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 computersupported 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. (loc: 8-20)

asynchronous learning networks (Bourne, McMaster, Rieger, & Campbell, 1997; Mayadas, 1997; Wegerif, 1998), knowledge building communities (Bielaczyc, 2006; Scardamalia & Bereiter, 1993), mobile and ubiquitous learning environments (Rogers & Price, 2008; Spikol & Milrad, 2008), online communities (Barab, Kling, & Gray, 2004; Renninger & Shumar, 2002), and learning in the context of “networked individualism” (Castells, 2001; Jones, DirckinckHolmfeld, & Lindstrom, 2006). (loc: 27-30)

“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. (loc: 32-33)

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. (loc: 34-41)

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 (in press) for further discussion of these analytic challenges. (loc: 46-48)

Our research methods have included experimental studies (Suthers & Hundhausen, 2003; Suthers, Vatrapu, Medina, Joseph, & Dwyer, 2008; Vatrapu & Suthers, 2009), activity-theoretic and narrative analysis of cases (Suthers, Yukawa, & Harada, 2007; Yukawa, 2006), adaptations of conversation analysis (Medina & Suthers, 2008; Medina, Suthers, & Vatrapu, 2009), and hybrid methods (Dwyer, 2007; Dwyer & Suthers, 2006). (loc: 50-53)

we developed the uptake analysis framework for conceptualizing, representing, and analyzing distributed (technology-mediated) interaction. (loc: 55-56)

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. (loc: 57-60)

beginning with microanalytic approaches inspired by the work of Tim Koschmann, Gerry Stahl, and colleagues (Koschmann et al., 2005; Koschmann, Stahl & Zemel, 2004). (loc: 75-76)

we applied the concept of uptake to track interaction distributed across these tools (Suthers, 2006a). (loc: 77-78)

we began analyzing asynchronous interaction involving threaded discussion and evidence mapping tools (Suthers, Dwyer, Vatrapu, & Medina, 2007). (loc: 78-79)

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, Dwyer, Medina, & Vatrapu, 2007). Concurrently, we were pursuing a separate line of work on analyzing participation in online communities through various artifact-mediated associations (Joseph, Lid, & Suthers, 2007; Suthers, Chu, & Joseph, 2009). (loc: 79-82)

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, Schellens, Valcke, & Van Keer, 2006; Rourke, Anderson, Garrison, & Archer, 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. (loc: 86-90)

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. (loc: 92-96)

Several analytic traditions find the significance of each act in the context of the unfolding interaction. These traditions include Conversation Analysis (Goodwin & Heritage, 1990; Sacks, Schegloff, & Jefferson, 1974), Interaction Analysis (Jordan & Henderson, 1995), and Narrative Analysis (Hermann, 2003). (loc: 102-4)

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, Koschmann, & Suthers, 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). (loc: 105-9)

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 & Baskerville, 2003, for arguments against basing generalization solely on sampling theory). Microanalysis (loc: 111-14)

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 & 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. (loc: 111-17)

The family of methods loosely classified as exploratory sequential data analysis (ESDA, Sanderson & 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, Herbsleb, & Rueter, 1994). (loc: 117-21)

like statistical analysis, computational support risks distancing the analyst from the source data. (loc: 123-24)

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 occurring event category. (loc: 124-25)

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 & Weijters, 2005) that capture longer sequential patterns than state transitions. (loc: 125-27)

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 & 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 & 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 & Jacobs, 1999; Herring, 1999). It is not appropriate to treat computer-mediated communication as a degenerate form of face-to-face interaction, because people use attributes of new media to create new forms of interaction (Dwyer & 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. (loc: 128-38)

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. (loc: 141-43)

Given a data stream of events, analysts select certain events as being of significance for analysis (ei bottom of Figure 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 evidence for interaction. (loc: 168-73)

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 Figure 1) in a manner that is grounded in specific actions and the relationships between them. (loc: 173-76)

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. (loc: 176-79)

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 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 Hyperedges Conceptual Foundation Coordinations Uptake Represent, annotate and index to source data for events Represent, annotate and index to source data for contingencies Acts in which an agent coordinates between personal and public realms Taking aspects of other coordinations as having certain relevance for ongoing activity 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 & Nardi, 2006 section 9.2). However, the framework outlined in this paper does (loc: 186-97)

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. (loc: 198-205)

Activity theory postulates three levels of activity: operations, actions, and activity (Kaptelinin & 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. (loc: 205-9)

We call this fundamental basis of interaction uptake (Suthers, 2006a, 2006b). (loc: 213-14)

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 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. (loc: 214-23)

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 & Nardi, 2006, chapter 6). (loc: 223-26)

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. (loc: 226-32)

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. (loc: 232-37)

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, Salmon, Zeitz, Wathen, and Holowchak (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 & Gibbs, 1979; De Wever et al., 2006; Herring, 2001; Rourke et al., 2001; Strijbos, Martens, Prins, & Jochems, 2006). (loc: 237-45)

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, or peers, or of oneself (Azmitia & Montgomery, 1993; Kruger, 1993; Teasley, 1997; Weinberger & 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. (loc: 247-52)

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. (loc: 253-57)

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 & Heritage, 1984; Schegloff & 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 taking up instead 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. (loc: 257-63)

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). (loc: 263-66)