LASI 2014 Workshop on Learning Analytics for the Social Media Age

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Learning Analytics Summer Institutes (LASI 2014)
Harvard University, Cambridge, USA
June 30, 2014

Anatoliy Gruzd @gruzd
Caroline Haythornthwaite @hthwaite
Rafa Absar @rafaabsar
Drew Paulin @drewpaulin

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LASI 2014 Workshop on Learning Analytics for the Social Media Age

  1. 1. Anatoliy Gruzd @gruzd Caroline Haythornthwaite @hthwaite Rafa Absar @rafaabsar Drew Paulin @drewpaulin Workshop on Learning Analytics for the Social Media Age Learning Analytics Summer Institutes (LASI 2014) Harvard University, Cambridge, USA June 30, 2014
  2. 2. Outline ¡ Introduction ¡ Social Media Data preparation ¡ Analysis of Social Media Data ¡  Part 1: Text Analysis & Visualization ¡  Part 2: Social Network Analysis & Visualization
  3. 3. Introduction ¡ Our team today: ¡  Anatoliy Gruzd ¡  Caroline Haythornthwaite ¡  Rafa Absar ¡  Drew Paulin ¡  Project on Learning Analytics in the Social Media Age (#pLASMA) ¡  To determine and evaluate measures that can help educators manage their use of social media for teaching and learning through the use of automated analysis of social media texts and networks pLASMA is supported by funding from the Social Science and Humanities Research Council and from the GRAND National Centre of Excellence.
  4. 4. Relational Perspectives on Learning and Media Use ¡  Today’s workshop focuses on two exercises, both taking a relational – or social network – perspective on learning and media use ¡  Relations ¡  What do people do with each other, communicate about, work on together ¡  What do people do when they learn from others, teach others, learn together ¡  What constitutes a ‘learning tie’? ¡  Networks ¡  Given a learning interaction, what networks on ties emerge? ¡  What do these structures tell us about learning? ¡  Who learns from whom, how information spreads, how knowledge is co-constructed, who has access to what resources
  5. 5. Part I: Defining ties ¡  Actors who are tied in networks maintain one or more relations ¡  When we ask people what they learn from others – (or what others learn from them) – what do they say? Do they do only one thing or more? ¡  Science teachers ¡  What did you learn from the 5-8 others with whom you communicate most frequently? ¡  Most often: learning techniques for teaching science in the classroom ¡  Multiple relations: most often teaching techniques plus science content Science Teachers 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* Content analysis
  6. 6. Who learns what from others in interdisciplinary teams? 0 10 20 30 40 50 60 70 Fact/Field Process M ethod R esearch Technology G enerate Socialization N etw orkingA dm inistration Types of Learning: Received Interdisciplinary Teams: science and social science Also found relational difference x role in the team: -- fact/field among PIs -- method between methodologists Content analysis
  7. 7. 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 … Weak Ties ¡  Engage in fewer, less intimate exchanges ¡  Have more instrumental exchanges ¡  Share fewer types of information and support ¡  Use fewer media Source of • Freely given resources • Feel obligation to share Orientation in a collective context • to both the purpose of the group and to the workings of the group or community Source of… • New information, new resources • Have little or no obligation to share Orientation in a collective context • to topic/purpose of the group more than to the workings of the group
  8. 8. Part II: Relations describe Networks Networks show ¡  density ¡  actor centrality ¡  centralization ¡  cliques ¡  stars ¡  brokers ¡  isolates ¡  cliques ¡  structural holes ¡  path lengths Network outcomes ¡  Resource flow ¡  inclusion and exclusion ¡  early and late access to information ¡  Roles ¡  stars, gatekeepers, entrepreneurs, brokers, translators ¡  information suppliers, help givers, social support givers ¡  Social structures ¡  Social capital, resilience Entrepreneurial Leadership in STEM : http:// enlist.illinois.edu/ Connections across schools built by learning relationships: I learn from / they learn from me about science teaching
  9. 9. Outline ¡ Introduction ¡ Social Media Data preparation ¡ Analysis of Social Media Data ¡  Part 1: Text Analysis & Visualization ¡  Part 2: Social Network Analysis & Visualization
  10. 10. #pLASMA 10 Working with Social Media Data #pLASMA MOOCs Data ¡ Athabasca University Courses ¡  CCK11: Connectivism and Connective Knowledge ¡  Change11: Change 2011 ¡  PLENK10: Personal Learning Environments Networks and Knowledge ¡ Not restricted to any one platform ¡  “Throughout this ‘course’ participants will use a variety of technologies, for example, blogs, Second Life, RSS Readers, UStream, etc.”
