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About My Research







Sean P. Goggins, Ph.D

Research Statement

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.

 

Group Informatics

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
socio-technical contexts.  The identity of the group is maintained through technology; and group informatics research demonstrates how identity
corresponds to structures revealed through network analysis of log files (Goggins, Laffey, Galyen, & Mascaro, 2011). 

 

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
data analysis with analysis of other, triangulating data specific to that application.

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.

 

Facebook:

Online Political Discourse

Group Informatics is applied broadly to areas of inquiry
where new kinds of social relations are emerging through computerization.  Political
action is one example of a domain that is increasingly rooted on the
Internet. During the 2010 election season, 73% of adults in the United States
used the Internet to acquire political information and 23% of individuals
used a social network site, such as Facebook or Twitter, to actively
participate in the political process (Purcell, Rainie, Mitchell, Rosenstiel,
& Olmstead, 2010). This represents a significant increase from 2008,
illustrating that technology plays an increasingly significant role in
engaging citizens in the political process, even in a midterm election.

 

With the increased reliance on technologically mediated
interactions in the political process, it is important to understand how
groups that exist in online social networks function. Group leaders and
candidate staff play an important agenda setting role within the group that
dictates discourse within a group. To date, there has been a significant
amount of research on agenda setting and framing in traditional media (McCombs
& Ghanem, 2001; McCombs & Shaw, 1972; Purcell et al., 2010) but there
has been limited examination of the agenda setting activities of online
groups. These activities by administrators of the groups have a significant
effect on the discourse that occurs within the group as a result of how the
topics are presented.

 

Political groups on social media sites are often viewed as
being analogous to political groups in the physical world (Freelon, 2010).
Researchers have debated the similarities and differences of political
interactions online and offline and there are still significant questions
about the manner in which the Internet facilitates political discussion (Wojcieszak
& Mutz, 2009). Though research has begun to examine how political groups
function online, there are a number of contributing factors to outcomes of
the group such as technology selection and leadership involvement in the
discourse.

 

Software Engineering

Group Informatics can be applied to questions of invisible
coordination as well as questions of political discourse if the proper
theoretical framing is used.  The coordination of concurrent activities by
multiple developers remains problematic for software development
organizations (Kraut & Streeter, 1995). Software engineering pioneers
such as Parnas (1972) and Brooks (1995) recognized the importance of
efficiently managing work dependencies and coordination overhead arising
within a development team. Such dependencies are becoming more critical as
software organizations become larger and more distributed (Cataldo, Bass,
Herbsleb, & Bass, 2007; Cataldo, Herbsleb, & Carley, 2008; Cataldo,
Mockus, Roberts, & Herbsleb, 2009).

 

Dependencies between tasks often result in Coordination
Requirements (CRs) among team members. Cataldo et al. (Cataldo et al., 2007;
Cataldo et al., 2008; Cataldo et al., 2009) introduced a framework to detect
and quantify CRs between pairs of software developers by identifying the
technical dependencies between software artifacts modified during their
assigned tasks. This formalization of CRs led to the definition of
Socio-Technical Congruence (STC) in software development. STC is an index
that measures the degree to which actual acts of coordination mirror
coordination requirements. Empirical studies suggest that high levels of STC
are beneficial: when coordination activities focus on the empirically
identified CRs, productivity is likely to improve.

 

Current methods for detecting CRs and calculating
socio-technical congruence have two serious drawbacks. First, CRs are
identified by mining the source control repository of the project for changes
to artifacts committed by a developer. This type of data is typically
available only towards the end of the development work for a task. Second,
for each file committed to a source code repository, a developer may have
consulted several other files. Knowledge of this source code reference
behavior is inaccessible from commit records.

 

Without a “live” view of activities, CRs and STC are not
actionable devices for managing coordination in software projects. Several potential
applications for such a live view have been discussed. Ehrlich et al. have
elaborated a way to rank CRs in a project, enabling prioritization of those
whose resolution can improve STC the most.  This thread of my research
program involves close partnership with colleagues in computer science,
particularly Peppo Valetto at Drexel.  My own experience leading technology
teams also informs the questions I ask.

 

Emergent Leadership &
Disaster Relief

Some coordination problems are well
suited to exploratory analysis using Group Informatics methods.  The US
Navy’s relief effort following the Haiti Earthquake on January 12, 2010 is
one example where the methods I have developed bring insight and
understanding to coordination work when the physical and virtual worlds
interact. 

 

Effective coordination and information
sharing in disaster situations is a matter of life and death.  Social media,
first responders, non-governmental organizations (NGOs) and both civil and
military government organizations cooperate to respond to and manage the
crisis that follows any disaster.  Improving information sharing and
coordination between government agencies with significant resources, NGOs and
affected civilians will save lives by saving time and improving the information
available for decision makers about resource distribution. The All-Partners
Access Network (APAN) is a US Navy sponsored electronic forum that enables
coordination between US Government Agencies – principally the Navy – and
individuals that do not have access to government systems, such as NGOs.

