A framework for conceptualizing, representing,
and analyzing distributed interaction
Daniel D. Suthers & Nathan Dwyer & Richard Medina &
Ravi Vatrapu
Received: 26 June 2009 /Accepted: 7 December 2009
# International Society of the Learning Sciences, Inc.; Springer Science+Business Media, LLC 2010
Abstract The relationship between interaction and learning is a central concern of the
learning sciences, and analysis of interaction has emerged as a major theme within the
current literature on computer-supported collaborative learning. The nature of technology-
mediated interaction poses analytic challenges. Interaction may be distributed across actors,
space, and time, and vary from synchronous, quasi-synchronous, and asynchronous, even
within one data set. Often multiple media are involved and the data comes in a variety of
formats. As a consequence, there are multiple analytic artifacts to inspect and the interaction
may not be apparent upon inspection, being distributed across these artifacts. To address
these problems as they were encountered in several studies in our own laboratory, we
developed a framework for conceptualizing and representing distributed interaction. The
framework assumes an analytic concern with uncovering or characterizing the organization
of interaction in sequential records of events. The framework includes a media independent
characterization of the most fundamental unit of interaction, which we call uptake. Uptake
is present when a participant takes aspects of prior events as having relevance for ongoing
activity. Uptake can be refined into interactional relationships of argumentation,
information sharing, transactivity, and so forth for specific analytic objectives. Faced with
the myriad of ways in which uptake can manifest in practice, we represent data using
graphs of relationships between events that capture the potential ways in which one act can
be contingent upon another. These contingency graphs serve as abstract transcripts that
document in one representation interaction that is distributed across multiple media.
Computer-Supported Collaborative Learning
DOI 10.1007/s11412-009-9081-9
D. D. Suthers (*) : N. Dwyer : R. Medina
Laboratory for Interactive Learning Technologies, Department of Information and Computer Sciences,
University of Hawai‘i at Manoa, 1680 East West Road, POST 309, Honolulu, HI 96822, USA
e-mail: collaborative-representations@hawaii.edu
e-mail: suthers@hawaii.edu
URL: http://lilt.ics.hawaii.edu
R. Vatrapu
Center for Applied ICT, Copenhagen Business School, Howitzvej 60, 2.floor,
Frederiksberg DK-2000, Denmark
e-mail: vatrapu@cbs.dk

This paper summarizes the requirements that motivate the framework, and discusses the
theoretical foundations on which it is based. It then presents the framework and its
application in detail, with examples from our work to illustrate how we have used it to
support both ideographic and nomothetic research, using qualitative and quantitative
methods. The paper concludes with a discussion of the framework’s potential role in
supporting dialogue between various analytic concerns and methods represented in
CSCL.
Keywords Theoretical and methodological framework . Interaction analysis . Distributed
learning . Uptake . Contingency graphs
Introduction
Researchers, designers, and practitioners in the learning sciences and allied fields study a
variety of technology-supported settings for learning. These settings may include tightly
coupled small group collaboration, distributed cooperative activity involving several to
dozens of persons, or large groups of loosely linked individuals. Examples include
asynchronous learning networks (Bourne et al. 1997; Mayadas 1997; Wegerif 1998),
knowledge building communities (Bielaczyc 2006; Scardamalia and Bereiter 1993), mobile
and ubiquitous learning environments (Rogers and Price 2008; Spikol and Milrad 2008),
online communities (Barab et al. 2004; Renninger and Shumar 2002), and learning in the
context of “networked individualism” (Castells 2001; Jones et al. 2006). These settings are
diverse in many ways, including the degree of coupling between participants’ activities,
varying temporal and social scales, and the supporting technologies used. However, they all
rely on interaction to enhance learning. “Interaction” is used here in a broad sense,
including direct encounters and exchanges with others and indirect associations via
persistent artifacts that lead to individual and group-level learning. The common element is
how participants benefit from the presence of others in ways mediated by technological
environments.
The distributed nature of interaction in technology-mediated learning environments
poses analytic challenges. Interaction may be distributed across actors, media, space, and
time. Mixtures of synchronous, quasi-synchronous, and asynchronous interaction may be
included, and relevant phenomena may take place over varying temporal granularities.
Participants may be either co-present or distributed spatially, and often multiple media are
involved (e.g., multiple interaction tools in a given environment, or multiple devices).
Furthermore, the data obtained through instrumentation comes in a variety of formats.
There may be multiple data artifacts for analysts to inspect and share, and interaction may
not be immediately visible or apparent, particularly when interaction that is distributed
across media is consequentially recorded across multiple data artifacts. Interpretation of
this data requires tracing many individual paths of activity as they traverse multiple tools
as well as identifying the myriad of occasions where these paths intersect and affect each
other.
Other analytic challenges are also exacerbated by technology-mediated interaction.
Human action is contingent upon its context and setting in many subtle ways. These
contingencies take new forms and may be harder to see in distributed settings. Interpreting
nonverbal behavior is also a challenge. When users of a multimedia environment
manipulate and organize artifacts in ways implicitly supported by the environment,
it may be difficult to determine which manipulations are significant for meaning
D.D. Suthers, et al.

making. The large data sets that can be collected in technology-mediated settings lead
to tensions between the need to examine the sequential organization of interaction
within an episode and the need to scale up such analyses to more episodes and larger
scale organization. We are challenged to understand phenomena at multiple temporal
or social scales, and to understand relationships between phenomena across scales
(Lemke 2001). See Suthers and Medina (2010b) for further discussion of these analytic
challenges.
We have encountered many of these challenges in our own research. This research
includes a diverse portfolio of studies of co-present and distributed interaction, via various
synchronous and asynchronous media, and at scales including dyads, small groups, and
online communities. Our research methods have included experimental studies (Suthers and
Hundhausen 2003; Suthers et al. 2008; Vatrapu and Suthers 2009), activity-theoretic
and narrative analysis of cases (Suthers et al. 2007e; Yukawa 2006), adaptations of
conversation analysis (Medina and Suthers 2008; Medina et al. 2009), and hybrid methods
(Dwyer 2007; Dwyer and Suthers 2006). Through the diversity of our work, we have come
to appreciate that the analytic challenges outlined above are not specific to one setting or
method, and we have been motivated to find a solution that gives our work conceptual
coherence rather than solutions that are specific to one type of environment and/or type of
analysis.
In order to address these challenges in a principled way, we developed the uptake analysis
framework for conceptualizing, representing, and analyzing distributed (technology-
mediated) interaction. We offer that framework in this paper in hopes that some aspects
of it may also be useful to others. The representational foundation of this framework is an
abstract transcript notation—the contingency graph—that can unify data derived from
various media and interactional situations and has been used to support multiple analytic
practices. The conceptual foundation of this framework includes uptake as a fundamental
building block of interaction, and the basis for construing interaction as an object of study.
Like any analytic framework, the uptake analysis framework carries theoretical assump-
tions. However, it is not primarily a theory: It provides a theoretical perspective on how to
look at interaction, but it does not provide explanations or make predictions. Nor is it
primarily a single method: It is a coordinated set of concepts and representations with
associated practices that support multiple methods of analyzing distributed interaction.
These distinctions are why we call it a “framework.”
This paper begins by elaborating on our motivations and requirements in the next
section. The following section presents the conceptual, empirical, and representational
foundations of the uptake analysis framework. We then detail practical aspects of
applying the framework, and provide selected examples from our work to illustrate how
it supports several types of analyses with multiple data sources. After a summary and
discussion of limitations and extensions, we conclude with a discussion of its potential
role in supporting dialogue between various analytic concerns and practices represented
in CSCL.
Motivations and requirements
This work had its origins in our recognition of the analytic limitations of our prior
work and our attempts to reconcile the strengths and weaknesses of two methodological
traditions. The first author’s earlier research program tested hypotheses concerning
“representational guidance” for collaborative learning in experimental studies where
Computer-Supported Collaborative Learning

participants’ talk and actions were coded according to categories relevant to the
hypotheses, and frequencies of these codes were compared across experimental groups
(Suthers and Hundhausen 2003; Suthers et al. 2003, 2008). While these studies suggested
that representational influences were present, the statistical analyses as they were
conceived did little to shed light on the actual collaborative processes involved and,
hence, of the actual roles that the representations played. To address this problem, we
began several years of analytic work to expose the practices of mediated collaborative
learning in data from our prior experimental studies, beginning with microanalytic
approaches inspired by the work of Tim Koschmann, Gerry Stahl, and colleagues
(Koschmann et al. 2004, 2005). In an analysis undertaken in order to understand how
knowledge building was accomplished via synchronous chat and evidence mapping tools,
we applied the concept of uptake to track interaction distributed across these tools
(Suthers 2006a). Subsequently, we began analyzing asynchronous interaction involving
threaded discussion and evidence mapping tools (Suthers et al. 2007b). In conducting this
work, we encountered limitations of microanalytic methods, discussed below. In
response, we developed our analytic framework to handle the asynchronicity and
multiple workspaces of our data, and with hopes of scaling up interaction analysis to
larger data sets (Suthers et al. 2007a). Concurrently, we were pursuing a separate line of
work on analyzing participation in online communities through various artifact-mediated
associations (Joseph et al. 2007; Suthers et al. 2009). This work further motivated the
development of a way of thinking about mediated interaction that would inform and unify
the diverse studies that we were conducting. In this section, we discuss several recurring
concerns that arose, including addressing the respective strengths and weaknesses of
statistical and micro-genetic interaction analyses, and handling the diverse data derived
from distributed settings in a manner that supports multiple approaches to understanding
the organization of interaction.
Statistical analysis
Many empirical studies of online learning follow a paradigm in which contributions (or
elements of contributions) are annotated according to a well-specified coding scheme (e.g.,
De Wever et al. 2006; Rourke et al. 2001), and then instances of codes are counted up
for statistical analysis of their distribution (e.g., across experimental conditions).
Research in this tradition is nomothetic, seeking law-like generalities, and, in
particular, is typically oriented toward hypothesis testing. This approach has significant
strengths. Coding schemes support methods for quantifying consistency (reliability)
between multiple analysts. Well-defined statistical methods are available for comparing
results from multiple sources of data such as experimental conditions and replications
of studies. Also, it is straightforward to scale up statistical analysis by coding more
data.
A limitation is that these practices of coding and counting for statistical analysis obscure
the sequential structure and situated methods of the interaction through which meaning is
constructed (Blumer 1986). Coding assigns each act an isolated meaning, and, therefore,
does not adequately record the indexicality of this meaning or the contextual evidence on
which the analyst relied in making a judgment. Frequency counts obscure the sequential
methods by which media affordances are used in particular learning accomplishments,
making it more difficult to map results of analysis back to design recommendations.
Another limitation is that in common practice statistical significance testing is applied to
preconceived hypotheses to be tested rather than oriented toward discovery. An analysis of
D.D. Suthers, et al.

