DCLA14_Haythornthwaite_Absar_Paulin
Upcoming SlideShare
Loading in...5
×
 

DCLA14_Haythornthwaite_Absar_Paulin

on

  • 1,127 views

DCLA14: 2nd International Workshop on Discourse-Centric Learning Analytics at LAK14: http://dcla14.wordpress.com

DCLA14: 2nd International Workshop on Discourse-Centric Learning Analytics at LAK14: http://dcla14.wordpress.com

Statistics

Views

Total Views
1,127
Views on SlideShare
1,048
Embed Views
79

Actions

Likes
0
Downloads
3
Comments
0

2 Embeds 79

http://dcla14.wordpress.com 78
https://dcla14.wordpress.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

DCLA14_Haythornthwaite_Absar_Paulin DCLA14_Haythornthwaite_Absar_Paulin Presentation Transcript

  • Words, Learning and Networks Caroline Haythornthwaite Rafa Absar Drew Paulin The iSchool @ UBC University of British Columbia Discourse Analytics Workshop LAK 14, 2014
  • Social Media and Learning —  Social Science and Humanities Research Grant (SSHRC) ◦  PIs Anatoliy Gruzd & Caroline Haythornthwaite, with George Siemens ++ Drew Paulin, Rafa Absar, Mick Huggett —  Primary purpose: ◦  To determine and evaluate measures that help educators manage their use of social media for teaching and learning through the use of automated analysis of social media texts and networks —  Examine facets such as ◦  common patterns of exchange ◦  development of shared language and understanding ◦  emergence of roles and positions —  Primary approach ◦  Automated analysis of social media texts and networks ◦  Who talks to whom about what via and which (social) media? —  Research goal is to discover ◦  What forms of social connection – conversational structures of communication between people in a network – reveal learning, learning practices, learning roles, etc.
  • Social Networks Social network building blocks: Actors (nodes) Relations (lines) Network (graph)
  • Networks & Discourse —  Discourse / Conversation / Communication ◦  Entails using language, often in a symbolic and prescribed way, that signals relations between objects, subjects, etc. – i.e., a network! —  Social Networks ◦  Describe relations between actors that signal social constructions such as cliques, groups, communities –  Discourse communities, epistemic communities, learning communities —  Relations can be determined from text ◦  The question for use is ‘What text signifies learning relations?’
  • Learning and Networks —  Psychological basis of networks –  Safety: leading to affiliation, group belonging -- embeddedness – strong ties –  ‘Effectance’: drive for autonomy, exploration, individuation – arm’s length – weak ties (Kadushin; Uzzi; Granovetter) —  Network outcomes ◦  Community –  Reduced individual social load (Burt); generalized reciprocity; border/gate- keeping Ø  Learning communities/Communities of Inquiry; Knowledge/ Epistemic communities ◦  Social Capital –  Resources held in the network –  Knowledge, expertise, physical and social support, companionship, trust, reserve resources (Lin;Wellman) —  Social structure affects outcomes –  Flow, quality and reach of information; Reward and punishment;Trust that others will do the ‘right thing’ (Granovetter)
  • Learning Networks —  Learning as acquisition of knowledge, marked by a transformation of the individual –  Information, knowledge, and learning networks —  Social Learning as learning with, by and through networks –  Accomplished through transfer of information, knowledge dissemination, discussio - trying out ideas on others —  Transformation evident as adoption or development of common practices –  Cultural, disciplinary, group language, discourse, genres, modes of communication, methodological approaches –  (Miller, 1984; DeSanctis & Poole, 1994; Haythornthwaite, 2006, 2013)
  • Learning from a Network Perspective —  Learning can be a relation that connects people —  Learning can be the characterization of the tie ◦  based on multiple, contextually determined relations —  Learning relations can be taken as input for design ◦  e.g., when addressing differences between online and offline learning —  Learning can be a characterization of the outcome of relations ◦  e.g., when a group becomes a learning community —  Learning as the network outcome of relations ◦  e.g., the social or learning capital of the network —  Learning as contact with ambient influence ◦  e.g., informal and ubiquitous learning
  • Who learns what from whom Learning Networks Words —  What exchanges support a learning tie? —  What relations and ties support a learning community? —  What can we ‘see’ in the texts of learners? ◦  Online conversations, but also essay/ exam texts; images; videos; multimodal texts ◦  Across media: discussion, blogs, twitter —  Social networks of ◦  Learning groups: Actors in a learning community ◦  Knowledge base:Topics in a knowledge domain ◦  Bibliometric base: Stars in the citation universe
