Results from 8 iterations of GSLIS classes LIS 380/501
Analisando textos de E-learning Caroline Haythornthwaite Anatoliy Gruzd Portuguese translations by Professora Gilda Olinto Graduate School of Library and Information Science University of Illinois at Urbana-Champaign Lectures at IBICT, June 2009 These powerpoint slides accompanied one of series of lectures given in June 2009 at IBICT, Instituto Brasileiro de Informao em Cincia e Tecnologia (The Brazilian Institute for Information in Science and Technology), Rio de Janeiro, where Professor Caroline Haythornthwaite was a guest of the institute. Thanks go to Celia Ribeiro Zaher, Coordenadora de Ensino e Pesquisa, C&T da Informao, IBICT for arranging this visit. Professora Gilda Olinto for working with me on this series and translating my slides into Portuguese Lectures included: An overview of e-learning; Computermediated communication (CMC) and e-learning; Social informatics (SI) and e-learning; E-learning networks; Theories and ideas emerging for e-learning; Networked learning Analisando textos de
E-learning Provide operational platform for visualization of interaction and group dynamics in online conversations Recapture the visibility of the group in online communication Augment linear text-based representation with spatial representation For feedback for participants, and overviews for instructors Provide analytical platform for comparison across cases Define concepts and metrics useful for analysis of group or community interaction Identify interactional relations and patterns that are key for successful online learning practice and experience The Stimulus March 2008 Archives by thread . Messages sorted by: [ subject ] [ author ] [ date ] . More info on this list... Starting: Sat Mar 1 13:26:33 PST 2008_Ending: Sun Mar 23 09:32:23 PDT 2008_Messages: 205 . [Air-L] Final goodbye for early web icon Dominic Pinto . [Air-L] Reminder - Gogimon Search Agent Beta Tesers David Miller . [Air-L] Meeting in Illinois, May 08 - "decolonized methodologies" Denise N.
Rall . [Air-L] Open Source and changing mode of productions in the third world Denise N. Rall . [Air-L] Companion to Digital Humanities Barry Wellman . [Air-L] Companion to Digital Humanities Jankowski . [Air-L] Origins of E-Commerce Alex -Vipowernet . [Air-L] Lessons in Second LIfe jeremy hunsinger . [Air-L] Invitation to 6th Annual Workshop on Open and User Innovation - HBS & MIT - August 4-6, 2008 Karim R. Lakhani . [Air-L] Facing up to Facebook - Michael Geist at Osgoode March 5 (Livecast available!) Giuseppina D'Agostino/osgoode . [Air-L] Instrument help: eveluate user's perception of online community Ke, Nan . [Air-L] CFP - DIAC Demos, Workshops, and Exploratory Papers Tom Erickson . [Air-L] call for papers for a special issue on consumption and Web 2.0 davidgbeer at aol.com . [Air-L] TVO The Agenda tonight Nancy Baym . [Air-L] Resources on On-Line Dating and SMS Language Andrew Herman
. [Air-L] Resources on On-Line Dating and SMS Language Gordon Carlson . [Air-L] Postdoc in new media (Germany/Switzerland) Elad Segev . [Air-L] Online research ethics Alecea Standlee . [Air-L] Online research ethics Nishant Shah . [Air-L] Online research ethics mhward . [Air-L] Online research ethics joana ro . [Air-L] Online research ethics Charles Ess . [Air-L] Online research ethics - my two and 1/4 cents Radhika Gajjala . [Air-L] Online research ethics - my two and 1/4 cents Lois Ann Scheidt . [Air-L] Online research ethics Jim Porter . [Air-L] Online research ethics Radhika Gajjala . [Air-L] Online research ethics coopman at u.washington.edu . [Air-L] Online research ethics Charles Ess . [Air-L] Online research ethics Jeremy Hunsinger
. [Air-L] Online research ethics Jeremy Hunsinger . [Air-L] Online research ethics Heidelberg, Chris . [Air-L] Online research ethics Derek Hansen . [Air-L] Online research ethics Jeremy Hunsinger . [Air-L] Online research ethics Steve Jones . [Air-L] Online research ethics Lois Ann Scheidt . [Air-L] Online research ethics Radhika Gajjala Rios de textos lineares Estrutura social invisvel Rapidamente gerada Padres de participao invsveis . [Air-L] Online research ethics Jeremy Hunsinger . [Air-L] Online research ethics