interaction might help researchers discover what actually happened that led to the statistical
results—whether statistical significance was obtained as predicted, obtained in patterns that
were not predicted, or absent. Such an analysis is only possible if the data was recorded in a
form that retains its interactional structure. Our framework is intended to support statistical
analysis in two ways: by providing sequential structures (as well as single acts) that can be
coded and counted, and by recording these structures for interaction analysis that helps
make sense of statistical results.
Sequential analysis
Several analytic traditions find the significance of each act in the context of the unfolding
interaction. These traditions include Conversation Analysis (Goodwin and Heritage 1990;
Sacks et al. 1974), Interaction Analysis (Jordan and Henderson 1995), and Narrative
Analysis (Hermann 2003). Some of these traditions (especially the first two cited) draw
upon the assertion that the rational organization of social life is produced and sustained in
participants’ interaction (Garfinkel 1967). A common practice is microanalysis, in which
short recordings of interaction are carefully examined to uncover the methods by which
participants accomplish their objectives. Microanalysis is becoming increasingly important
in computer-supported collaborative learning because a focus on accomplishment through
mediated action is necessary to truly understand the role of technology affordances (Stahl et
al. 2006). For examples applied to the analysis of learning, see Baker (2003), Enyedy
(2005) Koschmann and LeBaron (2003), Koschmann et al. (2005), Roschelle (1996), and
Stahl (2006, 2009).
Microanalysis has somewhat complementary strengths and weaknesses compared to
statistical analysis. It documents participants’ practices by attending to the sequential
structure of the interaction, producing detailed descriptions that are situated in the medium
of interaction. Yet analyses are often time consuming to produce, and are difficult to scale
up. As a result, microanalysis is usually applied to only a few selected cases, leading to
questions about representativeness or “generality” (but see Lee and Baskerville 2003, for
arguments against basing generalization solely on sampling theory). Microanalysis is most
easily and most often applied to episodes of synchronous interaction occurring in one
physical or virtual medium that can be recorded in a single inspectable artifact, such as a
video recording or replayable software log. Distributed interaction may occur in more than
one place, and learning may take place over multiple episodes, problematizing approaches
that assume that a single analytic artifact recorded in the medium of interaction is available
for review and interpretation.
The family of methods loosely classified as exploratory sequential data analysis
(ESDA, Sanderson and Fisher 1994) provide a collection of operations for transforming
data logs into representations that are successively more suitable for analytic
interpretation. In Sanderson and Fisher’s (1994) terms, the operations are chunking,
commenting, coding, connecting, comparing, constraining, converting, and computing.
ESDA draws on computational support for constructing statistical and grammatical
models of recurring sequential patterns or processes (e.g., Olson et al. 1994). Because of
this computational support, ESDA can be scaled up to large data sets while still attending
to the sequential structure of the data. On these points, ESDA compares favorably to the
respective limitations of microanalysis and “coding and counting.” However, like
statistical analysis, computational support risks distancing the analyst from the source
data. Another limitation is that many of the modeling approaches use a state-based
representation that reduces the sequential history of interaction to the most recently
Computer-Supported Collaborative Learning

occurring event category. Reimann (2009) presents a cogent argument for basing process
analysis on an ontology of events rather than variables, and describes Petri net process
models (from van der Aalst and Weijters 2005) that capture longer sequential patterns
than state transitions. These approaches will be discussed further at the end of the paper.
Our framework is intended to support both distributed extensions of microanalysis and
ESDA approaches.
Media generality
Some analytic traditions use units of analysis and data representations that are based
on the interactional properties of the media under study. Much of the foundational
work in sequential analysis of interaction has focused on spoken interaction. The
difficulty of speaking while listening and the ephemerality of spoken utterances
constrain communication in such a manner that turns (Sacks et al. 1974) and adjacency
pairs (Schegloff and Sacks 1973) have been found to be appropriate units of interaction
for analysis of spoken data. These units of analysis are not as appropriate for interactions
in media that differ in some of their fundamental constraints (Clark and Brennan 1991).
For example, online media may support simultaneous production and reading of
contributions, or may be asynchronous, and contributions may persist for review in
either case. Consequentially, contributions may not be immediately available to other
participants or may become available in unpredictable orders, and may address earlier
contributions at any time (Garcia and Jacobs 1999; Herring 1999). It is not appropriate to
treat computer-mediated communication as a degenerate form of face-to-face interac-
tion, because people use attributes of new media to create new forms of interaction
(Dwyer and Suthers 2006; Herring 1999). Because conceptual coherence of a set of
contributions can be decoupled from their temporal or spatial adjacency, our framework is
based on a unit of interaction that does not assume adjacency or other media-specific
properties.
Similarly, properties of distributed interaction place different demands on representations
of data and analytic structures. Because technology-mediated interaction draws on many
different semiotic resources, analysis of interactional processes must reassemble interaction
from the separate records of multiple media, while also being sensitive to the social
affordances of each specific medium being analyzed to distinguish their roles. A framework
for analysis of mediated interaction must be media agnostic—independent of the form of
the data under analysis—yet media aware—able to record how people make use of the
specific affordances of media. This is required to allow analysis to speak to design and
empirically drive the creation of new, more effective media. Our framework provides a
means of gathering together distributed data into a single representation of interaction that
does not make assumptions about media properties but indexes back to the original media
records.
Impartiality
Any analytic program must be based on theoretical assumptions concerning what kinds of
questions are worthwhile and what counts as data. Transcripts carry some of these
theoretical assumptions (Ochs 1979), but this bias is not a fait accompli: We can actively
shape the role of transcripts as representations in our analytic practices (Duranti 2006). We
believe that analytic representations should minimize assumptions concerning the answers
to the research questions posed, limiting assumptions to those necessary to ask those
D.D. Suthers, et al.

questions in the first place. This desideratum applies to basic analytic constructs such as the
choice of units of data to be analyzed (segmentation) and the fundamental relationships by
which we characterize interaction. Because we are analyzing and theorizing about
interaction from diverse settings, we want our data and analytic representations to support
variable and multi-leveled granularities, and our basic unit of interaction to be neutral
toward possible interpretations of that interaction.
In summary, the considerations discussed in this section led us to address our
practical analytic problems by developing an approach that records the sequential and
situational context of activity so that an account of the interactional construction of
meaning is possible, and does not pre-specify the interactional properties of the medium
of interaction (e.g., synchronicity, availability of contributions and their production,
persistence) but records these properties where they exist. Additionally, the approach is
sufficiently formalized to enable computational support for analysis (including
statistical and sequential analysis) and captures aspects of interaction in a manner that
impartially informs research questions concerning how the sequential organization of
activity leads to learning. The analytic framework we developed to meet these
requirements draws on other interaction analysis methods, but uses a generalized
concept of the unit of interaction and a data representation that is independent of any
particular medium.
The remainder of the paper first describes the conceptual, empirical, and representational
foundations for our analytic framework before turning to examples of how it is constructed
and used. Readers who prefer to begin with examples are invited to skip to those sections
after reading the brief overview section below, but are warned that the examples are
presented in terms of the framework they are intended to illustrate, so some prior
introduction to this framework is a prerequisite.
The uptake analysis framework
The framework we developed assumes an analytic concern with uncovering or
characterizing the organization of interaction in records of events. The framework offers
conceptual foundations (units of action and interaction that are inclusive of a range of
phenomena in distributed interaction); empirical foundations (observed events and
relationships between them that evidence these phenomena); and representational
foundations (an abstract transcript that captures this evidence in a unified analytic artifact
and that supports multiple analytic practices). These foundations for analysis are presented
in detail in this section, after a brief overview.
Overview
The framework is layered to make certain distinctions in analytic practice explicit. Given a
data stream of events, analysts select certain events as being of significance for analysis
(ei bottom of Fig. 1). Some of the events may be environmentally generated events, and
some of the events are points at which actors in the interaction coordinate between personal
and public realms. Next, the analyst identifies empirically grounded relationships between
events that provide potential evidence for interaction. We call these relationships
contingencies. Contingencies between events are represented in abstract transcripts that
we call contingency graphs. Contingencies indicate how acts are manifestly related to each
other and their environment. The analyst interprets sets or patterns of contingencies as
Computer-Supported Collaborative Learning

evidence for interaction. We propose the concept of uptake as an analytic way station in this
process of interpretation. An assertion that there is uptake is an assertion that a participant
has taken aspects of prior events as having relevance for ongoing activity. This assertion is
made more concrete in ways specific to analytic traditions, interpreting uptake as
recognizable activity (top of Fig. 1) in a manner that is grounded in specific actions and
the relationships between them.
To summarize, events and contingencies between them are the empirical foundations of
the uptake analysis framework; graphs representing events as vertices and contingencies as
edges are the representational foundation of this framework; and uptake between
coordinations is the conceptual foundation for identifying interaction in this framework.
In using the terms “coordination,” “contingency,” and “uptake,” we are collecting together
and clarifying concepts about interaction that exist in current theory and analytic practice.
These concepts are discussed in more detail below and are summarized in Table 1. We
begin with discussion of conceptual foundations, as this motivates the empirical and
representational foundations.
Conceptual foundations: Inclusive units of action and interaction
The conceptual foundations for the framework include concepts of action and interaction
that generalize from existing analytic concepts to factor out assumptions about the setting.
Fig. 1 Analytic schema
Table 1 Summary of framework levels and elements
Empirical foundation
Events Observed changes in the environment
Contingencies Manifest relationships between events (see Table 2)
Representational foundation (abstract transcript)
Vertices Represent, annotate and index to source data for events
Hyperedges Represent, annotate and index to source data for contingencies
Conceptual foundation
Coordinations Acts in which an agent coordinates between personal and public realms
Uptake Taking aspects of other coordinations as having certain relevance for ongoing activity
D.D. Suthers, et al.