  • Words, Learning and Networks —  Using text analysis to identify the building blocks of networks ◦  Actors/nodes, relations, ties —  Single mode ◦  Using text analysis to discover actors and relations —  Two mode ◦  Actors x Text ‘events’ à ‘actor x actor’ AND ‘text x text’ networks
  • Use text analysis to distinguish: Actors in the network ◦  Who is in the network Actor relations ◦  What text(s) tie actors in the network? ◦  What relations do these identify? ◦  2-mode:What actors are tied because of common text use? Actor ties ◦  Who talks to whom about what? ◦  Who is tied to whom by the identified relation(s) ◦  What constitutes weak to strong tie configurations for these actors (frequency, intimacy of relational /text content) Social networks ◦  What configurations of actors tied by text defines the network? Text in the network ◦  What topics/phrases/keywords are present/prevalent in the network Text relations ◦  What text should be tied to other text? ◦  2-mode:What text is tied because of common use by actors? Text ties ◦  How is text tied to other text? Social networks ◦  What configurations of text define the network? ◦  2-mode:What configurations of text tied by actors defines the network *** This is the work in progress considering what the text side means ***
  • Outcomes Actor-Text networks Collaboration —  What information sharing is or should (according to theory, pedagogical intent) be observed? Innovation —  What external information is or should brought to the network? Autonomy —  What independent thought is or should be evidenced in the network? SN concepts —  Weak vs strong ties —  Roles and positions —  Social capital Text Argumentation —  What co-location/configuration of text is or should (according to theory, pedagogical intent) be observed? Transformation —  What change in language/concept use is or should be evident? Emotion —  What emotion is or should be evident? Learning and literacy concepts —  Collaborative learning,Transformative learning —  Common language, Discourse communities —  Engagement (emotion) —  Enculturation, learning ‘to be’ an expert, a member of a group, a social media user, etc.
  • Three Studies (briefly – as time permits) —  Relational discovery ◦  Qualitative analysis to determine what constituted a ‘learning tie’ —  Node discovery ◦  Enhancing identification of network actors through text analysis —  Tie discovery ◦  Identifying network connections through common use of text
  • #1 Relational Discovery 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 Science, social science, and education teams Data = Number of pairs maintaining each type of relation Haythornthwaite, C. (2006). Learning and knowledge exchanges in interdisciplinary collaborations. Journal of the American Society for Information Science andTechnology, 57(8), 1079-1092. Name 5-8 others with whom you work most closely on the project. “What did you learn from {each of these others}?” Qualitative analysis of answers.
  • #2 Node and tie discovery Previous post is by Gabriel, Sam replies: ‘Nick,Ann, Gina, Gabriel: I apologize for not backing this up with a good source, but I know from reading about this topic that libraries…’   Previous posts by Gabriel, Sam, Gina, and Eva, then: ‘Gina, I owe you a cookie.This is exactly what I wanted to know. I was already planning on taking 302 next semester, and now I have something to look forward to!’   Post by Fred: ‘I wonder if that could be why other libraries around the world have resisted changing – it's too much work, and as Dan pointed out, too expensive.’   Ex.1   Ex.2   Ex.3   Gruzd,A. & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International Sunbelt Social Network conference, Jan. 22-27, St. Pete’s Beach, Florida. [http://hdl.handle.net/2142/11528] Issues: Actor identification Name resolution
  • Add Tie Weights: Distinguish important text Example Keep in mind that google and other search technology are still evolving and getting better. I certainly don't believe that they will be as effective as a library in 2-5 years, but if they improve significantly, it will continue to be difficult for the public to perceive the difference.   From To O r i g i n a l weight With IE A B 1 0.5 A C 2 1.6 A D 2 2.1 A E 3 2.5 A F 1 0 Using Yahoo!Term Extractor, a sample message below returns three concepts:“google”, “search technology” and “library”. The amount of information it transmits can be estimated as ; where 49 is the total number of words in the message.   06.0 49 3 = Example of how an overall informaton content weighting procedure influenced tie strengths in an ego network for a student A     Due to the absence of important or descriptive concepts in the communication between A and F, the link between them can be ignored.     i.e.,  remove  the  «I  agree»  messages.