Charlie Balch . [Air-L] Online research ethics Lois Ann Scheidt . [Air-L] Online research ethics Jeremy Hunsinger . [Air-L] Online research ethics Steve Jones . [Air-L] Online research ethics Andrew Rojecki . [Air-L] Online research ethics Mark D. Johns . [Air-L] Online research ethics - SL Radhika Gajjala . [Air-L] Online research ethics - SL Lois Ann Scheidt . [Air-L] Online research ethics - SL Radhika Gajjala . [Air-L] avatar research ethics Jeremy Hunsinger . [Air-L] Online research ethics Mark D. Johns . [Air-L] Postdoc in new media (Germany/Switzerland) Geder Parzianello . [Air-L] FW: 'Digital Ontario' A symposium Wednesday, March 5th, 2008 Thursday, March 6th, 2008 Michael Gurstein . [Air-L] CFP: HICSS 42 : Social Networks and Virtual Worlds for Work, Learning, and Play Caroline Haythornthwaite
. [Air-L] Invitation to Participate: Research Related to Internet Governance Nanette Levinson . [Air-L] IP/Gender 4/4/08 burkx006 at umn.edu . [Air-L] Top ten web apps mhward . [Air-L] Online research ethics Jankowski . [Air-L] REMINDER> 15 March deadline for e -Research 08 conference in Oxford Eric T. Meyer . [Air-L] Politics: Web 2.0, Royal Holloway, University of London LAST CHANCE TO REGISTER Chadwick Andrew . [Air-L] Online research ethics Marj Kibby . [Air-L] avatar research ethics Marj Kibby . [Air-L] avatar research ethics Radhika Gajjala . [Air-L] avatar research ethics Steve Jones . [Air-L] avatar research ethics Gordon Carlson . [Air-L] avatar research ethics Radhika Gajjala . [Air-L] avatar research ethics Kristin Lindsley . [Air-L] avatar research ethics
Gordon Carlson . [Air-L] TorontoStar: Facebook : The New Study Hall For The Wired Generation? Perhaps Not [:-( Gerry Mckiernan . [Air-L] TorontoStar: Facebook : The New Study Hall For The Wired Generation? Perhaps Not [:-( Greg Elmer . [Air-L] TorontoStar: Facebook : The New Study Hall For TheWired Generation? Perhaps Not [:-( Marj Kibby . [Air-L] TorontoStar: Facebook : The New Study Hall For TheWired Generation? Perhaps Not [:-( Dr. Steve Eskow . [Air-L] TorontoStar: Facebook : The New Study Hall For TheWired Generation? Perhaps Not [:-( Peter Timusk . [Air-L] TorontoStar: Facebook : The New Study Hall For TheWired Generation? Perhaps Not [:-( Peter Timusk . [Air-L] Online research ethics dddumitr at ucalgary.ca . [Air-L] Online research ethics Radhika Gajjala The Stimulus Growing volume of texts
contributed by a growing number of participants Increased amount of online text Increased use of online environments Greater need to understand online interaction processes Growth of Usenet 3.12 terrabytes a day (2007) Growth of online learning Almost 3.5 million students (US) were taking at least one online course during the fall 2006 term; (10% increase over 2005) ~20% of all U.S. higher education students were taking at least one online course in the fall of 2006 (Allen & Seaman, 2007) Growth of blog activity 175,000 new blogs a day (2006) Buscando sentido da ao conversasional
Usando abordagem de redes sociais (SNA) Enfatizando a descoberta emprica da base relacional da interao social e o exame da estrutura social Usando processamento de linguagem natural (NLP) Extrair tpicos chave da conversao Combinar SNA & NLP Estrair mais nuances Apresentao de textos mais integrada Prover visualizaes Interatividade, dinmica de grupo, uso da linguagem ICTA Version 1 xxx www.textanalytics.com ICTA version 2 Concept clouds and networks New network construction techniques Opes de construo de redes O foco presente na descoberta da rede
Chain Network (Rede em cadeia) Simple, based on who posted and the order of postings, no examination of text Subject Line Text Chain Networks (Rede de linha de assuntos) Middle complexity, based comparing the text in subject lines, and then using who posts after whom with the same subject line Message Text Name Network (Rede de nomes) More complex, based on analyzing the text of messages for names used in the body of postings Descoberta de Rede Ex.1 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! Ex.3