Events, acts, and coordination The framework assumes that analysis begins with records of
events that are characterized in terms of observable features such as changes in the
environment and their temporal and spatial locales. These events may include acts—those
events due to the agency of a specified, and for our purposes human, actor—and events
involving nonhuman actants (Latour 2005).
Many analyses of collaborative learning are particularly interested in acts by which
participants coordinate between personal and public realms, including with each other. The
term coordination is taken from the distributed cognition account of “coordination of [not
necessarily symbolic] information-bearing structures” between personal and public realms
(Hutchins 1995, p. 118). Whereas distributed cognition postulates bringing internal and
external representations into alignment, the concept of coordination can also be understood
as the intentionality that marks the divide between the agency of objects postulated by
actor-network theory (Latour 2005, p. 62ff) and the object-oriented agency of human actors
postulated by activity theory (Kaptelinin and Nardi 2006, section 9.2). However, the
framework outlined in this paper does not require assumptions about the nature of the
personal realm. We accept that some analytic traditions may identify relevant acts without
postulating cognitive representations or inferring intentionality.
Other literature uses the term contribution, but we desire a term that does not imply a
conversational setting, and that is not biased toward production as the only kind of relevant
action. For example, when a participant reads a message the personal realm is brought into
coordination with inscriptions in the message, and when the participant writes a message,
inscriptions are created in the public realm that are coordinated with the personal realm. In
previous writings, we used the term media coordination, because all interaction is mediated
by physical and cultural tools (Wertsch 1998), whether in ephemeral media such as thought,
vocalizations, and gesture, or persistent media such as writing, diagrams, or electronic
representations. The adjective media is dropped herein because it is redundant. The concept
of coordination is relevant to Vygotsky’s developmental view of learning as the
internalization of interpsychological functions (Vygotsky 1978), although these two ideas
are at different time scales.
Activity theory postulates three levels of activity: operations, actions, and activity
(Kaptelinin and Nardi 2006, section 3.4). Coordinations correspond most closely to the
level of action, lying between events generated at the operational level and the ongoing
activity that the analyst seeks to understand. Because of this correspondence, we will use
act as a synonym for coordination where it simplifies the prose. We use event when we
wish to include environmentally generated events or refer to the data stream of events
before specific events have been analytically selected as constituting coordinations.
Uptake Interaction is fundamentally relational, so the most important unit of analysis is not
isolated acts, but rather relationships between acts. The framework is based on a
relationship that underlines the various conceptions of interaction current in the CSCL
literature, but abstracts from assumptions about the format or setting of interaction.
Although there are many conceptions of how learning is social or socially embedded, each
of these forms of social learning is only possible when a participant takes something from
prior participation further. We call this fundamental basis of interaction uptake (Suthers
2006a, b). Uptake is the relationship present when a participant’s coordination takes aspects
of prior or ongoing events as having relevance for an ongoing activity. For example, in a
coherent conversation each contribution is interpretable as selecting some aspect of the
foregoing conversation, and, by foregrounding that aspect in a given way, bridging to
potential continuations of the conversation. Even more explicitly, a reply in a threaded
Computer-Supported Collaborative Learning

discussion demonstrates the author’s selection of a particular message as having certain
relevance for participation. But uptake can also be subtler. The aspects taken as relevant can
include not only expressions of information, but also attitudes and attentional orientation;
and their manifestations may be ephemeral as in speech or persistent as in writing or digital
inscriptions. Participants may take up others’ ways of talking about the matter at hand, or
may mimic representational practices, such as notational conventions or the organization of
objects in a workspace. Even the act of attending to another’s contribution is a form of
uptake. Thus, the concept of uptake supports diverse definitions of “interaction,” including
any association in which one actor’s coordination builds upon that of another actor or
actant. Uptake can cross media and modalities. Uptake conceptualizes relationships
between actions in a media-independent manner and potentially at multiple temporal or
spatial scales.
Uptake is transitive and transformative. Uptake is transitive in the grammatical sense that
it takes an object: Uptake is always oriented toward the taken-up as its object. Uptake
transforms that taken-up object by foregrounding and interpreting aspects of the object as
relevant for ongoing activity:Objekt becomes predmet (Kaptelinin and Nardi 2006, chapter 6).
Manifestations of this transformed object become available as the potential object of future
uptake in any realm of participation in which it is available (as discussed further below).
Therefore, uptake bridges to future activity. Uptake is transitive in the logical sense through
the composition of interpretations (Blumer 1986; Suthers 2006b). If uptake u1 transforms o1
into o2, and uptake u2 transforms o2 into o3, then o1 has been transformed into o3. More
importantly, the act of uptake u2 is taking up not only o2, but also taking up the
transformation o1—u1→o2 (the interpretation of o1 as o2), so u2 interprets the prior act of
interpreting o1. This is another way of saying that meaning making is embedded in a
successively expanding history.
A participant can take up one’s own prior expressions as well as those of others.
Therefore, uptake as a fundamental unit of analysis is applicable to the analysis of both
intrasubjective and intersubjective processes of learning. An act of uptake is available as
form of participation only within a realm of activity in which its transformed object is
manifest (e.g., visible, audible, or otherwise available to perception). An individual working
through ideas via mental processes and external notations has access to the transformed
objects of his or her mental uptake as well as those of acts in the external media, but in the
public realm only uptake that manifests via coordinations becomes available for further
uptake.
Related concepts Uptake is similar to several other relational units of interaction in the
literature, as it is intended to identify a more general conception that underlies them all. The
thematic connections of Resnick et al. (1993) are examples of uptake, although uptake
allows for nonlinguistic forms of expression, and for other kinds of interpretative acts in
addition to thematic or argumentative ones. Uptake has the advantage of being neutral with
respect to the type of relationships possible (not being limited to a given set of thematic
connections). An assertion that uptake is present postulates that a manifestation or trace of
prior action has been taken as having significance for further activity, but abstracts away
from what aspect of the prior action is brought forward, or what significance is attributed to
it. This means that uptake is only a step on the way to identification of theory-specific
relationships, for example, thematic connections or other interactional relationships
captured by coding schemes (e.g., Berkowitz and Gibbs 1979; De Wever et al. 2006;
Herring 2001; Rourke et al. 2001; Strijbos et al. 2006). However, unlike coding schemes,
D.D. Suthers, et al.

uptake meets the criterion of impartiality toward interpretations, so it can provide a
common foundation for comparison of different interpretations.
Uptake is related to but is broader than the concept of transactivity, which is often
defined as reasoning that operates on the reasoning of one’s partner, of one’s peers, or of
oneself (Azmitia and Montgomery 1993; Kruger 1993; Teasley 1997; Weinberger and
Fischer 2006). The transactivity literature focuses on interactional contexts in which a
contribution is explicitly directed toward an identified other, as in, for example, Berkowitz
and Gibbs’ (1979) coding categories for dyadic discussion. Uptake is broader in that it
includes situations where an actor takes up a manifestation of another actor’s coordination
without the necessity of either person knowing that the other exists, as happens in
distributed asynchronous networks of actors in which resources are shared. Taking-up need
not be directed at anyone. There are also differences in the analytic practices associated
with each concept. Some analysts, such as Berkowitz and Gibbs (1979) and Azmitia and
Montgomery (1993) who use their coding scheme, treat transactivity as a property of
individual utterances that can be identified by observing the other-directedness of the
utterance. Our proposal concerning uptake as an approach to analysis is relational. One
cannot assert uptake as a property of an individual act: It is evidenced by contingencies
between acts. However, the concepts of transactivity and uptake are compatible, with
uptake being inclusive of transactive relationships.
The relationship between uptake and the distinct conversation analytic concept of
preferences is worth a brief note. At a given moment in a conversation, speakers may elect
to continue the conversation in ways that differ in how they are aligned with the
immediately prior contribution, some being more aligned or “preferred” (Atkinson and
Heritage 1984; Schegloff and Sacks 1973). The meaning of the next utterance derives
partially from how it meets these expectations. In a conversational setting, uptake either
selects some aspect of the prior contribution as being relevant in a certain way, thereby
making a commitment (whether more or less preferred) concerning alignment to prior
contributions, or denies this relevance by instead taking up some other act as relevant. In
either case, a new set of preferences is offered based on the aspect of the prior act selected
as being relevant.
Epistemological utility, not ontological claim Although we have described uptake as
something that participants do, uptake is more accurately understood as an etic abstraction
used in the analytic practices of identifying interactionally significant relationships between
acts. From an emic perspective, participants do not engage in the abstract act of uptake;
they engage in specific acts that they affirm (through subsequent acts) as the
accomplishment of recognizable activity (Garfinkel 1967). Thus, from an ontological
standpoint (concerning the nature of the actual phenomenon), uptake provides an
inadequate account. However, from an epistemological standpoint (concerning the process
by which analysts come to know the phenomenon), uptake and its empirical support,
contingency, can be useful abstractions. For example, in a large data set, it may be useful to
identify the possible loci of interaction before constructing an analytic account of the
meaning of that interaction. As shown in Fig. 1, the analyst’s identification of uptake is a
bridge between empirical contingencies and further analysis. Uptake analysis is a proto-
analytic framework that must be completed by specific analytic methods motivated by a
given research program. The contingency graph, described next, provides another resource
for this analysis by offering potential instances of uptake and grounding analysis in
empirical events.
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Empirical and representational foundations: An abstract transcript
Although we are ultimately interested in analyzing interaction in terms of sequences of
uptake, one cannot jump immediately from raw data to uptake. Human action is deeply
embedded in, and sensitive to, the environment and history of interaction in many
ways, while only some of these contingent relationships enter into the realm of meaning
in which participants are demonstrably oriented toward manifestations of prior activity
as having relevance for ongoing participation. An analytic move is required to identify
those observable contingencies that evidence uptake, and accountability in scientific
practice requires that this analytic move be made explicit. This move is complicated
when interaction is distributed across media, as no recording of a single medium
contains all of the relevant data. Also, the complexity of potential evidence for uptake
and our desire to scale up analysis suggests that computational support is required.
Motivated by the need for a transcript representation that exposes interactional
structures in diverse forms of mediated interaction, and for a formal structure that is
amenable to computation, we developed the contingency graph. These empirical and
representational foundations for the practices of uptake analysis are described in this
section.
Events and coordinations Uptake analysis begins with selection of a set of observed
events. Events in general, rather than strictly coordinations, are included for two reasons:
First, data collection and computationally supported analysis may begin before
subsequent analysis identifies which events constitute coordinations; and second, actors’
coordinations may take up environmentally generated events that must be included to
understand those coordinations. Therefore, contingency graphs are defined over sets of
events that include but need not be limited to coordinations. Examples of coordinations
include utterances, electronic messages, and workspace edits. Later, we will see that
coordinations may be specified at larger granularities, for example, a sequence of moves
that creates a graphical arrangement of elements. Examples of events that are not
coordinations include display updates driven by environmental sensors or by coordina-
tions that took place on other devices. Events are represented in the formal contingency
graph by vertices, and are depicted by rectangular nodes in the figures (e.g., e1 and e2 in
Fig. 1 and e1…e4 in Fig. 2).
Contingencies If a coordination is to be interpreted as taking up a prior coordination or
event, then there must be some observable relationship between the two. Therefore, we
Fig. 2 Contingency graph
D.D. Suthers, et al.