 

Prior work in CSCW focuses on understanding and designing
social and participatory media to help members of the public contribute to
disaster response in a coordinated manner (Palen & Liu, 2007; Palen,
Vieweg, Liu, & Hughes, 2009). Similar coordination gaps exist between
government resources like the US Navy and NGOs during a crisis.  Connecting
government resources & information to NGO resources in a crisis includes
logistical, medical and transportation asset focused communication.  Unlike
current modes of citizen participation in relief efforts, Government-NGO
coordination and information sharing relies on conventional, “walled garden”
discussion forums like APAN.  In a walled garden, access is vetted. In this
way, information is at once more trustworthy and less available to those
outside the network. 

 

Emergent leadership is a construct that Group Informatics
seeks to uncover from electronic trace data across a variety of domains. 
Disaster relief and recovery, as a specific domain, holds a good deal of
promise for examining these phenomena, and also for having a positive impact
on the world.

 

 

New Research Directions

Toward A Theory of Group Informatics

The introduction of new technologies that enable peer-peer
interaction has dramatically changed the way that individuals communicate and
form groups.  My analysis of dozens of online small groups over the past five
years recognizes key differences between online groups and face to face
groups.  First, online groups are more easily formed online than in the
physical world (Yuan & Gay, 2006). Online groups multitask more than face
to face groups (Goggins, Laffey, & Tsai, 2007a), experience lower social
presence and describe becoming part of an online group as an experience
incommensurate with the experience of becoming a “real group” (Goggins et
al., 2007a; Goggins et al., 2007b).  Among other differences, we know that
online groups represent a looser type of affiliation (Kling & Courtright,
2004).  Despite the apparent differences between face to face groups and
online groups, prior research examines technologically mediated small groups
using theories of social organization that emerge principally out of group
studies in the physical world. Therefore, there is a need to reconceptualize
how we come to understand the social structures of groups that exist online (Goggins
& Mascaro, 2011; Goggins et al., 2011; Mascaro & Goggins, 2011).  To
frame such a reconceptualization, I propose a model for examining
technologically mediated groups, which I refer as Group Informatics. 

 

To facilitate systematic study of technologically mediated
groups, Group Informatics contributes a model and method to address the
entanglement of human experience in physically co-located groups with new,
technologically mediated forms of social engagement.  This begins with an
understanding that online and face to face social arrangements are materially
different.  When we enter a room we quickly develop a sense of the room as we
are able to see the physical position, proximity, gaze direction, stance, and
facial expressions of those already there (Lea & Spears, 1992).  This
sense includes visual and auditory cues about whether this will be a
welcoming place, who we might like to approach, who intimidates us and which
conversations may be safe to enter as an outsider.  Entering an online class,
public discussion forum, disaster relief coordination space, health &
fitness site, online dating service, or even a Facebook Group each create a
different opportunity to orient oneself to the others who are present.

 

Each social and technical environment dynamically
constructs its context (Dourish, 2004) through member interaction at
different levels. In public online environments, it is difficult to identify
who else is “there”, how often they come, and their purpose.  In walled
gardens, group structures may be prescribed (though not always followed), yet
purpose may still vary from group to group.   Collaboration is a vital skill
in the 21st Century; but the tools and forms of social
organization within which collaboration occurs are as dynamic as context.

 

Group Informatics provides an integrated framework for
analysis of online groups, where electronic trace data is one type of data
that is input into a model.  This enables the researcher to transform
technical, log records of interaction to coherent and valid socio-technical
interaction records across a range of socio-technical contexts.  Simply
examining electronic trace data without grounding in a model like the Group Informatics
model leads to issues of theoretical coherence, validity and reliability (Howison
et al., under fourth review).  Too often, research of online phenomena
conflates the technical artifacts with the social experience of
participants.  I present a solution.  Application of the Group Informatics
model and method systematically builds understanding of technologically
mediated groups across a range of contexts by ensuring clusters or networks
of interaction, derived from electronic trace data, are not conflated with
social networks.  Group Informatics avoids this pitfall by defining a model
and method for analysis of electronic trace data that integrates interview,
survey and ethnographic data and analyzes it using methods from ethnography and
grounded theory.  I further expand the richness of our understanding with
abductive, content analysis methods described by Krippendorff (2004) to
identify theoretically grounded constructs like political discourse, learning
and coordination.  The triangulated analyses are then used to drive focused,
theoretically and empirically grounded analysis of electronic trace data.

 

Together, the model and method for Group Informatics
provide a foundation for the development of new theories for new forms of
social organization through the systematic, empirical analysis of
technologically mediated group behavior.  Instead of numerous one time
studies of different contexts, my approach enables the comparison and
analysis of group behavior across socio-technical contexts.

