our use of systems Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, et al. (2009) Clickstream Data Yields High-Resolution Maps of Science. PLoS ONE 4(3): e4803. doi:10.1371/journal.pone.0004803
  21. Networks observable from our data traces  Social media, point of sale, GPS **Activist discussion: Canadian Tar Sands (Brittany White) **London Olympics, 2012 (Anatoliy Gruzd) #hcsmca – Health Care Social Media Canada Gruzd & Haythornthwaite, 2013 **Networks courtesy of the Social Media Lab, Dalhousie University
  22. Social Network Perspective  Not just pretty pictures  A method for social analysis: social network analysis  A relational approach  Emphasis on what people do together  Who talks to whom about what?  Who gives, receives, shares what kinds of resources?  Who learns from whom?  A network approach  Attention to network structures and their outcomes  How does the structure of a network affect resource flow among group members?  When do resources reach others?  What resources can network members access?
  23. A moment to look at network features Networks show Cohesion  Density, Centralization, Cliques, Structural Holes Actor Prominence  Prestige, Influence Roles and positions  Stars, Brokers, Gatekeepers, Isolates Network outcomes Resource Flow  control  inclusion and exclusion  early and late access to information Roles  information suppliers, help givers, social support givers Social structures  Social capital, network resilience In-class collaboration network – who works with whom
  24. Interactions  Rather than aggregates of behaviors  On average, 6000 tweets are sent per second, of these types:  Pointless babble – 40%; Conversational – 38%; Pass-along value – 9%; Self-promotion – 6%; Spam – 4%; News – 4% (Pear Analytics. 2,000 tweets 2009 US in English)  Examine behaviours in terms of social interaction  Pointless babble is ‘social grooming’ (boyd, 2009)  Information posting via Twitter comes with expectation of reciprocity (Holton et al, 2014)  Actors in closer relationships (work, friendship) communicate more often, about more things (Granovetter and others), and via more media (Haythornthwaite & Wellman, 1996)
  25. Under the hood: Network Data  Who to/from whom  Actor x Actor, 1-mode networks  Affiliation Networks  Actor x Events, 2-mode networks  Can derive actor x actor, and event x event networks  Reveals hidden common experience, knowledge TO Ava Brad Cam Dale Ed Frieda Gail Henri FROM Ava 0 1 1 1 0 1 1 0 Brad 0 0 1 0 1 0 0 0 Cam 0 1 0 1 1 0 1 0 Dale 0 0 1 0 0 1 1 0 Ed 0 0 0 1 0 1 1 0 Frieda 0 0 0 0 0 0 1 0 Gail 0 0 0 1 0 0 0 0 Henri 1 1 1 1 1 1 1 0 TO Corn Soy Tomatoes Carrots Peas Turnips FROM Ava 1 1 1 1 0 1 Brad 0 1 1 0 1 0 Cam 0 1 0 1 1 0 Dale 1 0 1 0 0 1 Ed 0 1 0 1 0 1 Frieda 1 0 1 0 1 0 Gail 1 0 0 1 0 0 Henri 1 1 1 1 1 1
  26. Let’s do a quick affiliation network 26  Who has read these books:  Any of Wizard of Oz, Alice in Wonderland, Peter Pan  Watership Down  Harry Potter (any of them)  Goodnight Moon  A contemporary Children’s Australian <is ‘Diary of a Wombat’ a reasonable choice?>  A classic Australian novel <help me name one!>  Who has attended these conferences:  Ascalite, Internet Researchers, CSCW, LAK … others?  Who knows at least one person in this room?  Who knows everyone in this room?  … latent tie structure