ground uptake analysis in empirical evidence by identifying contingencies between
events. A contingency is an observed relationship between events evidencing how one
event may have enabled or been influenced by other events. The concept of contingency
recognizes that “there might exist many metaphysical shades between full causality and
sheer inexistence” (Latour 2005, p. 72) between events that underlie the myriad of ways
in which human action is situated in its environment and history. This situatedness is
not bounded arbitrarily: Relevant contingencies include spatially and temporally local
contingencies, but also can include non-local contingencies at successively larger
granularities (Cole and Engeström 1993; Jones et al. 2006; Suthers and Medina 2010a).
Contingencies can be found in media-level, temporal, spatial, inscriptional, and
semantic relationships between coordinations: These will be discussed in the next
section. Ideally, contingencies are based on manifest rather than latent relationships
between events (Rourke et al. 2001), and can be formally specified and mechanically
recognized.
Contingency graph The contingency graph is a directed acyclic graph consisting of events
and the contingencies between them on which we may layer analytic interpretations.
Formally, the contingency graph is a one-to-many directed hypergraph G = (V, E). The set
of vertices V is the set of events selected for analysis, and the set of directed hyperedges E
records all the prior events on which each event is directly contingent. E is a set of tuples
(eu, {e1, ... en}), ei ∈ V, where event eu is contingent on events e1 through en. For example,
the graph depicted in Fig. 2 consists of V = {e1, e2, e3, e4} and E = {(e3,{e1}), (e4,{e1,e2})}.
A contingency graph respects the chronology of events: If the subscripts are time stamps
under a partial ordering “>” then in each contingency (eu, {e1, ... en}), u > i, for i = 1, ... n.
In a normalized contingency graph, none of {e1, ... en} are contingent on each other.
(Formally, if (eu, {e1, ... en}) ∈ E, then for any two ex and ey in {e1, ... en}, there does not
exist a tuple (ey,{... ex ...}) in E.) Normalization keeps the size of tuples to the minimum
necessary and prevents redundant paths in the contingency graph, so that it is easier to
find all the prior events upon which a given event is directly contingent. In many of
our analyses, we partition V into {E0, C1 … Cm} according to which participant 1…m
enacted the coordination, with E0 reserved for events by nonhuman actants. If some of
{e1, ... en} were by a different participant than eu (i.e., one of e1 ... en is in a different
partition than eu), then there are intersubjective contingencies, and the potential for
collaboration exists.
The contingency graph is an abstract transcript representation. By calling it “abstract,”
we emphasize two things. First, all transcripts are abstractions of the events themselves, but
contingency graphs abstract further from media-specific transcript formats to a common
format. Second, the contingency graph is a formal object. It should not be confused with
implementations. One need not construct the entire contingency graph for a given data set;
indeed, it may not be possible to do so. The actual implementation may create data
structures for whatever portions are sufficient and tractable for purposes at hand, or may
merely trace out contingencies as needed. Similarly, the contingency graph is not a type of
visualization: it is an abstract formal object that can be visualized in different ways. One
need not visualize the graph as a node-and-link diagram as in Fig. 2: It may be queried and
manipulated through other visualizations. The value of a contingency graph lies in making
the structure of the data available in a media-independent manner while also indexing to
that media.
Contingencies provide evidence that uptake may exist, but do not automatically imply
that there is uptake. Uptake is manifest in many ways evidenced in each instance by
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multiple corroborating contingencies. Once uptake has been identified, it may be
represented using an uptake graph, as in Suthers (2006a). An uptake graph is similar to a
contingency graph, but may collect together multiple contingencies into a single uptake
relation.
Constructing contingency graphs
This section describes the practical tasks involved in producing a contingency graph, and
discusses these tasks in relation to existing analytic practices.
Identifying events and coordinations
Any analysis selects events that the analyst believes are relevant to the analytic question.
For example, when an analyst transcribes an audio or videotape into Jeffersonian notation,
the transcript is necessarily less rich than the original data: The analyst is selecting those
events that she believes are relevant for further analysis. The act of “segmentation”
common in some methods identifies units of the data representation (segments) that are
suitable as meaningful units for the purpose of analysis. Similarly, an analyst may identify
points of interest in a media recording or extract events from software log files.
Identification of events believed to be relevant to the analytic question is also the first
step of constructing a contingency graph. Doing so follows existing analytic practice, but
makes this practice explicit by representing events as vertices in the contingency graph. The
practice of explicitly identifying the events on which an analysis is based makes clear the
specific events that were seen as relevant and helps expose assumptions. This helps
multiple analysts collaboratively review their observations and interpretations. The
contingency graph should allow the analyst to return to the event as accounted in the
data record.
As analysts of collaborative learning, we are particularly interested in participants’ acts
that coordinate with the public realm. Some coordinations are easy to identify. When
analyzing spoken conversation or discussion forums, utterances and messages (respectively)
are obvious candidates for coordinations. The creation or editing of an object or inscriptions in a
shared workspace is similarly easy to identify as coordination. We use the general term
expressions to refer to coordinations that produce manifestations potentially available to
others.
Perceptions (e.g., seeing or hearing an expression) are another form of coordination
between personal and public realms. Some analyses do not attempt explicit identification of
perceptions, and may implicitly assume that every contribution is available to others at the
time the contribution is produced or displayed. With asynchronous data, this assumption is
clearly untenable. The applicability of this assumption to some forms of quasi-synchronous
interaction can also be questioned. For example, we cannot assume that a chat message was
perceived when it was produced. Active participants may have scrolled back into the chat
history, or may be attending to an associated whiteboard. In our own work, maintaining the
distinction between expression and perception has forced us to question our assumptions
about which coordinations are available to others, and when. The contingency graph can
include explicit specification of evidence for perceptions as another form of coordination.
Perceptual coordinations are usually difficult to identify, but in some data, observable
proxies such as opening a message are available. This is useful information for some
analyses, such as tracing information sharing.
D.D. Suthers, et al.

We have found it necessary to include events generated by nonhuman actors in our
contingency graphs. For example, consider asynchronous computer-mediated interaction. A
person engages in an expressive act that results in a change in the digital environment, such
as the creation of an object in a workspace or the posting of a message. Later, another
person connects to the workspace or discussion and the software system displays the object
or message on that person’s device. The recipient’s perception of the new object or message
is contingent upon and cannot occur prior to this automated display. This is an important
distinction to make in order to track availability of inscriptions and avoid making
unwarranted inferences. Vertices can be included for any event in the environment for
which we claim analytic relevance.
Identifying contingencies
Another task in constructing a contingency graph is to identify and document the
contingencies between events. Contingencies map out the sequential unfolding of the
interaction. They are defined in terms of participating events (eu, {e1, ... en}), and evidence
for the contingency.
The term contingency is introduced to make an important distinction between the
identification of evidence and the identification of interpretations in analytic practice. In
many coding methods, the analyst simply asserts relationships between acts, for example,
that a contribution is an “elaboration” on or “objection” to another. Measures of inter-rater
reliability are used to establish that there is sufficient agreement among the judgments of
those researchers participating in the analysis, but validity is not addressed because the
basis for judgment is not made explicit and available to other researchers. We advocate for
separating evidence from interpretation by first identifying manifest (as opposed to latent;
Rourke et al. 2001) features of coordinations and ways in which they are contingent upon
the environment and history, before interpreting these features and contingencies as
evidence for interactional relationships of interest. This approach facilitates sharing and
scrutiny of data and analyses, and provides a representational foundation for scaling up
interaction analysis with machine support.
In our own work, we have identified several contingency types, summarized in Table 2
and discussed below along with examples. The most obvious contingencies are media
dependencies, which are present when an action on a media object required the existence of
Table 2 Summary of types of contingencies of ei on ej
Media dependency ei operates on a media object or state of that object that was created or
modified by ej
Temporal proximity ei took place soon after ej, where “soon” depends on the attentional properties
of the agent and persistency of the medium
Spatial organization The locality of inscriptions operated on in ei is in a spatial context
created by ej
Inscriptional similarity ei creates inscriptions with visual attributes similar to those of inscriptions
created by ej
ei creates inscriptions with lexical strings identical to those in inscriptions
created by ej
Semantic relatedness The meaning of inscriptions created by ei overlaps with that of inscriptions
created by ej
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a previous action that created the object or left it in a prerequisite state. For example, a reply
in a threaded discussion depends on the prior creation of the message being replied to, and
modifying an element of a shared workspace depends on the most recent act that modified
the element.
Media dependencies can include perceptual coordinations. Consider a reply in a
threaded discussion. The creation of the reply message is contingent on the author’s
perception of the message being replied to (and possibly on other perceptions), which, in
turn, is contingent on the creation of the message. The importance of this distinction will
be exemplified later, in the example associated with Fig. 10, where the inclusion of
contingencies involving read events gives a dramatically different impression of the
coherence of a discussion. However, for many analytic purposes or when evidence for
perceptual coordinations is not available, it is sufficient to work with contingencies
between expressive acts.
Temporal proximity is important in analysis of spoken dialogue and interaction in other
media where contributions are expected to be relevant to ones immediately prior.
Contingencies based on temporal proximity need not be limited to adjacent coordinations:
They can extend in time based on the attentional and memory properties of the agents and
on the persistence and availability of the media involved. For example, a comment by a
conference delegate on the quality of posters at a conference may be contingent upon
posters viewed during that poster session; and a message posted in a threaded discussion
may be contingent on messages read previously during the login session. We might assume
that temporal contingencies weaken with the passage of time, though it is difficult to
quantify this degradation in a satisfying manner.
Contingencies based on spatial organization may be useful for analysis of interaction in
media where spatial placement can be manipulated by participants. For example,
contingencies can be asserted when coordinative acts place objects in proximity in a two-
dimensional workspace. If two items are placed near each other in a workspace, this may be
an expression of relatedness. This example illustrates the more general principle of not
confusing the representational vocabulary of a medium with the actions supported by the
medium. For example, a medium that supports spatial positioning may be used to create
groups even if no explicit grouping tool is provided (Dwyer and Suthers 2006; Shipman
and McCall 1994). Membership in configurations such as lists may also be asserted as
contingencies. Spatial contingencies merely record the fact that the placement of one object
near the other depends on the prior placement: Whether we interpret this organization as
some kind of grouping or categorization is the concern of further analysis.
Inscriptional similarities are often used by actors to indicate relatedness (Dwyer and
Suthers 2006). For example, inscriptions can have similar visual attributes (e.g., color or
type face), shapes can be reused, or lexical strings can be repeated. Contingencies are
asserted between coordinations based on inscriptional similarities to record the possibility
that the reuse of the inscriptional feature indicates an influence of the prior coordinations
{c1, ... cn} on cu.
Semantic relatedness may be asserted when the semantic content of a coordination
overlaps with that of another coordination in a manner that requires recognition of meaning
(not merely inscriptional similarity). For example, if one inscription contains the phrase
“environmental factors” and another contains the phrase “toxins in the environment,” and
these are considered to be related ideas in the domain under discussion, then a semantic
contingency might be asserted. However, these are latent rather than manifest relations, so
care must be taken to not assert semantic contingencies that assume the uptake for which
those contingencies are to serve as evidence.
D.D. Suthers, et al.

