=Paper=
{{Paper
|id=None
|storemode=property
|title=Group Informatics: A Multi-Domain Perspective on the Development of Teaching Analytics
|pdfUrl=https://ceur-ws.org/Vol-894/paper2.pdf
|volume=Vol-894
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==Group Informatics: A Multi-Domain Perspective on the Development of Teaching Analytics==
https://ceur-ws.org/Vol-894/paper2.pdf
Group
Informatics:
A
Multi-‐Domain
Perspective
on
the
Development
of
Teaching
Analytics
Sean P. Goggins, Drexel University
Online Learning
In this position paper, I argue that the separation of learning analytics,
teaching analytics and other mechanisms for viewing the relationship between
electronic trace data and performance will be enhanced by a perspective that
takes related work in other domains into account. I present a few examples
from domains I have developed statistical and visual analytics for as exem-
plars of how a research program might accomplish the fluid transfer of analyt-
ics research across domains in a way that impacts teaching and learning. I
begin by characterizing some of the limitations I see in learning analytics gen-
erally, and which I argue remain salient issues in the development of teaching
analytics. In both cases, the goal is to advance learning.
Prior research measuring “online learning” performance has a number
of limitations and inconsistencies. First, prior studies of online learning
groups do not relate the temporality of group development as a central aspect
of analysis, yet group performance, structure and identity are widely under-
stood to change over time (Gersick, 1988; Knowles & Knowles, 1955; Tuck-
man, 1965). Second, learning performance is not consistently measured or is
not measured at all. Student grades are frequently used as a method of con-
venience, but their limited utility as a measure for learning performance is
well documented. Third, there is wide variation in the meaning of words like
“online” and “computer supported collaborative learning”. In some studies
online groups are those who meet partially online and partially face to face
(Cho, Gay, Davidson, & Ingraffea, 2007; Cress, Barquero, Buder, & Hesse,
2005; Johnson, Suriya, Yoon, Berrett, & Jason, 2002; Michinov & Michinov,
2007; Michinov & Michinov, 2008; Michinov, Michinov, & Toczek-Capelle,
2004) and in other studies the groups may actually be composed of geograph-
ically distributed subgroups (Cadima, Ferreira, Monguet, & Ojeda, 2010).
Only a few studies look explicitly at the completely online case (Goggins,
Laffey, & Galyen, 2009). Such differences in socio-technical context are
widely understood to have a material effect on group experience (Dourish,
2004; Nardi, 2010), but careful comparison and definition of context are miss-
ing from the literature. To build inquiry around teaching analytics, these same
limitations must be overcome.
I view teaching analytics as a mechanism for improving teaching;
which exists as an important profession in society because it serves the pur-
pose of facilitating learning. How teaching analytics improves learning is
therefore inseparable from the measurement of learning, and online teaching
contexts are of growing importance in societies around the globe. One meas-
ure of online learning performance, particularly in groups, is group efficacy.
Self-efficacy is a demonstrated predictor of individual performance (Bandura,
1997), and recent research has extended the concept of efficacy to the small
group unit of analysis. Hardin, Fuller & Valacich (2006) developed a four-
item online group efficacy survey based on the prior work of Whiteoak et al
(2004) and Gibson et al (2000). The results of their study included the deter-
mination that, in virtual settings, group efficacy is strongly related to group
performance. Hardin et al’s (2006) survey of group efficacy in an online
course is thus one suitable indicator of performance at the group level. Sys-
tematic evaluation and comparison of group work products, which Hardin et
al (2006) demonstrated to vary with Group Efficacy will serve as an essential
performance measure in the work under way. Teaching analytics that focus
on the small group unit of analysis, and work to develop the sense of group
efficacy within these groups are one important area for focus.