Ex.2 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. Construindo redes a partir de textos de mensagens Uso de informao de ns e laos que est no texto das mensagens Questes Discovering names and nicknames used in the text Identifying names of people in the class from names of authors being discussed Identifying all the names one person might use (e.g., James, Jim, [email protected], [email protected]) Distinguishing between two or more people with the same name (Jim G. and Jim M.) Identificao do ator Descoberta de nomes pessoais Class lists of names dont always work e.g., if someone uses their middle name which is not on the name list, or they use a short or nickname Mtodo
The 1990 US Census http://www.census.gov/genealogy/names Limitation at present is the emphasis on US names Capitalization Context words Hi, Sammy Good night, Jill Examplo 2: Extraindo os Nomes Egocentric network for Tyler Name Network Chain Network QuickTime and a decompressor are needed to see this picture. kurt -> Kurt Cobain, a lead singer for the rock band Nirvana dewey -> John Dewey, philosopher & educator -> Santa Monica Public Library mark > mark up language Visualization powered by http://www.netvis.org Example 2: Extraindo os Nomes EXAMPLE
From: [email protected] (= Wilma) Reference Chain: [email protected], [email protected] Hi Dustin, Sam and all, I appreciate your posts from this and last week . I keep thinking of poor Charlie who only wanted information on dogs.  Cheers, Wilma. Words to the Left` Hi Name Words to the Right Position Dustin Sam and 0 Yes 0 1 2=2
and all 0.01 Yes 0.01 0.99 2 = 1.98 who only 0.50 No 0.50 0.50 0.88 Yes 0.88 2 2 = 3.52
0.12 Hi Dustin Sam Of poor Charlie Cheers Wilma Context word? FROM TO Algorithm calculates weights for the likelihood that a name is associated with a sender or receiver. Weight under FROM points to Wilma as the poster. Weight under TO indicates Dustin and Sam as receivers. Comparando redes de cadeias e redes de nomes Results from samples of 534 and 853 messages
showed 27% and 38% more social network ties detected with the name algorithm than the chain algorithm In another set of classes, correlations between these networks ranged from 45% to 69% Other work has looked at how the name network is related to perceptions of relationships (as reported by students) For 4 of 6 classes, the name network was consistently more likely to match the self-report network than the chain network Uso de anlise de redes para explorar comportamento no E-learning Social Network Patterns Rhythms Participation Networks Sample used for initial analysis 8 iterations of the same course 2 per semester Fall 2001 to 2004 Note The following examples use only the network formed by the subject line network Estatsticas bsicas sobre a classe
No. of unique msgs 1205 1580 1469 1895 1280 1242 1493 2156 Same course, same instructor each semester Different teaching assistants, adjustments to course content Only public bulletin boards examined Other communication happened during synchronous lecture sessions, chat, email, private small-group bulletin boards Rtmos Existem rtmos nos postings? Weekly trend highly evident Semester -- small start-up and finish, but rhythm maintained at approximately the same kind of pace all semester Rede semanal Viso do semestre
Comportamentos de posting Interatividade What is a good interactivity ratio? What is a good response rate density? We dont know Here are some numbers as a baseline from these 8 classes, and as examples of what kinds of numbers might be worth looking at Interatividade Posting Activity Number of participants (Range: 38 - 58) Total number of postings (Range: 1205 - 2156) Threading (subject line) Number of threads (373 - 1022) Number of posts per thread (max. 19 - 36) Post : Response pairings Direct responding rates (690 - 1144 posts) Direct response network densities (.13 - .42)
Example: Class 2001A 38 participants (33 students + 5) 1205 posts in classwide bulletin boards 373 threads as determined by subject line 1-24 postings per thread 786 post:responses maintained by 499 pairs Atividades de posting: 8 classes 2500 60.00 Total no. posts Avg. no. posts / participant No. of Participants 2000 50.00 40.00
35 38 2001A 2001B 2002A 2002B 2003A 2003B 2004A 2004B Padres de conversao Post-Response Ties A tie is indicated if one participant posts directly after another participant with the same subject line Post from Fred on Shall we dance? is directly followed by a post from Ginger with subject line Shall we dance?