In general, contingencies are more convincing as evidence for uptake if multiple
contingencies are present offering convergent evidence (e.g., temporal proximity and
lexical overlap between the same two coordinations). Therefore, it can be important to
identify several types of contingencies and to interpret contingencies between coordinations
collectively.
Documenting other aspects of interaction
A contingency graph is a partial transcription of an interaction. It may be necessary to
annotate or augment the contingency graph formalism to contextualize the interaction. For
example, the reply structure of a threaded discussion is an important resource for
understanding the participants’ view of the medium, and so may be included as annotations
on contingency graphs. In asynchronous settings, it can be important to document
workspace updates by which participants received new data from their partner. These
updates can be represented in the contingency graph as vertices for events in which the
technological environment is the actant.
Role of the contingency graph in analysis
The contingency graph was developed to support diverse studies in our laboratory,
including multiple methods of analysis applied to a single source of data, as well as to help
integrate our thinking about interaction across several sources of data. The contingency
graph can be used for analysis in various ways, and methods cannot be described without
giving the context in which they were applied. Therefore, detailed explication of how the
contingency graph is used in analysis is taken up in the examples starting in the next
section. We conclude this section with a few general observations concerning analysis of
contingencies and uptake.
Iteration and densification Production of the contingency graph can be an iterative process
of densification in which multiple passes through the data identify additional elements and
provide new insights into the interaction (e.g., as in Medina and Suthers 2009). New events
and contingencies can be continually added to the graph. As the recorded data becomes
richer, warranted results also scale up. Grounded theory (Glaser and Strauss 1967) offers
tools for iterative analysis, including motivated addition of data through “theoretical
sampling.” However, the graph can never be considered complete, except with regard to
particular representational elements (e.g., it is possible to claim that every discussion
posting has been recorded). Therefore, as in any analysis, one must be cautious about
asserting that a practice or pattern never occurs.
Directions of analysis Analyses may take different directions from what is given to what is
discovered. A typical distributed cognition analysis starts by identifying a system’s function
(e.g., collaboratively steering a ship) and explains how that function is carried out by
tracing the propagation of information through the system and identifying transformations
of that information that take place at points of coordination between the participants and
external representations. In settings fundamentally concerned with the creation of new
knowledge, it is more appropriate to work bottom-up, starting with the identification of
visible acts of coordination and the contingencies between them, and then seeking to
recognize what is accomplished through the interaction. A hybrid approach is to start with a
recognized learning accomplishment, and then to work backwards in time to reconstruct an
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account of how this accomplishment came about. An example will be offered in the next
section.
In summary, a contingency graph is an abstract transcript that indexes to the original data
but indicates the aspects of that data that are chosen for analysis. It is only a starting point
for analysis. Collections of contingencies evidence uptake; and sequences of uptakes are
interpreted based on the theoretical phenomena of interest, such as argumentation,
knowledge construction, or intersubjective meaning making. In practice, the process may
iterate between identification of coordinations, contingencies, and uptake; and may be
driven by specific analytic goals or may be more exploratory in nature. Because the
explication of structure in the data and the analytic interpretation are separated, the
contingency graph can serve as a basis for comparison and integration of multiple
interpretations. Possible approaches to interpretation are diverse: Some examples are given
in the rest of the paper.
Detailed example of the contingency graph representation
In this section, we provide a simple yet detailed example of how a contingency graph is
derived from data, and how that contingency graph can be used for tracing out three
fundamental interaction patterns (information sharing, information integration, and round
trips). The purpose of this section is to help the reader understand the contingency graph as
an abstract data representation, to illustrate how to trace out intersubjective meaning
making in the graph representation, and to introduce the visual notations we use to display
graphs. Our claim that it is a useful analytic representation will also be addressed with
additional examples in the next section. The example in this section and two examples in
the next section are based on data derived from dyads interacting in a laboratory setting.
Therefore, we begin by briefly explaining the source of the data.
Asynchronous dyadic interaction in a laboratory setting
The data is derived from an experimental study of asynchronously communicating dyads,
conducted to test the claim that conceptual representations support collaborative knowledge
construction in online learning more effectively than threaded discussions (Suthers 2001;
Suthers et al. 2008). Participants interacted via computers using evidence mapping and
threaded discussion tools in a shared workspace to identify the cause of a disease on Guam
(Fig. 3). Three conditions were tested: threaded discussion only; threaded discussion side
by side with evidence map; and evidence map with embedded notes (the latter is shown in
Fig. 3). Information was distributed across participants in a hidden profile (Stasser and
Stewart 1992) such that information sharing was necessary to refute weak hypotheses and
construct a more complex hypothesis. The protocol for propagating updates between
workspaces was asynchronous. Process data included server logs and video capture of the
screens. Outcome data included individual essays that participants wrote at the end of the
session, and a multiple-choice test for both recall and integration of information that
participants took a week later. Results reported elsewhere (Suthers et al. 2007d, 2008)
showed that users of conceptual representations (the two conditions with evidence maps)
created more hypotheses earlier in the experimental sessions and elaborated on hypotheses
more than users of threaded discussions. Participants using the evidence map with
embedded notes were more likely to converge on the same conclusion and scored higher on
posttest questions that required integration of information distributed across dyads. One
D.D. Suthers, et al.

possible explanation for these convergence and integration results is that the higher
performing group shared more information, but this explanation was not supported by
analysis of essay contents and posttest questions designed to test information sharing.
Therefore, we undertook further analyses to explore information sharing during the
session.
Motivation for the analysis
Some of our analyses sought to identify whether and how the construction of the essays
was accountable to the prior session, and especially whether interaction between
participants influenced the essays. For each session analyzed, we began with the participants’
essays and traced contingencies back into the session (constructing the contingency graph as we
went) to identify uptake trajectories that may have influenced the essays. Some sessions were
chosen for analysis because there was convergence in the content of the essays and we wanted
to identify how this convergence was achieved interactionally. Other sessions were chosen to
examine divergent conclusions. In both cases, we wanted to relate significant instances of
intersubjective uptake or failure thereof to how participants used the media resources. The first
example presented below is of the former type, where participants converged in their individual
essays.
Elements of a contingency graph
In this section, we illustrate how elements of a contingency graph are related to interaction
data, drawing on an analysis we conducted for one session. Both participants (referred to as
P1 and P2) mentioned “duration of exposure” to environmental factors or toxins in their
essays, and the analysis sought to identify how this convergence in the individually written
essays was accomplished. We constructed a contingency graph by working backwards from
the events in which each participant wrote this explanation to identify the contingencies of
these writings on prior events. We constructed the contingency graph in OmniGraffle™ and
Microsoft Visio™ based on inspection of software log files (imported into Microsoft
Excel™) and inspection of video of participants’ screens (recorded in Morae™). The
Fig. 3 Interacting through graphical workspaces
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contingency graph we constructed focused only on the interaction relevant to the
aforementioned essay writing events, and includes about 180 events and 220 contingencies
between them. A visualization of a small portion of this graph is shown in Fig. 4. The
rounded boxes with text in them summarize the logged events on which the presented
portion of the graph is based. These are included solely as expository devices and are not
part of the contingency graph, although graph elements should always index back to their
data source. Vertices representing P1’s coordinations (the logged events) are shown as black
rectangles above the timeline, and vertices representing P2’s coordinations are shown as
white rectangles below the timeline. Each vertex was assigned an identifier as we
constructed the graph, vertices for perceptual coordinations being marked with the letter
“p.” Time flows left to right, but this being an asynchronous setting we cannot assume that
a contribution is available as soon as it is created, nor can we assume that the clocks on
each client were synchronized (inspection of the figure will reveal that they were not). The
vertical lines in each participant’s half demarcate when the local client updated that
participant’s workspace to display new work by the partner. (These events can be
represented as vertices in the contingency graph formalism, but for simplicity we show only
vertices for human actors.)
Arrows between the boxes visualize contingencies. Dotted arrows represent intra-
subjective and solid arrows represent intersubjective contingencies. For example,
contingency (20p, {20}), a media dependency, is present because P1’s coordination that
took place at 1:50:23, represented by vertex 20p, accessed the media object created by P2 in
the coordination that took place at 1:41:40, represented by vertex 20. Although the
preceding sentence is technically accurate, it is also tedious. For brevity, we will use the
numeric identifier as shorthand to refer to the coordination, any object or inscription that
may have resulted from the coordination, or the vertex that represents that coordination. For
example, we can state simply that 20p accessed 20’s media object, so a media dependency
Fig. 4 Fragment of a contingency graph and the events from which it was derived
D.D. Suthers, et al.

is present. However, we will make the distinctions more explicit when necessary for the
point at hand.
This graph illustrates how contingencies can be evidenced by the editing of media
objects or by lexical similarity, and can be further evidenced by temporal and spatial
proximity. For example, at 1:52:06, P1 added a comment (10) to the same note object
that she had just read at 1:50:23 (20p). (A note object can contain a sequence of
comments from both participants.) Because the coordination 10 could not have taken
place unless this media object existed, we have a media dependency of 10 on 20p. The
same example illustrates lexical and temporal contingencies. Coordination 10 uses the
phrase “environmental factors,” which is present in the note accessed at 20p, providing
an inscriptional contingency of 10 on 20p. (Coordination 10 is also contingent on 13 by
lexical overlap of “duration of exposure.”) Finally, 10 takes place less than 2 min after
20p, providing circumstantial evidence by temporal proximity that 10 is contingent on
20p.1 Therefore, the arrow from 10 to 20p in Fig. 4 visualizes a composite of three
contingencies that we take as evidence for uptake.
Interpretation of the contingency graph
Next we walk through the graph of Fig. 4 to trace out the interaction it represents and
illustrate its analytic use. Because Fig. 4 shows only those composite contingencies we
have selected as evidence for uptake, it is also an uptake graph. We show how the uptake
structure can be interpreted in terms of three phenomena: information sharing, integration
of information from multiple sources, and intersubjective round trips.
Sharing information At 1:41:40, P2 creates a note summarizing environmental factors as
disease causes (20). This note is not yet visible to P1. Around then in clock time but
asynchronously from the participants’ perspectives, P1 creates a data object (13) concerning
the minimum duration of exposure to the Guam environment needed to acquire the disease.
Subsequently, a workspace refresh (1:50:03) makes note 20 available to P1. P1 opens this
note shortly after (20p). The contingency (20p, {20}) could be interpreted as an
information-sharing event, as P2 has expressed some information in inscriptions and P1
has accessed these inscriptions. We emphasize that this is an analytic interpretation: There
is no requirement that the contingency graph be interpreted in terms of flow of information
or shared mental states.
Integrating information Later, P1 adds a comment to the note object (10) that is contingent
on 13 and 20p, as discussed in the previous section. We interpret these combined
contingencies (10, {13, 20p}) as evidence for uptake in which 10 integrated two lines of
evidence about this disease from 13 (“duration of exposure”) and 20p (“environmental
factors”). Taking the transitive closure of contingencies that pass through perceptual
coordinations, the contingencies on expressive events are (10, {13, 20}). Therefore 10
integrates information that originated from each participant P1 (13) and P2 (20) in the
hidden profile design.
1 The mapping of temporal proximity to evidential strength is relative to the medium and activity. Here, a
person is deliberating over various materials while her partner works asynchronously. A few minutes
deliberation is plausible.
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A round trip Let us now examine how P1’s integration (10) became available to P2.
Sometime after 13 was expressed, a refresh (1:45:33)2 made the corresponding object
available to P2, who opened it shortly after (13p). Subsequently (after P2 does other work
not shown), another refresh (1:54:29) makes 10 available to P2, soon opened (10p).
Because P2 has considered both 13p (“duration of exposure”) and P1’s indication that
duration of exposure is relevant to environmental factors (10p), we view P2’s inclusion of
these concepts as “the duration of exposure to toxins” in her essay (e3) to be an uptake of
both of these conceptions. The round trip from 20 through 20p, 10 and back to 10p, namely
the path ((20p, {20}), (10, {13, 20p}), (10p, {10})}), represents intersubjective meaning
making on the smallest possible scale beyond one-way information sharing (Suthers et al.
2007c). In this case, information provided by P2 (20) is combined with information
available only to P1 (13) and reflected back to P2. We cannot rule out that e3 is uptake of
only 20 and 13p and, hence, based on a one-way transfer of information, but nor can we
rule out that P1’s endorsement of the importance of the idea in 10, taken up in 10p, also
influenced P2’s inclusion of this idea in the essay. It is plausible that both were a factor.
Necessity of tracking availability and access events
Awareness of representational elements is not symmetrical in asynchronous media. At one
point in the session just described, the objects created by coordinations 13 and 20 both
existed, but neither was available to the other participant. A contingency graph can record
when the media manipulations of other participants become available to a given participant,
but analysis cannot simply rely on the appearance of a media object in a workspace. Some
analyses will require evidence that a contribution was actually accessed, which is why we
need vertices representing perceptual coordinations such as 20p. Notations developed for
face-to-face and synchronous communication often assume a single context and immediate
availability of contributions. These are reasonable assumptions for those media but
significantly limit those notations’ applicability to asynchronous media.
Analytic use of the contingency graph
In this section, we provide examples of several analyses we conducted with the aid of the
contingency graph formalism, to provide evidence for our assertion that the contingency
graph can productively support multiple types of analyses of distributed interaction. Our
evidence is that the contingency graph has served in this way in our own laboratory, where
we have undertaken both discovery-oriented analysis (ideographic research) and quantita-
tive hypothesis testing (nomothetic research) from the same source of data, the previously
described dyads interacting in a laboratory setting. We also conclude with an application of
the contingency graph to a different source of data, server logs of asynchronous threaded
discussions in an online course, as an illustration of generality across media.
2 It may seem impossible for an object created at 1:45:49 to become available at 1:45:33. We remind the
reader that the computer clocks were not synchronized. The analogy of a time zone may be useful. In real
time, 1:45:33 in P2’s “time zone” is after 1:45:49 in P1’s “time zone.” It would have been easy to hide this
from readers by changing the time stamps in the figure. However, we decided to leave the discrepancy in to
emphasize the point that even if the clocks were synchronized it would be misleading to compare times
across the upper and lower half of the figure due to the asynchronous updating, and more importantly, that
the contingency graph can handle partially specified orderings of events from distinct timelines.
D.D. Suthers, et al.