To develop teaching analytics as an area of inquiry, I suggest that we
must step back and consider the challenges and opportunities of analytics re-
search across a range of discourse communities. There are four important
challenges for leveraging electronic trace data for the development of analyt-
ics in any domain. First, the electronic trace data alone is not usually a com-
plete record of participant interactions (Goggins, Mascaro, & Valetto, 2012b;
Goggins, Valetto, Mascaro, & Blincoe, 2012a; Howison, Wiggins, & Crow-
ston, 2012). Second, the relationship between these traces and performance
requires systematic evaluation (Adar & Ré, 2007); third, organizational flexi-
bility as measured through the change in the social networks detectable from
electronic trace data is difficult to ground both theoretically and empirically
solely in analysis of those traces (Goggins et al., 2012b; Howison et al.,
2012). Fourth, leadership in virtual organizations can be captured through
social network analysis of electronic trace data, but how these networks relate
to structural change and performance varies significantly across contexts
(Blincoe, Valetto, & Goggins, 2012; Cataldo, Wagstrom, Herbsleb, & Carley,
2006; Goggins et al., 2012b; Goggins, Laffey, & Amelung, 2011; Goggins,
Laffey, Amelung, & Gallagher, 2010; Goggins, Mascaro, & Mascaro, 2012;
Gong, Teng, Livne, & Brunetti…, 2011; Huffaker, Teng, Simmons, Gong, &
Adamic, 2011; King, 2011; Mascaro & Goggins, 2011a). New methodologi-
cal approaches to technology mediated learning and teaching analytics re-
search, both empirical and theoretical, are required to address these challeng-
es.
The rest of my position paper is broken down into two sections. First,
I provide a review of prior approaches to the analysis of electronic trace data
across domains. Second, I present a brief overview of my Group Informatics
methodological approach (Goggins et al., 2012a; Goggins et al., 2012b).
Theoretical Background and Approach
Electronic Trace Data for Socio-Technical Analysis and Measurement
The non-teaching and learning environments I discuss frame the discourse on
teaching analytics in a larger context, and bring an important perspective to
the goals of the workshop. Previous research leveraging large scale electronic
trace data has used a variety of approaches. Golbeck et.al (2010) analyzed
Twitter use by the US Congress to identify how elected officials were using
the technology. Sense-making has been used to understand how Twitter facili-
tates information sharing in a crisis situation (Heverin & Zach, 2011). In this
study, content and discourse analysis along with a time-series analysis were
used to analyze the frequency of messages over time, but there was limited
identification of important actors as part of the network. Other research uses
algorithmic approaches to understand sentiment and trends in social media
(Jansen, Zhang, Sobel, & Chowdury, 2009; Naaman, Becker, & Gravano,
2011; Thelwall, Buckley, & Paltoglou, 2011a; Thelwall, Buckley, & Pal-
toglou, 2011b). Examining sentiment using text analysis tools, without quali-
tative analysis of the content in this communication or analysis of the social
networks that emerge and change through technology severely limits and even
distorts the findings and potential contribution of these studies, and others like
them (Back, Küfner, & Egloff, 2010; Back, Küfner, & Egloff, 2011; Pury,
2011). Network analysis is one important tool that Group Informatics adapts
to represent emergent social phenomena, especially within groups that emerge
in technology mediated environments.
Network analysis of technologically-mediated groups leverages
knowledge from decades of social science research focused on understanding
how social interactions between individuals evolve into social networks, and
how these networks influence individual and group behavior (Freeman, 2003;
Freeman, 2004; Straus, 1993). Through decades of research on thousands of
datasets describing interactions in the physical world, network analysts built a
set of validated measures to help identify important actors in these social net-
works. Well-known statistical measures of individual influence and network
position include betweenness, which identifies bridging individuals who con-
nect two clusters in a network; closeness, which describes the ability of a per-
son to reach information within the network through a set of ties; and degree
centrality, which is a measure of an actors overall connectivity to other actors
in the network. These measures have different meanings when viewed
through different theoretical lenses and care must be taken to understand the
meaning in each application (Freeman, 1979; Friedkin, 1991).
Technology mediated environments are studied as networks (Brown
& Duguid, 2000), communities of practice (Wenger, 1998), groups (Goggins,
Laffey, & Tsai, 2007; Rohde & Shaffer, 2003; Rohde, Reinecke, Pape, &
Janneck, 2004) and individual relationships (Granovetter, 1985). Like Mitch-
ell (Mitchell, 1969), we identify the relationship between these different or-
ganizational structures as existing on a continuum that is discernable through
comparative studies of social network characteristics, such as density and size.
A New Methodological Approach for Theory Building
My research team and I developed a comprehensive methodological approach
and ontology for the study of virtual organizations that addresses the four
challenges outlined above (Goggins et al., 2012b). This approach includes the
contextualization, aggregation and weighting of member interactions, cap-
tured as electronic trace data, with the technical environment producing trace
data, artifact categories, characteristics of members and groups and the nature
and type of interactions that occur between technology mediated learning en-
vironment members. Among the tenets of the Group Informatics approach is
the focus on the small group as the unit of analysis, and the integrated, con-
certed use of quantitative and qualitative methods for that analysis, which
leverage electronic trace data produced within virtual organizations. We want
to study and develop analytics for a range of virtual organizations at the small
group level in response to changes in the role of ICTs in daily life and work.
There is a well recognized reflexive relationship between organizational
change and ICT uptake and use (Kling & Scacchi, 1982; Kling, 1979; Kling,
1980; Kiesler, Boh, Ren, & Weisband, 2005), but the shift in ICT use from
systematic, work-focused use to wide, diffuse use in daily life (Grudin, 2010;
Sawyer, 2009) calls for a reconsideration of the role that small group’s, who
form or emerge within technology mediated organizations, play in adoption of
ICT, and their impact on structural change and performance. Further motiva-
tion for this shift is supported by long-standing analysis of social behavior that
recognizes the central role small groups play in organizational change, socie-
tal change (Fine & Harrington, 2004) and ICT adoption and use (Goggins,
Laffey, & Gallagher, 2011; Mead, 1934; Mead, 1958; Stahl, 2006). In the
past, the munificent variation of what constitutes a group inspired calls for
abandonment of “group” as a construct for collaborative computing research
(Schmidt & Bannon, 1992), yet important theory development related to ICT
mediated groups contained in larger organizational contexts continued as a
relatively small thread within information science and CSCW research
(Latour, 2007; Turner, Bowker, Gasser, & Zacklad, 2006). The theory devel-
opment we propose recognizes these tensions between units of analysis in the
field.
Group Informatics is principally concerned with the emergence and
development of small groups within larger socio-technical environments,
which may be conceptualized as communities of practice, networks of prac-
tice or, more broadly as virtual organizations. In the Group Informatics mod-
el, individual relationships are implicit in the occurrence of an interaction
between two people, made visible via electronic trace data.
The types of collaboration and emergence we study are easily conflat-
ed with a milieu of socio-technical community, group and organizational
forms often discussed in broad strokes while presenting data focused on a
singular example. A few articles have deliberately advanced less specific
descriptions of group size in favor of a broad consideration of collaboration
through technology (Schmidt & Bannon, 1992). When looking across differ-
ent socio-technical systems, more care must be taken with the use of these
terms. There are important differences between studies of popular social net-
working sites, collaborative wikis, and what we mean by technology mediated
environments. Social networking sites like Facebook and MySpace make a
person’s ego network more visible, encourage the development and mainte-
nance of weak ties (Granovetter, 1985), and do little to support group work or
group identity, though many people do join particular online groups as an
expression of identity. In these settings, groupness is less a phenomenon that
emerges from discourse or work, but is instead predetermined by identity
formed outside the environment (Goggins & Mascaro, 2012; Goggins et al.,
2012a; Mascaro & Goggins, 2011a; Mascaro & Goggins, 2011b). In the so-
cial networking sense, the use of Facebook for social coordination constitutes
what Brown and Duguid characterize as networks of practice (Brown &
Duguid, 2000). Networks are more loosely configured than “communities”,
but in general are a more apt description for these phenomena.
Present day collaborative editing systems invert those same limita-
tions. Studies of Wikipedia demonstrate the interjection of individual effort
into a collaborative virtual knowledge space that is heavily controlled by
member practices (Kittur & Kraut, 2008; Priedhorsky et al., 2007) and sys-
tematic bureaucracy (Kittur, Suh, Pendleton, & Chi, 2007). In Facebook,
members are locked into an ego-centered interaction, whereas in Wikipedia,
users are locked into an artifact-centered interaction. Neither system makes
fluid movement between the people working on the system and the artifact
possible. Notions of coherent, emergent groups are designed out of each sys-
tem and the trace data these systems produce reflect these orthogonal, but
equally narrow types of social interaction. User modeling and personalization
complements this work through its focus on designing for the user. Integra-
tion of research focused on the limitations of artifact focus, social interaction
focus and user modeling and personalization is therefore an especially promis-
ing, synthesized area of inquiry. One could argue that the similarities be-
tween wiki governance and a traditional classroom are striking.
The interaction is central to Group Informatics, and is captured be-
tween people, or people and artifacts; which are treated as boundary objects
(Lee, 2007; Star & Griesemer, 1989) around which interactions occur. The
contribution to the workshop that I propose leverages my methodological ap-
proach to measure structural change between teachers and students in techno-
logically mediated learning environments by contextualizing their interactions
and roles; and operationalizing Dourish’s (2004) view of context as a dynamic
construct in the service of developing teaching analytics as a new and im-
portant area of inquiry.
References
Adar, E., & Ré, C. (2007). Managing uncertainty in social networks. IEEE
Data Eng. Bull, 30(2), 15-22.
Back, M. D., Küfner, A. C. P., & Egloff, B. (2010). The emotional timeline of
September 11, 2001. Psychological science, 21(10), 1417. Retrieved
from http://pss.sagepub.com/content/21/10/1417.short
Back, M. D., Küfner, A. C. P., & Egloff, B. (2011). Automatic or the People?
Psychological Science, 22(6), 837-838. Retrieved from
http://pss.sagepub.com/content/22/6/837.short
Bandura, A. (1997). Self Efficacy: The Exercise of Control. New York: Mac-
millan.
Blincoe, K., Valetto, G., & Goggins, S. (2012). Leveraging Task Contexts for
Managing Developers' Coordination. Proceedings from ACM Confer-
ence on Computer Supported Cooperative Work, 2012, Seattle, WA.
Brown, J. S., & Duguid, P. (2000). The Social Life of Information. Cam-
bridge, MA: Harvard Business School Press.
Cadima, R., Ferreira, C., Monguet, J., & Ojeda, J. (2010). Promoting social
network awareness: A social network monitoring system (54). Elsevier.
doi:10.1016/j.compedu.2009.11.009
Cataldo, M., Wagstrom, P. A., Herbsleb, J. D., & Carley, K. M. (2006). Iden-
tification of Coordination Requirements: Implications for the Design of
Collaboration and Awareness Tools. Proceedings from ACM Confer-
ence On Computer Supported Cooperative Work, Banff, Alberta, Cana-
da.
Cho, H., Gay, G., Davidson, B. D., & Ingraffea, A. (2007). Social Networks,
Communication Styles, and Learning Performance in a CSCL Commu-
nity. Computers and Education, 49, 309-329.
Cress, U., Barquero, B., Buder, J., & Hesse, F. W. (2005). Social dilemma in
knowledge communication via shared databases. Barriers and Biases in
Computer-Mediated Knowledge Communication, 143-167.
Dourish, P. (2004). What We Talk About When We Talk About Context. Per-
sonal and Ubiquitous Computing, 8, 19-30.
Fine, G. A., & Harrington, B. (2004). Tiny publics: small groups and civil
society. Sociological Theory, 22(3), 341-356. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1111/j.0735-
2751.2004.00223.x/abstract
Freeman, L. C. (1979). Centrality in social networks conceptual clarification.
Social networks, 1(3), 215-239. Retrieved from
http://www.sciencedirect.com/science/article/pii/0378873378900217
Freeman, L. C. (2004). The development of social network analysis. Empirical
Press Vancouver, BC.
Freeman, L. C. (2003). Finding social groups: A meta-analysis of the south-
ern women data. Proceedings from Dynamic Social Network Modeling
and Analysis: workshop summary and papers.
Friedkin, N. E. (1991). Theoretical foundations for centrality measures. Amer-
ican journal of Sociology, 1478-1504. Retrieved from
http://www.jstor.org/stable/2781908
Gersick, C. (1988). Time and Transition in Work Teams: Toward a New
Model of Group Development. Academy of Management Journal, 31,
9-41.
Gibson, C., Randel, A. E., & Earley, P. C. (2000). Understanding Group Effi-
cacy. Group and Organization Management, 25, 67-97.
Goggins, S., Laffey, J., & Amelung, C. (2011). Context Aware CSCL: Moving
Toward Contextualized Analysis. Proceedings from CSCL 2011, Hong
Kong.
Goggins, S., Mascaro, C., & Mascaro, S. (2012). Relief after the 2010 Haiti
Earthquake: Participation and Leadership in an Online Resource Co-
ordination Network. Proceedings from ACM Conference on Computer
Supported Cooperative Work, Seattle, WA.
Goggins, S., Laffey, J., & Galyen, K. (2009). Social Ability in Online Groups:
Representing the Quality of Interactions in Social Computing Environ-
ments. Proceedings from IEEE Conference on Computer Science and
Engineering, Vancouver, BC.
Goggins, S., Laffey, J., & Tsai, I.-C. (2007). Cooperation and Groupness:
Community Formation in Small online Collaborative Groups. Proceed-
ings from Proceedings of the ACM Group Conference 2007, Sanibel Is-
land, FL.
Goggins, S., & Mascaro, C. (2012). Context Matters: ICT’s Effects on Physi-
cal, Informational and Cultural Distance in a Rural IT Firm. The Infor-
mation Society, under second review.
Goggins, S., Valetto, P., Mascaro, C., & Blincoe, K. (2012a). Creating A
model of the Dynamics of Socio-Technical Groups using Electronic
Trace Data. User Modeling and User-Adapted Interaction: The Journal
of Personalization Research, Accepted.
Goggins, S. P., Laffey, J., & Gallagher, M. (2011). Completely online group
formation and development: small groups as socio-technical systems.
Information Technology & People, 24(2), 104-133.
doi:10.1108/09593841111137322
Goggins, S. P., Laffey, J., Amelung, C., & Gallagher, M. (2010). Social Intel-
ligence In Completely Online Groups. Proceedings from IEEE Interna-
tional Conference on Social Computing, Minneapolis, MN.
Goggins, S. P., Mascaro, C., & Valetto, G. (2012b). Group Informatics: A
Methodological Approach and Ontology for Understanding Socio-
Technical Groups. JASIS&T, Accepted.
Golbeck, J., Grimes, J. M., & Rogers, A. (2010). Twitter use by the US Con-
gress. Journal of the American Society for Information Science and
Technology, 61(8), 1612-1621. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1002/asi.21344/full
Gong, L., Teng, C. Y., Livne, A., & Brunetti…, C. (2011). Coevolution of
Network Structure and Content. Arxiv preprint arXiv: …,. Retrieved
from http://arxiv.org/abs/1107.5543
Granovetter, M. (1985). Economic action and social structure: the problem of
embeddedness. American journal of sociology, 91(3), 481.
Grudin, J. (2010). Timelines: CSCW: time passed, tempest, and time past.
Interactions, 17(4), 38-40.
Hardin, A. M., Fuller, M. A., & Valacich, J. S. (2006). Measuring Group Effi-
cacy in Virtual Teams. Small Group Research, 37(1), 65-87.
Heverin, T., & Zach, L. (2011). Use of microblogging for collective sense‐
making during violent crises: A study of three campus shootings. Jour-
nal of the American Society for Information Science and Technology,.
Retrieved from
http://onlinelibrary.wiley.com/doi/10.1002/asi.21685/full
Howison, J., Wiggins, A., & Crowston, K. (2012). Validity Issues in the Use
of Social Network Analysis with Digital Trace Data. Journal of the As-
sociation of Information Systems, 12(2)(2).
Huffaker, D. A., Teng, C. Y., Simmons, M. P., Gong, L., & Adamic, L. A.
(2011). Group Membership and Diffusion in Virtual Worlds. IEEE’11,.
Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power:
Tweets as electronic word of mouth. Journal of the American society
for information science and technology, 60(11), 2169-2188. Retrieved
from http://onlinelibrary.wiley.com/doi/10.1002/asi.21149/full
Johnson, S. D., Suriya, C., Yoon, S. W., Berrett, J. V., & Jason, L. (2002).
Team Development and Group Process of Virtual Learning Teams.
Computers and Education, 39, 379-393.
Kiesler, S., Boh, W. F., Ren, Y., & Weisband, S. (2005). Virtual Teams and
the Geographically Dispersed Professional Organization. Proceedings
from Extending the Contributions of Professor Rob Kling to the Analy-
sis of Computerization Movements.
King, G. (2011). Ensuring the Data Rich Future of the Social Sciences. Sci-
ence, 331(6018), 719-721. doi:10.1126/science.1197448
Kittur, A., & Kraut, R. (2008). Harnessing the Wisdom of Crowds in Wikipe-
dia: Quality Through Coordination. Proceedings from CSCW 2008,
San Diego, CA.
Kittur, A., Suh, B., Pendleton, B. A., & Chi, E. H. (2007). He Says, She Says:
Conflict and Coordination in Wikipedia. Proceedings from CHI 2007,
San Jose, CA.
Kling, R. (1980). Social analyses of computing: Theoretical perspectives in
recent empirical research. ACM Computing Surveys (CSUR), 12(1), 61-
110.
Kling, R., & Scacchi, W. (1982). The web of computing: Computer technolo-
gy as social organization. Advances in computers, 21, 1-90. Retrieved
from http://linkinghub.elsevier.com/retrieve/pii/S0065245808605677
Kling, R. (1979). Social Issues and impacts of computing: From arena to dis-
cipline. Proceedings from Computers and Human Choice, Vienna.
Knowles, Knowles. (1955). Introduction to Group Dynamics. New York:
Associated Press.
Latour, B. (2007). Reassembling the Social: An Introduction to Actor-Network
Theory. Oxford:.
Lee, C. P. (2007). Boundary Negotiating Artifacts: Unbinding Routine of
Boundary Objects and Embracing Chaos in Collaborative Work. Com-
puter Supported Cooperative Work, 16, 307-339.
Mascaro, C., & Goggins, S. P. (2011a). Brewing Up Citizen Engagement: The
Coffee Party on Facebook. Proceedings from Communities & Technol-
ogies, 2011, Brisbane, Australia.
Mascaro, C., & Goggins, S. (2011b). The Daily Brew: The Structural Evolu-
tion of the Coffee Party on Facebook During the 2010 United States
Midterm Election Season. Proceedings from 4th Annual Political Net-
works Conference, Ann Arbor, MI.
Mead, M. (1934). Kinship in the Admiralty Islands. Transaction Pub. Re-
trieved from
http://books.google.com/books?hl=en&lr=&id=8Fyj0h0G_OUC&oi=fn
d&pg=PR9&dq=mead+"small+group"+"M+mead"&ots=-
7aKCAORBO&sig=IzsWex2tPvK2vmGGWBpfm-6OFXs
Mead, M. (1958). Cultural determinants of behavior. Behavior and evolution,
480-503.
Michinov, E., & Michinov, N. (2007). Identifying a transition period at the
midpoint of an online collaborative activity: A study among adult learn-
ers. Computers in Human Behavior, 23, 1355-1371.
Michinov, N., & Michinov, E. (2008). Face-To-Face contact at the midpoint
of an online collaboration: ITs impact on the patterns of participation,
interaction, affect and behavior over time. Computers and Education,.