  11. 11. #pLASMA 11 #pLASMA MOOCs Data: Sample course page
  12. 12. #pLASMA 12#pLASMA MOOCs Data: Data Structure Daily Newsletters Blog posts Comments Discussion threads Twitter posts Retweets
  13. 13. #pLASMA 13#pLASMA MOOCs Data: Overview CCK11 Change11 PLENK10 Blogs 812 2486 719 Discussion Threads 68 87 Comments 306 134 Tweets 1722 5665 2121
  14. 14. #pLASMA 14 #pLASMA MOOCs Data: Average size of messages for CCK11 CCK11 Avg character count Avg word count Blogs 332.3 54.2 Discussion Threads 541.8 90.8 Comments 837.6 138.9 Tweets 113.9 14.4
  15. 15. #pLASMA 15#pLASMA MOOCs Data: Challenges ¡ Data-related Issues ¡  Some posts contain mostly images/videos or are live seminars ¡  Unreachable links, e.g. expired blog links ¡  Some domains have disappeared or now require a login password ¡  Participant comments are partly on the MOOC site, partly on blog pages hosted elsewhere
  16. 16. #pLASMA 16#pLASMA MOOCs Data: Challenges ¡ Participant-related issues ¡  Participants located all over the world ¡  Some posts are not in English (e.g. Spanish, Swedish) ¡  Participants sometimes miss live sessions that happen at “bad” local times (e.g. 4 am)
  17. 17. Issues: Identity resolution ¡  Coreference resolution ¡  How to identify single identities across platforms? ¡  Alias resolution ¡  How to identify two or more people with the same alias?
  18. 18. Outline ¡ Introduction ¡ Social Media Data preparation ¡ Analysis of Social Media Data ¡  Part 1: Text Analysis & Visualization ¡  Part 2: Social Network Analysis & Visualization
  19. 19. Social  Big  Data    -­‐>    Visualiza2ons    -­‐>    Understanding   How to Make Sense of Social Big Data?
  20. 20. How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  21. 21. Can we automate the process of creating an “effective representation” of the texts produced by online communities? A “representation” is an excerpt [of the original text] that describes or may stand in for the original text. An “effective” representation can help us to answer: ¡  Can we discover what the community interests and priorities are? ¡  Can we discover patterns of language and interaction that characterize a community? How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  22. 22. 1.  Most frequently used words 2.  Important Topics Over Time 3.  Community Style 4.  Community Support Haythornthwaite,  C.  &  Gruzd,  A.  (2007).  A  Noun  Phrase  Analysis  Tool  for  Mining  Online   Community.  Proceedings  of  the  3rd  Communi3es  and  Technologies  Conference,  Michigan   State  University.  Springer,  pp.  67-­‐86.  DOI:  10.1007/978-­‐1-­‐84628-­‐905-­‐7_4   How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  23. 23. 1. Most frequently used words (Example) ¡ Profession-related words ¡  book/s, information, library/libraries, librarian/s ¡  user/s, and patron/s, people ¡  database/s, search, document/s ¡ Learning-related words ¡  question/s, article/s, example/s, way, study, class, course, research, journal, reading, method, problem, hard time How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  24. 24. 2. Important Topics Over Time (Example) % of messages containing "Database(s)" and "Google" 0 5 10 15 20 25 2001 2002 2003 2004 School year %ofmsgs database(s) google How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  25. 25. 3. Community Style (Example) % of messages containing Don’t Think, Don’t Know, Don’t Have 0 5 10 15 20 2001 2002 2003 2004 School Year %ofmsgs How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  26. 26. 4. Community Support (Example) % of messages agreeing or disagreeing or containing ‘Thanks’ 0 2 4 6 8 10 12 14 16 18 20 2001 2002 2003 2004 S c hool  Y e a r Agree Disagree Thanks How to Make Sense of Social Big Data? PART 1: Automated Text Analysis & Visualization
  27. 27. Practical Part 1 with Netlytic.org ¡  Using Netlytic to help code text into categories that identify particular learning or conversational themes ¡  Data from MOOC online discussion Netlytic.org - a cloud-based analytic tool for automated text analysis & discovery of social networks from online communication Follow steps at http://bit.ly/lasi14plasma
  28. 28. Outline ¡ Introduction ¡ Social Media Data preparation ¡ Analysis of Social Media Data ¡  Part 1: Text Analysis & Visualization ¡  Part 2: Social Network Analysis & Visualization
  29. 29. How to Make Sense of Social Big Data? PART 2: Social Network Analysis & Visualization
  30. 30. Example: Twitter Network @John @Peter @Paul • Nodes = People • Ties = “Who retweeted/ replied/mentioned whom” (“Name network”) • Tie strength = The number of retweets, replies or mentions How to Make Sense of Social Media Data?
  31. 31. AnatoliyTwitter: @dalprof 2012  Olympics  in  London   Example 1