Discovery of an interactional pattern
Figure 5 presents a contingency graph derived from a different dyad in the study described
previously. This dyad was using a combination of evidence maps and threaded discussions.
The analysis was done to understand how these two participants used the available media
resources to converge on the conclusion that aluminum in the environment is probably not
the cause of the disease under consideration. We were also considering whether
convergence is achieved by information sharing alone or whether interactional round trips
are required (Suthers et al. 2007a, c). Construction of the contingency graph allowed us to
discover an interesting interactional pattern that goes beyond simple round trips. The
information that “aluminum is the third most abundant element” and that this contradicts
aluminum as a causal agent were successfully shared via coordinations 27, 27p, 20, 19 and
20p (all of which took place in the evidence map). Specifically, the contingency (27p, {27})
is evidence that P2 is aware of P1’s hypothesis that aluminum is the cause; and the
composite contingency (20p, {20, 19}) is evidence that P1 is aware that P2 has expressed
the idea that the abundance of aluminum (20) is evidence against this hypothesis (19). From
an information-sharing perspective, these two contingencies are sufficient to explain the
fact that both the participants mentioned the abundance of aluminum as evidence against
aluminum as a disease factor. From an intersubjective perspective, the inclusion of the
contingency (19, {27p, 20}) makes this sequence a round trip in which P1’s expression (27)
has been taken up (27p), transformed (20, 19), and reflected back to P1 (20p).
The contingency graph exposed a second round trip over 20 min later in the session
(7, 7p, 8, 8p). This round trip made explicit and confirmed the interpretation implied by the
first round trip. By exposing this dual round trip structure, the uptake analysis enabled us to
hypothesize an interactional pattern in which information is first shared in one round trip,
and then agreement on joint interpretation of this information is accomplished in a second
round trip. We call these W patterns after their visual appearance in diagrams like Fig. 5.
The analysis also helped us discover that participants accomplished the confirmation round
Fig. 5 Partial contingency graph of a dyad collaborating with multiple media. Rectangles, octagons, and
ellipses represent coordinations with an evidence map, a threaded discussion, and a word processing tool,
respectively
Computer-Supported Collaborative Learning

trip in a different interactional medium, the threaded discussion (the coordinations
represented by octagons in the figure). The first round trip is reasoning about evidence in
the domain, easily expressible in the evidence map notation. The second round trip is
reflecting on the status of the domain evidence and how it should be interpreted. This
reflection is not as easily expressed in the evidence map, and indeed is a second-order act of
stepping outside of that map and interpreting it, so the use of natural language in the
threaded discussion seems appropriate. Similar distribution of domain and second-order
conversation across evidence maps and synchronous chat has been observed in another
study (Suthers 2006a).
Quantitative queries for hypothesis testing
This example illustrates how contingency graphs can be used to support quantitative
hypothesis testing. A study discussed previously found that dyads using evidence maps
with embedded notes came to agreement on the disease hypothesis far more than dyads
using other software configurations, even though the evidence map users discussed more
hypotheses (Suthers et al. 2008). This group also had higher scores on posttest questions
requiring integration of information. Given the central role of information sharing in
theorizing about collaboration (e.g., Bromme et al. 2005; Clark and Brennan 1991;
Haythornthwaite 1999; Pfister 2005), one might expect that this group shared more
information. We compared the use of shared information in essays, and also compared
performance on posttest questions that tested for shared information, but neither analysis
supported the assertion that there were differences in information sharing. These being
“outcome” data, we decided to see whether there was evidence for differential information
sharing in the sessions themselves. We found all patterns of contingencies in which
information uniquely given to one person was expressed in the shared medium and then
accessed by the other person (Fig. 6a). Our results showed that, measured this way,
information sharing in the session was uncorrelated with the convergence results (see also
Fischer and Mandl 2005). Given the apparent importance of round trips observed in the
previous analysis, we decided to similarly trace out round trips in the experimental sessions.
We found all patterns of contingencies that began with the pattern of the previous analysis,
but was further extended in that the recipient then re-expressed the information (possibly
transformed or elaborated) in a media object that was then accessed by the originating
participant (Fig. 6b). Results showed a possible difference (p=0.065) between the
experimental groups on round trips (Suthers et al. 2007c). However, a later analysis with
post hoc groups formed on presence or absence of convergence did not support either
information sharing or round trips as explanations, which presents a problem for the
dominant information sharing theory. The negative result on round trips may be due to our
Fig. 6 Information sharing and round trip patterns
D.D. Suthers, et al.

failure to track round trips based on hypotheses: see Suthers et al. (2007c) for an
explanation.
The point of this discussion is that contingency graphs can also support quantitative
hypothesis testing. In particular, basing quantitative analyses on theoretically interesting
patterns of contingencies as the fundamental units to be counted can make quantitative
studies more relevant to CSCL than studies based on attributes of isolated events or
outcome measures alone. A secondary point is that it is not necessary to construct a full
contingency graph in advance: In this study, the patterns of Fig. 6 were traced out and
counted algorithmically in coded log files without constructing an explicit graph
representation.
Uncovering representational practices through multi-level analysis
The next example analysis illustrates four related points. First, automated generation of
contingency graphs is possible and can be useful. Second, analysis often uses the
contingency graph in conjunction with the source data, and, indeed, part of the value of the
graph is to select relevant portions of the source data for further analysis. Third, one can
aggregate coordinations and contingencies to discover patterns at a coarser granularity.
Fourth, analysis of a contingency graph can lead to insights into nonverbal behavior.
One session from the “evidence map plus threaded discussion” condition was chosen for
analysis because participants appeared to converge on the role of cycad seeds in the disease,
but not on the role of drinking water. This analysis sought to determine why this might be
the case.
Contingency graph construction Because manual construction of the previous contingency
graphs was tedious, we used computational support. In this analysis, the contingency graph
was generated through mixed human-computer interaction. We first generated a
contingency graph based on media dependencies, by linking sequential chains of events
that referenced the same media object (see Medina and Suthers 2008, 2009 for details). We
wrote a collection of scripts packaged into a small application—the Uptake Graph Utility—
that controls interaction between a MySQL database and Omnigraffle™ (a commercial
application for diagramming and graphing) to visualize the contingency graph. See Fig. 7
for a portion of the initial visualization of the data under discussion. The Uptake Graph
Utility enables one to selectively filter elements of the graph from view, generate
subgraphs, and isolate structural or temporal properties of the data. For example, in this
analysis, we visualized the subgraph manipulating media objects that contained the strings
“drinking water” or “aluminum.”
Revealing a nonverbal interaction pattern A striking feature of the contingency graph was
that one participant appeared to be primarily contributing information by creating graph
objects, while the other participated primarily by manipulating graph objects, particularly
by moving them around. Figure 8 shows this pattern in an annotated portion of the
Fig. 7 A 20-minute portion of an automatically constructed contingency graph
Computer-Supported Collaborative Learning

contingency graph. P2 could be moving nodes around in order to see them, or to get them
out of the way: Dragging and dropping of graphical objects for these reasons is frequent.
However, in this case, the periodic pattern and density of P2’s series of movements
suggested more deliberate activity. This led us to examine the video record from P2’s
workstation. We found that P2 was performing a series of graph space reconfigurations to
organize information previously shared during the session. After P1 contributed new
information, P2 moved nodes to create spatially distinct groups, each of which contained
conceptually related items. In addition to this spatial organization, P2 created nodes
containing brief categorical labels such as “CYCAD INFO” and linked these nodes to
group members to further clarify their inclusion in the group.
Alternation between inspection of the contingency graph and viewing relevant video
from both workstations revealed that P1 took up these practices from P2, as detailed in
Medina and Suthers (2008, 2009). This led us to identify uptake of information and of
representational practices at a coarser granularity, as shown in Fig. 9. Beginning at the left,
P1 shared information containing a reference to aluminum in water as a contaminant in the
first two episodes (E1 & E3). The third information-sharing event by P1 contains two
references that correlate aluminum and neurological symptoms of the disease (E6). P2’s
uptake of this information is seen as episodes of graph space manipulations (E2, E4, E5 &
E7–10). Intersubjective uptake within this sequence of activity is initiated in P2’s visual
transformation of the shared information, and is followed by a series of intrasubjective
uptakes as P2 adjusts the representations. As shown in the far right of the diagram,
intersubjective interaction resumes when P1 takes up P2’s graphical organization in E11,
and in the concluding work episode.
Analytic roles of the contingency graph In this analysis, the contingency graph exposed
patterns of interaction and provided direct pointers (via time stamps) to relevant locations in
the video record, enabling us to conduct coordinated analysis of the two separate video
streams that identified the emergence of a shared representational practice. The contingency
graph supported flexible transitions between identification of macro uptake patterns and
microanalysis of a series of graphical manipulations during this analysis. Understanding the
development of representational practices requires macro and micro understandings
Fig. 8 Information sharing by P1 followed by systematic graph manipulations by P2
Fig. 9 High level view of uptake over the entire session
D.D. Suthers, et al.