  27. Social Networks Research 27 WHAT DO WE KNOW SO FAR ABOUT SOCIAL NETWORKS?
  28. Some Key SNA Findings 28  Individual/Dyadic/Triadic  Relational multiplexity  Strength of weak ties (Granovetter)  Strength of strong ties (Krackhardt, Granovetter)  Forbidden triangle  Organizational  Structural holes (Burt)  Diffusion of innovations (Rogers)  Gatekeepers, Technological Gurus (Allen); Absorptive Capacity (Cohen & Levinthal)  Internet/Media effects  Media Multiplexity (Haythornthwaite & Wellman)  Latent Ties (Haythornthwaite)  Crowds and Communities (Haythornthwaite ) Society  Community lost, saved, liberated (Wellman)  Core discussion networks (McPherson & Smith-Loven)  Homophily  Birds of a feather flock together  Transitivity  Tendency for our friend’s friends to be our friends  Inclusion/Exclusion  Organizational work hours and places support homophily (Smith-Loven)  Social mobility (Lin)  Social capital (accessed and mobile) (Lin)
  29. Weak and Strong Ties Weak Ties . . .  Acquaintances, casual contacts  Tend to be unlike each other  Travel in different social circles  Resource exchanges  Infrequent, instrumental  Few types of resources, exchanges, relations  Low motivation to share  Strength of weak ties  Experience / Information /Attitudes comes from a different social sphere  But, no obligation to share . . . Strong Ties  Friends, close friends, team-mates  Tend to be like each other  Travel in the same social circles  Resource exchanges  Frequent, multiple types: emotional and instrumental  High level of intimacy, self-disclosure  Reciprocity in exchanges  Strength of strong ties  Motivated -- obliged -- to share what resources they have  But, access to same resources
  30. Societal Connections Community Lost, Saved & Liberated, and now Networked (Wellman)  Lost. Lament for the passing of the pastoral ideal of community, lost in urbanization  Saved. Rediscovered local community amid the towers of urban living  Liberated. Social network based (Wellman, 1979) – place independent, liberated from geography, sustained through phone and travel  Networked – the New Operating Systems (Rainie & Wellman, 2012)  Personal communities – networked individualism – sustained through ICT, networked living, wireless connectivity  Neo-liberated. Finding career, work, friends, homophilous others through computer networks  Hyper-liberated. Unbound by boundaries of organizations and traditional workplaces  Free of constraints of single career, employer, institution  learning within institutional boundaries (e.g., MOOCs)  human capital resource location  single author/ publisher/curator  Community now found in myriad multi-threaded instances
  32. Actors  Individuals  Adults, teens, children  Employers, employees, co-workers  Collectives  Groups* or Teams  Organizations  Communities*  Other  Countries, Governments, Schools, Websites, Documents  Individuals  Teachers, students  Schools, universities  Co-workers, collaborators, team mates  Collectives  Research teams  Professional organizations, clubs  Communities, neighborhoods, societies  Online groups  More? NOTE: A group in SNA is defined as a highly interconnected clique. Thus Groups – and I also maintain Communities – are a hypothesis to be tested.
  33. Actor Roles and Positions  Centrality. Network Star  Betweenness. Bridge, Broker  Prominence  Influence, Prestige  Equivalence  Identical ties to and from others or to and from equivalent others  E.g., teachers of same class, or teachers of equivalent classes in different schools  Roles  Technological guru  Troll  Information provider  Learner-leader, facilitator  Answer or discussion person  Who dominates conversation? Who seeds it?  Who suggests new resources?  Who controls the flow of information?  Who does everyone ask? And about what?  Who does everyone listen to? And about what?  Who gives emotional support?  Who disrupts, diverts, obstructs discussion?  What matters for teaching and learning, or in learning communities?
  34. Relations: Content, Direction & Strength  Content. Physical, emotional, or informational  Chat - gossip, ‘social grooming’  Advice  Instruction  Collaboration - work, learning, play  Social support –major or minor emotional support  Trust  Services  Small to large: babysitting, lending money, cleaning up after disasters, helping neighbors  Direction of resource flow between actors  Giving or Receiving  Strength of the relation  How much, how often, and how important  Intimacy, Frequency, Intensity, Quantity, Regularity, Longevity, Value  Defined both objectively and subjectively  Minor versus major social support  Daily, weekly, monthly communication
  35. Learning Relations  Learning  Know-what: facts from teachers, books, etc.  Know-how: apprenticeships, informal learning  Fiction: contagious diffusion of gossip and rumour  Group: practices, who knows what (transactive memory), who knows who knows what  Education  Teaching, learning  Evaluation: giving/handing in assignments, giving/ receiving grades  Delivery of information: giving/attending lectures,assigning/reading materials  Community  Social support for learning, technology use  Teaching by experts, learning by novices  Learning community practices: culture, society, behavior, etc.
