Sean P. Goggins, Ph.D
In my research I employ a variety of methods and work across academic disciplines to explore the relationship between technology and new forms of social organization made possible by new social, and technological environments. How are small groups that exist exclusively through technology different than face to face groups? To what extent do fully distributed groups build identity and how? What are the performative and interactional differences between groups that are effective in their work and play and those that are not? What patterns of interaction are common across different kinds of technologically mediated groups? Given a particular context, which existing theories are useful for framing study? Which theories conflate socio-technical phenomena with phenomena in the physical world?
The broad purpose of my research is to understand how distance is experienced in virtual organizations. This interdisciplinary research challenge leads me to conceptualize both distance and proximity through the development of analytical tools, and to design positive interventions that lead to more optimal distributed group experiences in the future. Theoretically, I address coordination, knowledge construction, information behavior, discourse and group identity as key constructs in my work across domains. Each domain is more or less informed by each of these theoretical perspectives, according to what fits.
Two units of analysis are the center of my research: The Small Group and the technologically mediated interaction. I begin with an explanation of the importance of the small group unit of analysis to a research program I describe as “Group Informatics”. Sociologist Margaret Mead views the small group as the principle unit of human organization at which societal level change begins. Group Informatics research is inspired by the notion that small groups are uniquely powerful forms of social organization. Groups who rely on technology, and not physical co-location, for interaction leave traces of their interactions behind. The goal of Group Informatics research is to use these traces to build a better understanding of how such groups change over time, and how these changes might be anticipated. Group Informatics accomplishes this by building models of technologically mediated groups in online learning, disaster relief, software engineering, online dating, public political discourse, adult recreational sports and other domains where groups emerge and develop through technology.
Group informatics examines how technologically mediated groups form, develop identity, use information, create knowledge and evolve structurally. I apply a novel approach to discerning structure from electronic trace data using interface adapted log file transformations (Goggins, Laffey, Amelung, & Gallagher, 2010) and both time weighted and mode weighted network analysis (Goggins, Laffey, & Gallagher, 2011). Applying the time dimension to the analysis of small groups is a recognized strategy for understanding their development (Mcgrath, 1991; McGrath, Arrow, & Berdahl, 2000).
Small groups have three functions: Performance of work, maintenance of the group and satisfaction of member needs (Arrow, McGrath, & Berdahl, 2000). By examining electronic trace data we can learn how the structure and interactions of groups change over time and how different groups appear differently when performing the same tasks in similar
Stahl shows how groups work together to construct original ideas through technology, a phenomena he calls group cognition (Stahl, 2006). Group informatics applies findings from group cognition and other computer supported collaborative learning (CSCL) and computer supported cooperative work (CSCW) research to identify context specific ways to measure technologically mediated group performance.
Situating Group Informatics
Informatics is a term used in a variety of disciplines. Medical informatics, social informatics, community informatics, bioinformatics and many other "informatics areas of inquiry" share the word, but apply it in different ways. Some informatics disciplines, like medical informatics, are more data structure driven. Others, like bioinformatics, have a computational focus.
Social informatics is a way of examining the social aspects of computerization. Group informatics is conceptualized here as considering the influences of computerization on the development of small, technologically mediated groups. Small groups are viewed as "engines of knowledge building" in group cognition research. Group Informatics emerges from the idea that small groups are uniquely powerful forms of social organization across a wide range of domains; then explores groups who rely on technology, and not physical co-location, for interaction. The goal of Group Informatics research is to build a better understanding of how such groups change over time, and how these changes might be anticipated. Compared to social informatics, group informatics takes a more active stance. I analyze phenomena through electronic trace data in concert with other theoretically valid data, including interview, survey and content analysis.
Recently there has been much research finding clusters of interaction in large populations, or developing computational mechanisms for "group discovery". For the group informatics researcher, these discoveries are incomplete; I seek to understand what the cluster means, where it came from and how it evolves over time. By scaling down to the small group, Group Informatics enables "scaling up" of group discovery grounded in the experience of the members. Group Informatics approaches social and computational computing challenges with an integrated mix of computer science, technology and social science theory.
The time element in technologically mediated groups is little studied because to understand changes in small groups (information, knowledge and social behavior), one must, in the sociological tradition, understand both the structure observed and what those changes mean. Therefore, the first goal of Group Informatics research is the development of a library of socio-technical patterns of small group development.
Electronic Trace Data: Closing the Theoretical Coherence Gap
Electronic trace data analysts sometimes conflate notions of social connection in the virtual world with constructs understood in the physical world, but there are important differences. Online connection is not experienced in the same way or influential through the same mechanisms as face-to-face connection. Well-known social network constructs, like leadership (Balkundi & Kilduff, 2006; Fletcher & Kaufer, 2003), brokerage (Burt, 2005; Diani, 2003; Fleming & Waguespack, 2007) and information diffusion (Valente, 1996) are manifested differently in technologically mediated environments because both how they are experienced and how they are observed are different.
One positive difference is that electronic trace data helps to eliminate some of the shortcomings in traditional network analysis because all system interactions are logged (Lazer et al., 2009). This greater completeness helps to overcome sampling issues that occur in network analysis that depends on periodic observation or self-reporting of perceived connections between actors in the physical world (Bernard, Killworth, Kronenfeld, & Sailer, 1984; Freeman, Romney, & Freeman, 1987; Howison, Wiggins, & Crowston, under fourth review; Parigi & Bearman, 2005). Analysis of physical social networks through traditional methods is also vulnerable to boundary specification issues (Laumann & L.P., 1997; Laumann, Marsden, & Prensky, 1989), while analysis of technologically mediated groups establishes a clear boundary of participation in the system.
One caution when analyzing social networks derived from electronic trace data is that not all interactions are necessarily logged in a system the researcher or analyst can see. Awareness of the potential (likely) existence of non-logged interactions between users and each other or between users and other systems is therefore crucial; if you are only looking at log data, there is a good possibility you have not accounted for the full story. For example, software engineers using a bug tracking system, MyLyn and a source code repository may also email each other. In other instances where face-to-face relationships also exist, the electronic interactions may extend and augment interactions that have occurred in the physical space. As Laumann (2006) notes, it is up to the researcher to determine the representativeness of the sample used for network analysis. In my studies of software engineering, online courses, disaster relief and other venues I, like Laumann (2006) find the traces to examine representative of connections overall. I determine representativeness by triangulating electronic trace data and network analysis with other data collection and analysis methods. The model of Group Informatics that I have developed helps to systematically assess representativeness of trace data and provides empirical justification and a clear rationale for connection weighting decisions in different socio-technical environments.
Group Awareness and Task Context Data
Individuals participating in technologically mediated forms of organization often have difficulty recognizing when groups emerge, and how the groups they take part in evolve. My research on context awareness and context adaptivity contributes an analytical framework that improves awareness of these virtual group dynamics through analysis of electronic trace data from tasks and interactions carried out by individuals in systems not explicitly designed for context adaptivity, user modeling or user personalization. My work thus far focuses on two distinct cases to which I have applied this analytical framework. These two cases provide a useful contrast of two prevalent ways for analyzing social relations starting from electronic trace data of either artifact-mediated or direct person-to-person interactions. The cases integrate electronic trace
This work demonstrates the promise of my general model of Group Informatics, which can serve to construct group context, and be leveraged by future tool development aimed at augmenting context adaptivity with group context and a social dimension. Papers under review in this part of my research program describe methods, data management strategies and technical architecture to support the analysis of individual user task context, increased awareness of group membership, and an integrated view of social, information and coordination contexts.
Online Political Discourse
Group Informatics is applied broadly to areas of inquiry
With the increased reliance on technologically mediated
Political groups on social media sites are often viewed as
Group Informatics can be applied to questions of invisible
Dependencies between tasks often result in Coordination
Current methods for detecting CRs and calculating
Without a “live” view of activities, CRs and STC are not
Emergent Leadership &
Some coordination problems are well
Effective coordination and information
Prior work in CSCW focuses on understanding and designing
Emergent leadership is a construct that Group Informatics
New Research Directions
Toward A Theory of Group Informatics
The introduction of new technologies that enable peer-peer
To facilitate systematic study of technologically mediated
Each social and technical environment dynamically
Group Informatics provides an integrated framework for
Together, the model and method for Group Informatics
Rural IT Workers: Physical, Informational and Cultural
This thread of my research is embodied in a single paper
This is the newest thread of my research program, and
Creating a culture of learning within the organization was
But what do firms do when the answer is not known, the
“Learning from the past is not enough to
Technology solutions are one component of supporting
The basic questions for industrial and information-society
style='font-size:12.0pt;font-family:Times'>Argyris, C., & Schon, D. A.
(1974). Theory in practice: Increasing professional effectiveness.
Jossey-Bass. Retrieved from http://doi.apa.org/psycinfo/1975-03166-000
style='font-size:12.0pt;font-family:Times'>Arrow, H., McGrath, J. E., &
Berdahl, J. L. (2000). Small Groups as Complex Systems. Thousand Oaks,
CA: Sage Publications.
style='font-size:12.0pt;font-family:Times'>Balkundi, P., & Kilduff, M.
(2006). The ties that lead: A social network approach to leadership. The
Leadership Quarterly, 17(4), 419-439. Retrieved from
style='font-size:12.0pt;font-family:Times'>Bernard, H. R., Killworth, P.,
Kronenfeld, D., & Sailer, L. (1984). The problem of informant accuracy: The
validity of retrospective data. Annual Review of Anthropology, 13,
495-517. Retrieved from http://www.jstor.org/stable/2155679
style='font-size:12.0pt;font-family:Times'>Brooks, F. P. (1995). The Mythical
Man-Month: Essays on Software Engineering. Adison Wesley. Reading, MA,.
style='font-size:12.0pt;font-family:Times'>Brown, J. S., & Duguid, P.
(1991). Organizational Learning and Communities-Of-Practice: Toward a Unified
View of Working, Learning and Innovation. Organizational Science, 2(1),
style='font-size:12.0pt;font-family:Times'>Brown, J. S., & Duguid, P.
(2000). The Social Life of Information. Cambridge, MA: Harvard Business
style='font-size:12.0pt;font-family:Times'>Burt, R. S. (2005). Brokerage and
closure: An introduction to social capital. Retrieved from
style='font-size:12.0pt;font-family:Times'>Cataldo, M., Bass, M., Herbsleb, J.,
& Bass, L. (2007). On Coordination Mechanisms in Global Software
Development. Global Software Engineering, International Conference on, 0,
style='font-size:12.0pt;font-family:Times'>Cataldo, M., Herbsleb, J. D., &
Carley, K. M. (2008). Socio-Technical Congruence: A Framework for Assessing
the Impact of Technical and Work Dependencies on Software Development
Productivity. Proceedings from International Workshop on Socio-Techical
style='font-size:12.0pt;font-family:Times'>Cataldo, M., Mockus, A., Roberts, J.
A., & Herbsleb, J. D. (2009). Software dependencies, work dependencies, and
their impact on failures. IEEE Transactions on Software Engineering,
style='font-size:12.0pt;font-family:Times'>dePaula, R., & Fischer, G.
(2005). Knowledge Management: Why Learning from the Past Is Not Enough! Knowledge
Management, 21-53. Retrieved from
style='font-size:12.0pt;font-family:Times'>Diani, M. (2003). 5.Leaders' Or
Brokers? Positions and Influence in Social Movement Networks. Social
movements and networks, 1(9), 105-123. Retrieved from
style='font-size:12.0pt;font-family:Times'>Dourish, P. (2004). What We Talk
About When We Talk About Context. Personal and Ubiquitous Computing, 8,
style='font-size:12.0pt;font-family:Times'>Smith, M., Lyles, M. A., &
Peteraf, M. A. (2009). Dynamic capabilities: current debates and future
directions. British Journal of Management, 20, S1-S8.
style='font-size:12.0pt;font-family:Times'>Fleming, L., & Waguespack, D. M.
(2007). Brokerage, boundary spanning, and leadership in open innovation
communities. Organization Science, 18(2), 165. Retrieved from
style='font-size:12.0pt;font-family:Times'>Fletcher, J. K., & Kaufer, K.
(2003). Shared leadership. Shared leadership: Reframing the hows and whys of
style='font-size:12.0pt;font-family:Times'>Freelon, D. G. (2010). Analyzing
online political discussion using three models of democratic communication. New
Media & Society, 12(7), 1172. Retrieved from
style='font-size:12.0pt;font-family:Times'>Freeman, L. C., Romney, A. K., &
Freeman, S. C. (1987). Cognitive structure and informant accuracy. American
anthropologist, 89(2), 310-325. Retrieved from
style='font-size:10.0pt;font-family:Times'>Goggins, S., Laffey, J., Galyen, K.,
& Mascaro, C.
(2011). Group Awareness in Completely Online Learning Groups: Identity, Structure,
Efficacy and Performance. International Journal of Computer Supported
Cooperative Work, Under Review.
style='font-size:12.0pt;font-family:Times'>Goggins, S., Laffey, J., & Tsai,
I.-C. (2007a). Cooperation and Groupness: Community Formation in Small
online Collaborative Groups. Proceedings from Proceedings of the ACM Group
Conference 2007, Sanibel Island, FL.
style='font-size:12.0pt;font-family:Times'>Goggins, S., & Mascaro, C.
(2011). Context Matters: ICT’s Effects on Physical, Informational and Cultural
Distance in a Rural IT Firm. The Information Society, under review.
style='font-size:12.0pt;font-family:Times'>Goggins, S., Tsai, I.-C., Kim, B.,
Kumalasari, C., Laffey, J., & Amelung, C. (2007b). Building a Model
Explaining the Social Nature of Online Learning. Proceedings from AERA,
2007, Chicago, IL.
style='font-size:12.0pt;font-family:Times'>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),
style='font-size:12.0pt;font-family:Times'>Goggins, S. P., Laffey, J., Amelung,
C., & Gallagher, M. (2010). Social Intelligence In Completely Online Groups.
Proceedings from IEEE International Conference on Social Computing,
style='font-size:12.0pt;font-family:Times'>Howison, J., Wiggins, A., &
Crowston, K. (under fourth review). Validity Issues in the Use of Social
Network Analysis for the Study of Online Communities. Journal of the
Association of Information Systems,.
style='font-size:12.0pt;font-family:Times'>Kling, R., & Courtright, C.
(2004). Group Behavior and Learning in Electronic Forums: A Sociotechnical
Approach. In S. Barab, R. Kling, & J. H. Gray (Eds.), Designing for
Virtual Communities in the Service of Learning (pp. 91-119). New York, NY:
Cambridge University Press.
style='font-size:12.0pt;font-family:Times'>Kraut, R., & Streeter, L.
(1995). Coordination in software development. Communications of the ACM,
style='font-size:12.0pt;font-family:Times'>Krippendorff, K. (2004). Content
Analysis: An Introduction to Its Methodology. Thousand Oaks, CA: Sage
style='font-size:12.0pt;font-family:Times'>Laumann, E. O., Marsden, P. V.,
& Prensky, D. (1989). The boundary specification problem in network
analysis. Research methods in social network analysis, 61, 87.
style='font-size:12.0pt;font-family:Times'>Laumann, E. O., & L.P., S.
(1997). Measuring social networks using samples, Is network analysis relevant
to survey research. In J. Bancroft (Ed.), Researching sexual behavior:
Methodological issues (pp. 390-416). Bloomington, IN: Indiana Univ Pr.
style='font-size:12.0pt;font-family:Times'>Laumann, E. O. (2006). A 45-Year
Retrospective on Doing Networks. Connections, 27(1), 65-90.
style='font-size:12.0pt;font-family:Times'>Lazer, D., Pentland, A., Adamic, L.,
Aral, S., Barabasi, A. L., Brewer, D., … Van Alstyne, M. (2009). Social
science. Computational social science. Science, 323(5915),
style='font-size:12.0pt;font-family:Times'>Lea, M., & Spears, R. (1992).
Paralanguage and social perception in computer-mediated communication. Journal
of Organizational Computing and Electronic Commerce, 2(3), 321-341.
style='font-size:12.0pt;font-family:Times'>Mascaro, C., & Goggins, S. P.
(2011). Brewing Up Citizen Engagement: The Coffee Party on Facebook.
Proceedings from Communities & Technologies, 2011, Brisbane, Australia.
style='font-size:12.0pt;font-family:Times'>McCombs, M., & Ghanem, S. I.
(2001). The convergence of agenda setting and framing. Framing public life:
Perspectives on media and our understanding of the social world, 67-81. Retrieved
style='font-size:12.0pt;font-family:Times'>McCombs, M. E., & Shaw, D. L.
(1972). The agenda-setting function of mass media. Public opinion quarterly,
36(2), 176. Retrieved from
style='font-size:12.0pt;font-family:Times'>Mcgrath, J. E. (1991). Time,
Interaction and Performance (TIP). Small Group Research, 22(2),
style='font-family:Times'>McGrath, J. E., Arrow, H., & Berdahl, J. L style='font-size:12.0pt;font-family:Times'>. (2000). style='font-family:Times'>The Study of Groups: Past, Present, and Future style='font-size:12.0pt;font-family:Times'>. Personality and Social Psychology Review style='font-size:12.0pt;font-family:Times'>, 4( style='font-family:Times'>1), 95-105 style='font-size:12.0pt;font-family:Times'>.
style='font-size:12.0pt;font-family:Times'>Palen, L., & Liu, S. B. (2007). Citizen
communications in crisis: anticipating a future of ICT-supported public
participation. Proceedings from Proceedings of the SIGCHI conference on
Human factors in computing systems.
style='font-size:12.0pt;font-family:Times'>Palen, L., Vieweg, S., Liu, S. B.,
& Hughes, A. L. (2009). Crisis in a Networked World: Features of
Computer-Mediated Communication in the April 16, 2007, Virginia Tech Event. Social
Science Computer Review, 27(4), 467-480.
style='font-size:12.0pt;font-family:Times'>Parigi, P., & Bearman, P. S.
(2005). Cloning headless frogs and other important matters: Conversation topics
and network structure. Social Forces, 83(2), 535-557. Retrieved
style='font-size:12.0pt;font-family:Times'>Parnas, D. L. (1972). On the
criteria to be used in decomposing systems into modules. Communications of
the ACM, 15(12), 1058.
style='font-size:12.0pt;font-family:Times'>Purcell, K., Rainie, L., Mitchell,
A., Rosenstiel, T., & Olmstead, K. (2010). Understanding the participatory
news consumer. Pew Internet and American Life Project, 1.
Participatory News Consumer.pdf
style='font-size:12.0pt;font-family:Times'>Stahl, G. (2006). Group
Cognition: Computer Support for Building Collaborative Knowledge. Boston,
MA: MIT Press.
style='font-size:12.0pt;font-family:Times'>Valente, T. W. (1996). Social
network thresholds in the diffusion of innovations. Social Networks, 18(1),
69-89. Retrieved from http://www.sciencedirect.com/science/article/pii/0378873395002561
style='font-size:12.0pt;font-family:Times'>Wojcieszak, M. E., & Mutz, D. C.
(2009). Online groups and political discourse: do online discussion spaces
facilitate exposure to political disagreement? Journal of Communication,
59(1), 40-56. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1460-2466.2008.01403.x/pdf
style='font-size:12.0pt;font-family:Times'>Yuan, C. Y., & Gay, G. (2006).
Homophily of Network Ties and Bonding and Bridging Social Capital in
Computer-Mediated Distributed Teams. Journal of Computer Mediated
Communication, 11, 1062-1084.
Sean P. Goggins – Teaching Statement
My teaching philosophy is to create learning experiences
for students who do not have real world experience, and to leverage the real
world experience of students whenever possible. This requires a high
degree of preparation on my part, and an ability to adapt material as it comes
into contact with the experience (or inexperience) of students. This
philosophy is informed by my personal travels as a teacher and a student:
I have been a technology leader
in a variety of software-centric businesses, while simultaneously earning two
M.S. degrees (one in adult education and human resource development, another in
computer science) and pursuing a PhD. For over ten years of my professional
life, I have led strategic technology during part of the day, and actively
reflected on that work in the context of the latest research in my field during
other parts of the day.
Argyris and Schon’s construct of learning-in-action is at
the center of my teaching philosophy. This is evident in two ways.
First, I do not believe mistakes are necessary for learning. Instead,
learning-in-action suggests that learning occurs through reflection on the
theory-in-action that is driving present behavior. Through ongoing
learning, teaching and reflection, I have experienced learning-in-action and am
therefore well equipped to model, identify and coach it with my students.
Second, Schon’s subsequent work articulating the powerful
use of reflection-in-action by classroom teachers is an example and
philosophical support I use to operationalize learning-in-action in my
classrooms. I create learning experiences that are based on real experiences
from the corporate world, and include pre-determined points of reflection
situated around critical decision points in the case. These critical decision
points are identified through research and practice. The result is classroom
modeling of reflection-in-action structured so that students develop a capacity
for recognizing critical patterns for decision-making without experiencing
failure first hand.
Teaching has been a primary or secondary component of my
work for over 10 years, in a variety of settings: University, community
college, corporate training and the global software design & engineering
communities of practice I participate in. The quality of my teaching is
attested to through high marks on student evaluations and reference letters
from supervisors and colleagues. Copies of both are available upon request.
On Learning and Technology
I am especially interested in making education available to
people who are not able to participate in face to face classroom sessions. The
model I have used for this in the past is focused on designing pedagogical, social
and technical aspects of asynchronous online learning experiences. Students
are reticent to participate in online groups especially. Through explicit
design of the different learning dimensions I have had a good deal of success
teaching students in an online setting.