(Suthers and Medina 2010a), and the contingency graph mediates between the two. As
Lemke states,
“… we always need to look at least one organizational level below the level we are
most interested in (to understand the affordances of its constituents) and also one
level above (to understand the enabling environmental stabilities).” (2001, p. 18)
We examined the video record to see how P1 used the affordances of the graph
representation to organize information, and we examined uptake at a coarser level over time
to see how the persistence of inscriptions in this environment enabled P2 to notice and pick
up these practices.
Asynchronous online discussion
In order to explore how the contingency graph framework can be adopted to conventional
online learning settings, we analyzed server logs of asynchronous threaded discussions in
an online graduate course on collaborative technologies. The software (discourse.ics.hawaii.
edu, developed in our laboratory) records message-opening events as well as message
postings, but there is no other record of participants’ manipulations of the screen. Figure 10
diagrams a fragment of the contingency graph we constructed in one analysis. After reading
Fig. 10 Fragment of contingency graph for an online discussion. Inset lower left shows threading structure
Computer-Supported Collaborative Learning

a paper on socio-constructivist, sociocultural, and shared cognition theories of collaborative
learning (Dillenbourg et al. 1996), a student facilitator suggested that students write “grant
proposals” to evaluate learning in the course itself, and discuss how their choice of theory
would affect how they approach the evaluation. The episode we analyzed took place over
several days, demarcated in Fig. 10 by vertical lines for midnight of each day. The reply
structure of the threaded discussion is shown in the inset, lower left of Fig. 10. The episode
of Fig. 10 was chosen because it illustrates conceptual integration across two subthreads,
and, hence, the analytic value of contingencies that are independent of media structure.
Stepping through our interpretation of the graph, in 1 the instructor (P2) has posted a
comment concerning a prior contribution that used the phrase “socio-cultural” but seemed
to express a socio-cognitive approach. Unfortunately, “socio-cognitive” had not been
discussed in the paper, and the student (P1) reading this message (1p) is confused by the
different name. She raises questions about the distinction in two separate replies, 2 and 3.
Between 2 and 3, she has read a sequence of messages (X1…Xn): P1 appeared to be
searching for more information on the topic. The next day, P2 returns, sees 2 (2p), replies
with an explanation of “socio-cultural” in 4, and then starts down the other subthread.
Seeing 3 (3p) the source of the confusion becomes apparent and P2 replies with a
terminological clarification (5). Later that day, P1 reads both threads (4p, 5p) but replies
only to the second with a “thank you” (6). On the third day, P3 reads messages in another
discussion (Y1…Ym), enters this discussion and reads both threads (2p, 4p, 3p, 5p, 6p), and
then replies to the last “thank you” message with a comment (7) about the confusion that
related back to the other discussion: an integrative move that was consistent with her
assigned role as student facilitator for this assignment.
Participants’ reading and posting strategies as well as the default display state and no-
edit policy of the medium affect whether conversations are split up or reintegrated. By
posting two separate replies (rather than editing her first reply—not allowed—or
responding to that reply), P1 opens up the possibility of a divergent discussion. By
following a strategy of reading and replying to each message one at a time, P2 continues the
split that P1 has started. The discussion tool also allows one to scroll through a single
display of all messages that one has opened in a discussion forum. By following a strategy
of reading all messages before replying, P3 brings these separate subthreads together.
However, the reply structure imposed by the discussion tool does not allow this
convergence to be expressed in the medium: P3 must reply to one of the messages, so
she replies to the last one she read.
Many analyses of online discussion consider only threading structure, which provides an
oversimplified record of interaction. If the analysis examined threading structure alone
(inset of Fig. 10), it would not be clear why P1 posted two separate questions (2 and 3), and
P3’s message (7) would seem odd as a reply to the “thank you,” as it is referring to “several
of our grant proposals.” But the contingency graph captures aspects of the coherence of the
mediated interaction that are not apparent in the threaded reply structure. The contingency
graph reveals that P1’s second posting was motivated by an attempt (X1…Xn) to find the
new phrase (“socio-cognitive”), and that P3 had read through a discussion of grant
proposals (Y1…Ym) about an hour before posting 2.
3 Although some of this coherence can
be recovered through analysis of quoting practices (Barcellini et al. 2005), our analysis goes
further to include (for example) lexical and temporal evidence for coherence, evidence that
3 In disCourse, a list of who has read each message at what time is available to participants on demand in a
separate display, but this analysis suggests that other awareness visualizations may be useful, such as
summaries of activity prior to a posting.
D.D. Suthers, et al.

can also be partially automated. This ability to identify trajectories of participation that are
independent of yet influenced by media structures is an important strength of the method.
Summary and discussion
The relationship between interaction and learning is a central concern of the learning
sciences. Computer-supported collaborative learning itself has been defined as “a field
centrally concerned with meaning and practices of meaning making in the context of joint
activity and the ways in which these practices are mediated through designed artifacts”
(Koschmann 2002). Our research focuses this agenda on how technology affordances
(designed or otherwise) influence and are appropriated by participants’ intersubjective
meaning making (Suthers 2006b). We take the concept of “interaction” broadly, to include
not only co-present interaction that is tightly coordinated to maintain a joint conception of a
problem (Teasley and Roschelle 1993), but also distributed asynchronous interaction in
online communities (Barab et al. 2004; Renninger and Shumar 2002), and even indirect
ways in which individuals benefit from the presence of others in “networked learning”
(Jones et al. 2006). In a world in which connectivity is ubiquitous and the distinction
between “online” and “offline” is no longer defensible, these forms of interaction will
become increasingly mixed in any learner’s experience, and the boundary between them
will become less clear. Therefore, researchers studying learning through interaction are well
advised to work with a fundamental conception of interaction that underlies its various
forms.
Our own research has included and continues to include instances of all of these forms
of interaction, including dyads interacting face-to-face, synchronously via computer and
paper media, and asynchronously; and larger numbers of participants interacting directly
and indirectly in online sociotechnical systems. The framework reported in this paper is the
result of our effort to provide theoretical coherence to our research while also addressing
practical problems in the study of distributed interaction. These two objectives are related.
We found that some theoretical accounts were expressed in terms derived from the
properties of media they studied, while we wanted to use a single conceptual framework.
The practical problems began when we tried to apply methods of face-to-face interaction
analysis to distributed interaction. The interaction was distributed across actors, media, and
time, and included asynchronous as well as synchronous interaction, making traditional
transcript representations and analytic concepts unsuitable. Also, we needed to integrate
data recorded in diverse formats. Therefore, we realized it would be valuable to collect the
various records of interaction into a single analytic artifact that does not assume a particular
interactional context and that can be inspected for evidence of distributed interaction and
phenomena at multiple granularities. Due to eclectic work in our laboratory, we needed to
support multiple methods of analysis. In particular, we wanted to apply sequential analysis
of interaction to expose the methods by which participants engage in intersubjective
meaning making, apply computational support to scale sequential analysis up to larger data
sets, and also support statistical testing of hypotheses concerning patterns of interaction. A
further objective we set for scientific accountability was to separate the empirical evidence
and the claims being made while also identifying the relationships between the two.
Over time, our efforts to address these problems and objectives resulted in the
framework for analysis reported in this paper. The empirical foundation of the framework is
the identification of events and contingencies between them. The representational
foundation of the framework is an abstract transcript, the contingency graph, which
Computer-Supported Collaborative Learning

represents events as vertices and contingencies as edges. The conceptual foundation of the
framework in terms of which interaction is identified is uptake between coordinations. We
have applied this framework to data derived from synchronous and asynchronous
interaction of dyads and small groups, as exemplified in this paper and other publications,
and have found it helpful in unifying diverse research in our own laboratory.
While a commitment to contingencies between events is inseparable from this
framework, the contingency graph may be adopted independently of the concepts of
coordinations and uptake. The contingency graph provides a single representation of data
that applies to diverse contexts and forms of interaction, supports computational tools for
scaling up sequential analysis, enables quantitative methods to operate on interactional
patterns, and separates empirical grounds from interpretation. The contingency graph is
media-agnostic. It records the multiple coordinations that took place in an interaction and
maps out their interdependencies. However, it is not media ignorant; it can bring in
medium-specific information and index to media recordings, so the relationship between
meaning making and the media can be examined.
We find the concept of uptake useful in interpreting contingency graphs. An uptake
analysis makes commitments to intentional agency by identifying coordinations, and then
uses corroborating contingencies to identify ways in which coordinations demonstrably
take manifestations of prior participation as relevant to ongoing participation. Abstracting a
contingency graph to an uptake graph enables one to trace out individual trajectories of
participation, to examine how these trajectories affect each other; and to step back and
analyze the composite web of interpretations that constitutes “distributed cognition”
(Hutchins 1995) or “group cognition” (Stahl 2006). Furthermore, we find the concept of
uptake to be useful for questioning assumptions concerning what constitutes interaction and
thinking about interaction in the diverse forms it takes.
Related work
The uptake analysis framework has strong affinities with the Constructing Networks of
Activity Relevant Episodes (CN-ARE) approach (Barab et al. 2001), although we offer a
framework rather than one method, and there are differences in granularity of analysis. As
the name implies, Activity Relevant Episodes (ARE) are episodes (rather than events) that
have been analytically identified as being relevant to activity in the activity theoretic sense.
Barab et al.’s AREs are larger units than events, and are identified further into the analytic
process than events. Contingency graphs could be constructed on AREs, but they also can
be constructed on automatically selected events prior to identification of the relevance of
events (or episodes) to an activity. In the uptake analysis framework, the contingency
graphs are the input to the analytic process: No prior coding other than identification of
latent events and contingencies is needed. In CN-ARE, the AREs are the product of an
analytic process of identifying and coding segments. AREs are defined in terms of “core
categories” such as “issue at hand,” “instigators,” and “practices,” categories that could be
the output of uptake analysis at a finer granularity.
The “links” of CN-ARE and our “contingencies” are very similar if not identical ideas.
Links are
“… anything that ties one node ... to any other node. Thus, conceptually, all our codes
can serve as links between nodes. Time links nodes historically, practices link nodes
of similar practices together, resources link nodes of specific resources used together,
and initiator and participant codes link people.” (Barab et al. 2001, p. 78).
D.D. Suthers, et al.

