![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](https://image.slidesharecdn.com/haythornthwaiteutsworkshopaug202014-140924211102-phpapp01/85/CIC-Networked-Learning-Practices-Workshop-Caroline-Haythornthwaite-38-320.jpg)
CIC Networked Learning Practices Workshop - Caroline Haythornthwaite
- 1. Networked Learning Practices CAROLINE HAYTHORNTHWAITE WORKSHOP UTS SYDNEY AUG 20, 2014
- 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
- 5. Part I: Trends SOCIAL AND TECHNOLOGICAL NETWORK EFFECTS ON INDIVIDUALS AND SOCIETY
- 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) http://www.pewinternet.org/Reports/2011/College-students-and-technology/Report.aspx
- 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. https://tekri.athabascau.ca/analyti 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)
- 15. II. SOCIAL NETWORKS, LEARNING NETWORKS
- 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) http://www.cmu.edu/joss/content/articles/volume8/Welser/
- 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 http://www.plosone.org/article/info: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 http://socialmedialab.ca/
- 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
- 31. Networks Structures 31 ACTORS / NODES RELAT IONS / EDGES T IES NETWORKS
- 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. http://www.jmir.org/2013/10/e248/ See also: http://haythorn.wordpress.com/