This is a very simple measure of a tie Padres de resposta: 8 classes 2500 2000 900 800 Response Posts Independent Posts Pairs 700 600 1500 500 400 1000 300 200
500 100 0 0 2001A 2001B 2002A 2002B 2003A 2003B 2004A 2004B Caractersticas das redes Network density, directed ties Tie is considered to be present if a pair has at least one post-response sequence Ties are directed: ties from A to
B are counted separately from ties from B to A Density = number of ties / (n x (n-1)) Example: Class 2001A Number of pairs = 499 Number of possible pairs for 38 participants = 38 x 37 Density = 499 / 1406 = .35 Meaning? So far we can only say that they are different Cant say more about success of the class until we check on plans and outcomes Post-Response tie configurations across 4 classes Densities (directed) .35, .32, .14, .38 Densidade da interao 2001A 2001B 2002A 2002B 2003A 2003B 2004A 2004B 38
47 43 50 56 58 35 38 No. students + instructors No. of possible 1406 2162 1806 2450 3080 pairs (n x (n-1)) Number of pairs x number of post : response cases 1 or more 499 601 583 766 442 2 or more 181 211 155 251 156 Density (directed) x number of post : response cases 1 or more .35
.38 .15 .42 .18 Densities of .13 to .42 for at least one post-response. Densities fall off rapidly for indication of second post:response (range .05 to .18). Low densities in particular for 2003A and B. Laos de resposta 500 Most pairs are connected by only one immediately following posting (57-73%) 1 2 3 4 >4 400
17-24% on two subsequent postings; 6-11% on 3; 2-5% on 4; 0-5% on more than 4 iterations 300 200 100 0 2001A 2001B 2002A 2002B 2003A 2003B 2004A
2004B NB. excludes consideration of multi-way interaction e.g. A<-B, C<-B, A<-C Redes: Densidade, clique, fora do lao Class 2002A: 1 to 4 post:response sequences Concluso Objetivos e metas Colher e comparar dados de diversas redes Identificar que tipos de interaes sociais esto relacionadas a experincias bem sucedidas de indivduos e grupos At agora Colhendo dados, criando ferramentas automticas Trabalho a fazer Novas anlises sobre interaes nas classes Anlises de outras comunidades online Novas comparaes entre dados automticos e dados fornecidos pelo indivduo Referncias
Haythornthwaite, C. & Gruzd, A. (June, 2007). A noun phrase analysis tool for mining online community. In C. Steinfield, B.T. Pentland, M. Ackerman & N. Contractor (Eds.). Communities and Technologies 2007: Proceedings of the Third Communities and Technologies Conference, Michigan State University (pp. 67-86). London: Springer. Gruzd, A. & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International Sunbelt Social Network conference, Jan. 22-27, St. Petes Beach, Florida. Haythornthwaite, C. & Gruzd, A. (2008). Analyzing networked learning texts. Paper presented at the Networked Learning Conference, Halkidiki, Greece, May 5-6, 2008. [http:/htl.handle.net/2142/11518] Gruzd, A. & Haythornthwaite, C. (forthcoming). Networking online: Cybercommunities. In J. Scott & P. Carrington (Eds.), Handbook of Social Network Analysis. London: Sage.
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