Michinov, N., Michinov, E., & Toczek-Capelle, M.-C. (2004). Social Identity,
Group Processes, and Performance in Synchronous Computer-Mediated
Communication. Group Dynamics Theory, Research and Practice, 8,
27-39.
Mitchell, J. C. (1969). Social networks in urban situations: analyses of per-
sonal relationships in Central African towns. Humanities Press Intl. Re-
trieved from
http://books.google.com/books?hl=en&lr=&id=8RrpAAAAIAAJ&oi=f
nd&pg=PA1&dq=african+social+networks&ots=nnO3VuOiWL&sig=
C2wNoTiD1C9tTLiNpLprZOxdo4c
Naaman, M., Becker, H., & Gravano, L. (2011). Hip and trendy: Characteriz-
ing emerging trends on Twitter. Journal of the American Society for In-
formation Science and Technology,. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1002/asi.21489/full
Nardi, B. (2010). My life as a night elf priest: An anthropological account of
World of Warcraft. Ann Arbor, MI: University of Michigan Press. Re-
trieved from
http://books.google.com/books?hl=en&lr=&id=8Ac9pUyROE8C&oi=f
nd&pg=PA3&dq="My+life+as+a+night+elf+priest",+nardi&ots=OjL2v
FCUlg&sig=uhvzENmLQ8WtW97esdrLoyTpXQc
Priedhorsky, R., Chen, J., Lam, T., Panciera, K., Terveen, L., & Riedl, J.
(2007). Creating, Destroying and Restoring Value in Wikipedia. Pro-
ceedings from Group '07, Sanibel Island, FL.
Pury, C. L. (2011). Automation can lead to confounds in text analysis: back,
kufner, and egloff (2010) and the not-so-angry americans. Psychol Sci,
22(6), 835-836. doi:10.1177/0956797611408735
Rohde, M., Reinecke, L., Pape, B., & Janneck, M. (2004). Community-
Building with Web-Based Systems - Investigating a Hybrid Community
of Students. Computer Suppoerted Cooperative Work, 13, 471-499.
Rohde, M., & Shaffer, D. W. (2003). Us, Ourselves and We: Thoughts about
Social (Self-) Categorization. SIGGROUP Bulletin, 24(3), 19-24.
Sawyer, S. (2009). Advancing Social Informatics. Proceedings from The 5th
Annual Social Informatics Research Symposium on “People, Infor-
mation, Technology: The Social Analysis of Computing in a Diverse
and Pluralistic World,” ASIST Special interest group on social infor-
matics, Vancouver, BC.
Schmidt, K., & Bannon, L. (1992). Taking CSCW seriously. Computer Sup-
ported Cooperative Work (CSCW), 1(1), 7-40.
Stahl, G. (2006). Group Cognition: Computer Support for Building Collabo-
rative Knowledge. Boston, MA: MIT Press.
Star, S. L., & Griesemer, J. R. (1989). Institutional Ecology, 'Translations' and
Boundary Objects: Amateurs and Professionals in Berkeley's Museum
of Vertebrate Zoology, 1907-39. Social Studies of Science, 19, 387-420.
Straus, A. (1993). Continual Permutations of Action. Hawthorne, NY: Aldine
De Gruyter.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011a). Sentiment in Twitter
events. Journal of the American Society for Information Science and
Technology,. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1002/asi.21462/full
Thelwall, M., Buckley, K., & Paltoglou, G. (2011b). Sentiment strength de-
tection for the social web. Journal of the American Society for Infor-
mation Science and Technology,. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1002/asi.21662/full
Tuckman, B. W. (1965). Developmental Sequence in Small Groups. Psycho-
logical Bulletin, 63, 384-399.
Turner, W., Bowker, G. C., Gasser, L., & Zacklad, M. (2006). Information
Infrastructures for Distributed Collective Practices. Computer Support-
ed Cooperative Work, 15, 93-110.
Wenger, E. (1998). Communities of Practice: Learning, Meaning and Identi-
ty. New York: Cambridge University Press.
Whiteoak, J., Chalip, L., & Hort, L. (2004). Assessing Group Efficacy. Small
Group Research, 35, 158-173.