  • #3: Conversation, Collaboration, Interaction —  Conversation ◦  Considered essential for learning ◦  Exploring text records for evidence of interactivity, social network dynamics, and conversation levels —  Sample ◦  8 iterations of the same course ◦  2 per semester Fall 2001 to 2004 ◦  Using message header information •  Aim •  Use simplest most widely accessible form of data •  Determined tie based on position in conversational sequence with a posting with the same subject line •  {nb. many caveats re the subject line use} •  Discover interactivity patterns through text association Haythornthwaite, C. & Gruzd,A. (Jan. 2012). Exploring patterns and configurations in networked learning texts. Proceedings of the 45th Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE.
  • Strength of Post : Response Pairings • Most pairs are connected by only one immediately following posting (57-73%) • 17-24% on two subsequent postings; 6-11% on 3; 2-5% on 4; 0-5% on more than 4 iterations NB. excludes consideration of multi-way interaction e.g.A<- B, C<-B,A<-C 0 100 200 300 400 500 2001A 2001B 2002A 2002B 2003A 2003B 2004A 2004B 1 2 3 4 >4
  • Network Structures Dichotomized at 1, 2, 3 and 4 Ties [density, undirected] .56 .20 .07 .02 Conversational ‘turns’ Revealing a ‘core discussion’ group
  • Network Comparisons Post : Response tie configurations across 4 different classes 2001A (.35) 2002A (.32) 2004A (.38) 2003A (.14) Revealing different class structure configurations
  • Words, Learning and Networks —  Who is in the network – actors ◦  Text analysis for name identification, separation of named entities from named actors —  What is exchanged – relations ◦  Text analysis for identification of relations, key discussions, pivotal text or topics that connect conversations and thus the network —  Who is talking to whom – ties ◦  Discover how conversations happen across the the network. —  Who maintains what relations with whom ◦  What combinations of topics/texts/ keywords, etc. create what kinds of ties between people: work, social, support; instrumental, emotional —  From text to network structures ◦  Assess what leads to, confers, or sustains network positions such as network stars and brokers, weak and strong ties ◦  Identify structural holes, topic lacunae and avoidance ◦  Compare networks for similarities across structure, and conversational text —  From networks to text ◦  Use network ties to inform the analysis of text, e.g., where close ties use a variety of terms that appear to represent the same object or topic.
  • Wordle from subject lines from one class
  • MOOC data overview and challenges Rafa Absar
  • MOOC data —  Courses ◦  CCK11: Connectivism and Connective Knowledge ◦  Change11: Change 2011 ◦  PLENK10: Personal Learning Environments Networks and Knowledge —  Not restricted to any one platform ◦  “Through out this ‘course’ participants will use a variety of technologies, for example, blogs, Second Life, RSS Readers, UStream, etc.”
  • Structure of the MOOC data Daily Newsletters Blog posts Comments Discussion threads Comments Twitter posts Retweets
  • Overview of data CCK11 Change11 PLENK10 Blogs 812 2486 719 Discussion Threads 68 87 Comments 306 134 Tweets 1722 5665 2121
  • Twitter network
  • 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?
  • Social relations and learning   29 “… it  made  me  think   of  [an example] that   Karen  posted.. ”   Learn “ Anne  and  I  have  been   corresponding  via  e-­‐mail  and   she  reminded  me  that  we   should  be  having  discussion   here.." “ [Instructor’s name], if you see this posting would you please clarify for us..” Collaborative Work Help
  • Social constructivist learning theories! Zone of Proximal Development (ZPD)! From: Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education, 10(1), 15–25" (More Knowledgeable Other)
  • Who are the More Knowledgeable Others in a learning community?! External indicators! •  Previous roles of leadership or expertise in a knowledge community" •  History of publications and presentations" •  Bibliometric measures (citations)" Internal indicators! •  Contributions to the discussion; evidence of knowledge and expertise" •  Mentions, references by others, quotes, retweets, etc." •  Productive roles (brokers, question-askers, critical thinkers)"
  • Why do we want to know who are the MKO?! Practical: ! •  Organize optimal ZPD for learning sub-groups" Analysis:! •  How do MKO contributions disseminate/resonate/ diffuse through the network?
 " •  Is there a correlation between ‘expertise’and network centrality measures?!
  • Do you use social media in your courses?! ! Please participate in our online survey:" (You could win 1 of 3 iPad Minis!)" http://tinyurl.com/SMlearningsurvey"