 

Rural IT Workers:  Physical, Informational and Cultural
Distance

This thread of my research is embodied in a single paper
reporting on a three year ethnography.  The length of data gathering,
analysis and writing for this thread of my research is a testament to the
enduring interest I have in bridging people, communities and groups from
isolation into participation.  In the paper, presently (December, 2011) under
second review at The Information Society, I synthesize social informatics
literature with literature on regional studies to frame an examination of the
social impacts of ICT uptake and use on the experience of distance in a rural
technology firm.  Though distance is much talked about and regarded as a
central component of distributed work, the distinct ways that distance is
experienced and influences collaboration in contexts outside of large, urban
centered firms is little explored (we actually found no prior studies). The
case study sheds light on the effects of ICT use on distance and
collaboration in a rural technology firm; bringing new knowledge into the
social informatics literature, and contributing a new perspective on distance
to the literature on collaboration. In the first part of our results, I
describe the unique geographical, technical and collaboration practice facets
of a rural technology firm.  In the second part of our results, I frame these
facets as contributing factors in the rich experience of distance in a rural
technology firm.  There are three experiences of distance described:
physical, informational and cultural.  I then synthesize the findings to
reconstruct the role of distance and our understanding of the social impacts
of ICT uptake and use to overcome distance.

 

CSCL@Work

This is the newest thread of my research program, and
emerges from a two plus year collaboration with Isa Jahnke, presently a
professor at Umeå University in Sweden, where I visited in the fall of 2011. 
Computer-Supported Collaborative Learning at Work – CSCL@Work – bridges the
knowledge of CSCL researchers, who are focused on learning, to the domain of
workplace learning. CSCL@Work research, as we conceptualize it, aims to
understand how organizations create the knowledge they require when that
knowledge is not already known within the organization. We are proposing a
framework for research focused on knowledge sharing by looking closely at the
process of knowledge sharing, and defining CSCL@Work as a mechanism for making
learning practices visible
, and centering research on the collaborative
creation of new knowledge.  In other words, CSCL@Work frames a new area of
inquiry, focused on making collaborative learning in the workplace
explicit through social media and other collaborative technologies integrated
into workplaces.

 

Creating a culture of learning within the organization was
the focus of Organizational Learning, beginning with Argyris & Schoen (Argyris
& Schon, 1974) and continuing through its development by Brown &
Duguid (2000, 1991) and others. Historically, knowledge management solutions
focus on the capture, cataloguing and retrieval of information and work
processes to promote the findability of known information within an
organization.

 

But what do firms do when the answer is not known, the
problem is not yet framed, or there are no existing solutions? For example,
traditional book and newspaper publishers lose customers and authors in the
Social Media age.  Some publishers have adapted by adopting social media and
blogging strategies, but these solutions did not emerge from knowledge
management systems, which are insufficient for acquiring new knowledge.  When
an industry goes through these types of fundamental changes, entire
workforces need to learn new methods and approaches for performing their
work.  To accelerate this process, CSCL@Work asks, how can collaborative
learning
be supported explicitly in the workplace?

 

Learning from the past is not enough to
help stakeholders accomplish their tasks and practices” (dePaula &
Fischer, 2005). In this new world, “knowledge is not a commodity to be
consumed but is collaboratively designed and constructed in the doing
of work
(dePaula & Fischer, 2005) (p. 30).
What sounds simple is often implicitly done instead of designing solutions
for collaborative knowledge construction as an explicit way of learning
.
Some firms even avoid the term “learning”. A few large technology firms built
and use interactive learning environments (SAP, Ideo, Google, Apple), but a
greater number of firms do not focus on fostering collaborative
learning in the workplace.  Learning is not made into a visible, integrated
part of work processes. 

 

Technology solutions are one component of supporting
work-based learning.  While there are a lot of new technologies making
collaboration through Social Media outside of work more common (e.g., social
networking systems, Blogs), most organizations do not yet focus clearly on
using technologies like these to foster learning in general or collaborative
learning, specifically. New kinds of knowledge management systems – reframed
as CSCL@Work systems – might contribute to this. 

 

The basic questions for industrial and information-society
firms include, a) are they able to create new knowledge when the answer to a
problem is not available?  and, b) what concepts of collaborative learning
exist and are they supported?  Reframing work as an active learning activity
is a significant challenge for firms that need to adapt quickly in a dynamic
world (EasterbySmith, Lyles,
& Peteraf, 2009). We argue that new concepts of learning – supported by
new technologies – at the workplace and a new understanding of work are
required to foster a work-based learning culture for creative thinking,
creative actions and innovations.  To make progress toward these important
goals, we propose a CSCL@Work research agenda at the boundary between
research on knowledge management, CSCW and CSCL. 

 

 

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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.  

 

 

  • Apr 3 2012