In our framework, many of these relationships between events can serve as
contingencies, although our analyses are applied at a finer granularity to identify practices
as displayed by sequences of coordinations rather than to assume them as properties of
single episodes, and we prefer to apply analytic interpretations to relationships between acts
or patterns of uptake rather than to single acts or episodes treated as “nodes.”
In CN-ARE, episodes are organized along an ordinal timeline. In the contingency
graph, contingencies are the fundamental organizer of events rather than time.
Timelines may also be included, but we do not assume a single timeline. The CN-
ARE practice of following “tracers” is similar to our practices of tracing pathways
through contingencies. New work underway at this writing focuses on developing
methods for “tracing out the movement, confluences, and transformations of persons,
artifacts and ideas in sociotechnical systems” via the contingency graph and derivative
representations (Suthers and Rosen 2009).
In general, we are very sympathetic to CN-ARE, and see potential for productive
synthesis when activity-relevant episodes are the right granularity of analysis. Contingency
graphs may be applied directly at this granularity or may be used to discover episodes in
subgraphs of a contingency graph that are then chunked into AREs for further analysis.
The contingency graph is an abstract data representation, not a modeling tool, but brief
comparison to related representations for modeling highlights some important points. State-
based representations (e.g., Jeong 2005; Olson et al. 1994) are not appropriate for
distributed interaction because there is no single event at a given time nor a single unit of
time common to all actors to which state attributes can be assigned. The confluences of
events in distributed systems are too complex to represent as a state. Furthermore, state
representations are historical in that they encapsulate all history in the state: The sequential
organization of prior events is not accessible from a state, and the sequential development
of learning processes is unavailable. Petri net representations summarized by Reimann
(2009) and detailed by van der Aalst and Weijters (2005) solve some of these problems.
They have superficial similarities to contingency graphs (capturing the sequential
organization of events in a partial ordering), but, being process-model representations
rather than data representations, they include devices such as conjunctive and disjunctive
branching that are not relevant to a record of an actual network of events. Furthermore,
significant analytic work has to be done before building these models: The algorithm of van
der Aalst and Weijters (2005) requires that instances of the process to be modeled have
been identified, that each event has been associated with a single instance of the process,
and that each event has been categorized with a code that is unique within its assigned
process.
Similarly, the uptake-analysis framework is not a software tool, but brief comparison to
software tools for analysis help highlight some affinities and differences with other
approaches. Some analytic tools are embedded within particular software environments,
enabling replay of recorded sessions (e.g., VMT; Stahl 2009) and display of derived
analytic representations (e.g., Larusson and Alterman 2007; Teplovs 2008). In contrast, the
uptake-analysis framework has supported both empirical and theoretical integration of
investigations in multiple software environments. Several useful analytic tools have been
developed that integrate multiple sources of data by aligning them to a single timeline by
which they are synchronized during analysis or replay. These include the Collaborative
Analysis Tool (Avouris et al. 2007), Digital Replay System (Brundell et al. 2008) and
Tatiana (Dyke and Lund 2009). These efforts are to be commended for developing analytic
software and making it available to others, a step we have not yet taken. Generally these
tools are developed to support reconstruction of synchronous interaction at a scale
Computer-Supported Collaborative Learning

experienced by a small set of participants. Partial synchronization via contingencies (or
temporal constraints however expressed) between data streams could make future versions
of these tools applicable to asynchronous distributed interaction as well. However, scaling
up to phenomena arising from distributed interaction between larger numbers of individuals
will require stepping outside of the replay paradigm.
Finally, the uptake analysis framework is not a visualization tool. Contingency graphs
have been visualized in this paper as node-link diagrams for exposition, but this
visualization is not identical to the formal graph-theoretic representation, and other
visualizations are possible. For example, it may be useful to visualize contingency graphs
using episodic timelines, such as in CORDFU (Luckin 2003) and CORDTRA (Hmelo-
Silver 2003). Events can be defined using time durations rather than time points, or a
recurring sequence of similar events at time points can be aggregated and visualized as a
continuous episode (but see Reimann’s 2009 caution concerning treating event-based
phenomena as continuous). The potential visualizations are limited only by the underlying
ontology.
Limitations
A limitation of the framework is that, in focusing on observed interaction in an event-based
ontology, it does not explicitly acknowledge the cultural or historical situatedness of the
participants, or address identity and community, except where these constructs might be
recorded in terms of prior events. It may be possible to represent influences exogenous to
the interaction with contingencies to pseudo-events that exist prior to the interaction.
In interpreting our graphs, we have encountered several issues related to the intrinsic
incompleteness of a contingency graph as a data representation. One must be careful not to
make inferences based on the absence of events and contingencies in the graph: Any graph
is partial and can be extended indefinitely due to the continuous nature of human action.
There are risks in conducting an analysis entirely by using the contingency graph. In
addition to being a structure of interest in its own right, the graph should be used as an
index to the original data. Visualization software can facilitate this by overlaying or
simultaneously displaying the graph with the source media (e.g., with tools such as
Brundell et al. 2008; Dyke and Lund 2009).
We have also found that it is important not to fix analysis at one level (Lemke 2001),
and, in particular, that meaningful units may occur at granularities above the granularity at
which events are originally identified. Our work has suggested two constructions: (1)
interactionally defined representational elements that do not correspond to any explicit
representational notation (e.g., defining groups by spatial co-location), and (2) composite
coordinations in which two or more media events seem to share a conception (e.g., a
sequence of moves that forms a representational configuration). A pressing task is to extend
the contingency graph formalism to better incorporate composite events and ambiguities
and degrees of association in contingencies. A complete explication of these two items is
necessary to extend the potential algorithmic support provided by the contingency graph
structure.
Another postulated limitation is actually a strength of the framework. Colleagues have
remarked that the number of potential contingencies for any given act is huge, and so the
contingency graphs can become quite complex. The richness of contingencies is a property
of human action, not a limitation of the contingency graph approach. An approach that
allows and encourages analysts to make contingencies explicit, and does so with a formal
representation that is amenable to computational support for analysis, is superior to one that
D.D. Suthers, et al.

does neither. Yet these colleagues’ concern demonstrates the need for software support in
retrieving information from and obtaining selective views of the contingency graph.
Future work
The greatest practical need is to develop software tools to help construct and use the
contingency graph. The need for improved analysis tools is a recurring theme (Sanderson
and Fisher 1994), and the size and density of potential data sets in the emerging
ubiquitously connected world exacerbates this need. It is time consuming to construct a
contingency graph manually. Initially, our contingency graphs were constructed using tools
such as Excel™, Visio™, and Omnigraffle™. Early analyses took place over many months
concurrently with extensive discussions in which we developed the theoretical and practical
basis for the framework and revised the graphs multiple times. Subsequently, we conducted
similar manual analyses of other sessions in several days. Customized software support can
help address this problem by partially automating data collection and the construction of the
graph through contingencies. Two prototype tools have been constructed: the Uptake Graph
Utility described previously, and a tool for constructing and visualizing the reply structure
of discussions in Tapped IN and CLTNet online communities. The present work has
developed the initial representational specifications for further development of a shareable
set of tools. These tools should enable access to contingency graphs at multiple
granularities and through filters, compressing them in time and/or scanning for patterns.
An analyst need not even use a graph representation at all: Visualization tools can convert
the underlying graph model into any useful visualization. Other visual representations
should be explored.
In ongoing work, we continue to apply the uptake analysis framework to a diversity of
data in preparation for development of software support tools. Our objective is to speed up
the analysis of intersubjective meaning making to the point where it need not be limited by
tedious microanalysis, but can also be efficiently applied on a larger scale. An important
aspect of evaluating this framework will be to determine how well it scales to larger groups
across longer time scales. With improved automation it might be possible to generate
contingency graphs for larger online communities over the course of months or even years.
It remains to be seen whether the constructs of coordinations, contingencies, and uptake
remain useful as the foundation for further analysis at these scales.
Boundary objects for CSCL
The framework presented in this paper was developed to meet the immediate practical
needs in our laboratory to support multi-method analyses of distributed interaction.
However, this is only part of the story. We also had an additional motivation that to our
surprise has turned out to be controversial, and, hence, left for the end of this paper so as
not to detract from the primary contribution. We believe that the need for theoretical and
methodological dialogue that we encountered in our own laboratory is a microcosm of a
need that also exists in the CSCL community. Diverse lines of work exist in CSCL and
allied endeavors: We study interaction in different media, examine phenomena ranging
from micro-episodes in small groups to large communities over periods of weeks to
months, and analyze data using various “qualitative” and “quantitative” analytic practices in
studies framed by diverse and potentially incommensurate world views. This multivocality
of CSCL is a strength, but only if there are “boundary objects” around which productive
discourse can form (Star and Griesemer 1989). Boundary objects “have different meanings
Computer-Supported Collaborative Learning

in different worlds but their structure is common enough to more than one world to make
them recognizable, a means of translation” (ibid, p. 393). Various candidates for such
objects exist: For example, productive discourse might form around shared phenomena of
interest, data sets, research questions, topic domains, and/or theories. Suthers (2006b)
proposed the study of technology affordances for intersubjective meaning making as a focal
phenomenon for CSCL, arguing that this topic distinguishes CSCL; is one on which we are
best positioned to make progress; and that progress would inform not only our
understanding of learning but other aspects of human activity as well. The work reported
in this paper can be taken as a different basis for discourse in CSCL: a framework for
representing data and conceptualizing interaction that unifies data from diverse sources and
supports analytic practices from multiple traditions. Other researchers have constructed
various specialized analysis and visualization tools to address the challenges of analyzing
distributed interaction, but we suggest that a less ad hoc approach will further progress.
Advances in other scientific disciplines have been accompanied with representational
innovations, and shared instruments and representations mediate the daily work of scientific
discourse (Latour 1990). Similarly, researchers studying learning that takes place through
interaction may benefit from shared ways of conceptualizing and representing the phenomena
of interest as a basis for scientific and design discourse. Without these, it is difficult to build on
each other’s work except within homogeneous sub-literatures. We offer this framework to the
research community in hopes it may support productive dialogue within the learning sciences.
In doing so, we do not claim that a theoretically and methodologically unified field with one
object of study is possible. Far from this, we do not even think it is desirable: Multivocality is a
strength, and the value of boundary objects is based on this diversity. Rather, we advocate only
for identifying common objects for productive discourse across what would otherwise be
disjoint bodies of work, and herein propose further such objects.
Acknowledgments The studies and analyses on which this paper is based were supported by the National
Science Foundation under award 0093505. The work developed during years of intensive discussion among
the authors that also benefited from interaction with numerous colleagues in our laboratory and elsewhere.
We especially thank Gerry Stahl for his ongoing commentary on these ideas and the anonymous reviewers
for comments that helped address problems with prior drafts.
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