  32. 32. AnatoliyTwitter: @dalprof #tarsand  Twi7er  Community   Example 2
  33. 33. AnatoliyTwitter: @dalprof #1b1t  Twi7er  Book  Club   Example 3
  34. 34. #pLASMA 34Social Media, Social Network Analysis and Learning ¡ Can Twitter facilitate learning? ¡ How might Twitter facilitate learning? ¡  Looked at Twitter network around #LAK14. ¡ Interested in experts and leaders in the field ¡ Can their positions in the network facilitate learning for others? (Gilbert & Paulin, 2014)
  35. 35. #pLASMA 35How can learning happen in Twitter? ¡ Social Learning (Bandura, 1977; 1986). Observing others’ behaviour, adopting/adapting. ¡ Modelling (Haythornthwaite, Kazmer, Robins and Shoemaker, 2000). New learners look to more experienced learners to understand how to ‘be’ online in a learning context. ¡ Zone of Proximal Development (Vygotsky, 1978). Interacting with those who are more knowledgeable. ¡  Connectivism (Siemens, 2005). Access to good information sources, focus on connecting information together towards sensemaking.
  36. 36. #pLASMA 36 Twitter provides learners access to and interaction opportunities with experts. Twitter offers potential for experts to serve as: •  hubs of information exchange. •  observable models for learning behaviours and sensemaking. Who is learning from whom?
  37. 37. 37 Centrality: structural importance of a node in the network. Betweenness: how often a node falls along the shortest path (geodesic path) between 2 other nodes. Eigenvector: How well-connected/central a node is, weighted by connectedness of adjacent nodes. Degree: The number of ties a node has. In-degree – prestige; out-degree – influence. Positions in the network
  38. 38. 38 Eigenvector betweenness – #LAK14 Retweet network (Gilbert & Paulin, 2014)
  39. 39. 39 Betweenness centrality – #LAK14 Retweet network (Gilbert & Paulin, 2014)
  40. 40. 40 Who mentions whom – #LAK14 mention network (Gilbert & Paulin, 2014)
  41. 41. #pLASMA 41Experts’ positions in the network Findings: •  Experts are more likely to have high centrality in a network than non-experts. •  Experts are re-tweeted significantly more than non- experts. •  Experts were mentioned ~40 times more (average) than non-experts.
  42. 42. #pLASMA 42Experts’ positions in the network Implications: Highly central roles allow experts to make impactful contributions to learners in the community: •  Hubs of information exchange. •  Provide sources of quality information for learners. •  Facilitate connection-making, both people-to-people and in terms of connecting knowledge. •  High visibility provides observable modelling for learners.
  43. 43. Identifying Social Relations in Learning Networks Learning ● Collaborative Work ● Help Gruzd,  A.  (2009).  Studying  Collabora2ve  Learning  Using   Name  Networks.  Journal  of  Educa3on  for  Library  and   Informa3on  Science  50(4):  243-­‐253.  
  44. 44. Identifying Social Relations in Learning Networks 44 Learning ● Collaborative Work ● Help –  Postings that show attention to subject matter discussed by someone else “… it made me think of the faceted catalogs' display that Karen posted ”
  45. 45. Identifying Social Relations in Learning Networks 45 Learning ● Collaborative Work ● Help –  Organizing group work, taking a leadership role “ Some quick poking around shows that Steve and myself are here in Champaign, [...] and Nicole is in Chicago. [...] does anyone have a strong desire to be our contact person to the administrators ”
  46. 46. Identifying Social Relations in Learning Networks 46 Learning ● Collaborative Work ● Help –  A reference to an event or interaction that happened outside the bulleting board “ Anne and I have been corresponding via e-mail and she reminded me that we should be having discussion here "
  47. 47. Identifying Social Relations in Learning Networks 47 Learning ● Collaborative Work ● Help –  Postings that ask others for help “ [Instructor’s name] if you see this posting would you please clarify for us ”
  48. 48. Iden?fying  Roles  in  Learning  Networks           •  Iden2fy  students  who  might  need  extra  a_en2on/help  from  the  instructor   •  Discover  if  lectures  or  other  class  materials  were  unclear   •  Iden2fy  peer-­‐help   •  Find  ac2ve  group  members  who  oaen  take  a  leadership  role  in  a  group   48   Student   Instructor   Student   Group     Leader    Student   Student  
  49. 49. Practical Part 2 with Netlytic.org Follow steps at http://bit.ly/lasi14plasma ¡  Using Netlytic to display networks of interaction from a MOOC online discussion dataset
  50. 50. #pLASMA Social Media and Learning Survey Please participate in our online survey (You could win 1 of 3 iPad Minis!) http://tinyurl.com/SMLearningSurvey

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