  36. Analyzing the Relational Mix  Asking relational questions to address learning relationships and structures  Who talks to whom, about what? And via which media?  What relations are maintained by actors who report a learning tie?  How does a learning tie differ from a work, social or collaborative tie? TYPE OF INTERACTION Group Members: 1 2 3 ... 20 How often have you received instructions (i.e., exact directions on what work to do) from this person? in unscheduled face-to-face meetings in scheduled face-to-face meetings by telephone by fax by electronic mail by videoconferencing How often: D for daily W for Weekly M for Monthly Y for Yearly 0 for never For in between amounts use e.g., 2D for twice a day, 6Y for six times a year
  37. Analyzing the Relational Mix (3 examples)  1. Co-located Computer Science Department  25 respondents (of 35 member group) answered 24 questions about a variety of their work and social interactions with 10-20 others within the group  Asked about relations and type of work and friendship tie  Factor analysis revealed six dimensions of work and social interaction reflecting  Work practices : Receiving work (engaged in by 57% of pairs); Giving work (57%)  Major work products : Collaborative Writing (32%); Computer Programming (56%)  Social support relations : Sociability (86%); Major Emotional Support (7%)
  38. Analyzing the Relational Mix  2. Interdisciplinary Research Teams  3 teams: science, social science, education; qualitative and semi-structured interviews;transcripts coded for learning exchanges  Who do you learn from or receive help in understanding something from? (and Who learns from you)  Nine categories of learning  Major: Factual (Field) knowledge; Process (how to) knowledge; Method; Joint research  Minor: Technology knowledge , Socialization; Generation of new ideas, Networking, Administration [very minor] Data = Number of pairs maintaining each type of relation
  39. Analyzing the Relational Mix  3. Science Teachers (54)  What did you learn from the 5-8 others with whom you communicate most frequently about your area of science and science teaching  Five codes derived from content analysis of questionnaire responses  Science teaching techniques  Science content  Class and behavior management  Matters external to their school Distribution of ‘learn from’ relations Relation 256 100% Teaching techniques (T) 173 68 Science Content (C) 72 28 Classroom Management (M) 32 13 External Matters (E) 27 11 Administrative functions (A) 17 7 None 9 4  School and administrative function
  40. Relations define Ties  From Weak to Strong show increases in:  Number and types of interaction  Intimacy and reciprocity  Attention and commitment to the relationship  Frequency of interaction  Number of means of communication used  Motivation to share information and resources
  41. Strong and Weak Ties Strong Ties …  Maintain more relations  Have more frequent interaction  Include intimacy and self-disclosure  Use more media  Have higher reciprocity in exchanges Source of • Freely given resources • Feel obligation to share  Questions • How do you build strong learning ties, online and through computer media? • How do you motivate sharing in crowd-and community-based initiatives? • How do you build learning communities?
  42. Strong and Weak Ties Weak Ties …  Engage in fewer, less intimate exchanges  Have more instrumental exchanges  Share fewer types of information and support  Use fewer media Source of… • New information, new resources • Have little or no obligation to share  Questions • How do you bring peripheral actors into the learning community? • What is the right mix of tie strength to sustain innovation and commitment?
  43. Networks: Structure  Cohesion  Density: # actual ties to possible ties  Centralization: extent organized around a central core:  Cliques, clusters, components  Reach  Can every network member be reached by some path  Path length to get information around the network In-class communication networks: •Chat •Discussion board •Email
  44. Networks of Networks  Knowledge transfer from “community-embedded learning” (Kazmer, 2007)  Local community  classmates and online learning community  Course knowledge   learner’s workplace   learner’s home community  One community  another  through contact in the e-learning community  One institution of higher learning  another  through contact in the e-learning community Teacher networks across schools (top: EnLiST project; bottom: De Laat, 2010)
  45. Learning Ties 45 EXERCI SE - - - DEF INING LEARNING TIES - - - - - - READING NETWORK S TRUCTURES - - -
  46. Learning Scenarios 46 You started a learning initiative with the aim of creating connections among group members so the community will become self-sustaining. You want to see if the effort has worked. What will you look for in connection between actors to show connectivity outcomes? • Each group choose one of the scenarios from the options below • define a (realistic) outcome you want** • determine a definition of a tie that matters to this outcome • determine what (one or more relations) you will ask about (OR analyze transcripts for) as evidence of this interactional learning outcome Small online class (15-25) Workplace with strong norms and procedures Big online class of 1000s (e.g., a MOOC) Open learning community Examples of outcomes: Common knowledge, New knowledge, Innovative thinking, Group cohesion, Shared resources, Cooperation, Collaboration, Collaborative learning, Shared practice
  47. What Constitutes a Learning Tie? Which interaction, for what outcome?  Fact/ know-what. Received from teachers, texts  Fiction. Contagious diffusion of gossip and rumour  Know-how. Apprenticeships, observation, non-formal learning  Group processes. Norms and practices  Informal learning  Group knowledge. Who knows what; Who knows who knows what Let’s add to this list and ideas  What level of attention?  Individual, dyadic, small group, institution, community, society  Education Relations  Teaching, learning  Reviewing, evaluating  Collaborative learning  Community/Societal Relations  Social support for learning, technology use  Learning community processes and practices  Societal distributions of resources, access and knowledge  Did we consider re tie behavior the role of:  Trust  History of actors, of the network  Future expectations of association
  48. Reading Networks 48 Clockwise: online class; 2 x workplaces; xMOOC; open learning community
  49. Further Reading 49  Haythornthwaite, C. & Andrews, R. (2011). E-learning Theory and Practice. London: SAGE.  Haythornthwaite, C. & De Laat, M. (2011). Social network informed design for learning with educational technology. In A.D. Olofsson & J. O. Lindberg, (Eds.). Informed Design of Educational Technologies in Higher Education: Enhanced Learning and Teaching (pp. 352-374). IGI Global.  Andrews, R. & Haythornthwaite, C. (2007). Introduction to e-learning research. In R. Andrews & C. Haythornthwaite (Eds.), Handbook of E-Learning Research (pp. 1-52). London: Sage.  Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research. 2013;15(10):e248.  See also: