seminal works on distributed cognition, situated cognition, activity theory, mediated cognition, situated learning, knowledge building, ethnomethodology, actor network theory, dialogics, small-group theory and social theory. (loc: 174-75)

Cooke and Gorman’s (2009) work on interaction-based measures of cognitive systems, especially measures of communication flow, which allow for analyses of influence and stability within group discussions. (loc: 249-50)

The VMT Project at the Group Cognition Lab at Drexel University has developed an ethnomethodologically-informed approach to interaction analysis of synchronous online interaction data (Zemel, Xhafa & Çakir, 2009). This approach is defined and described in Chapter 28 of Studying Virtual Math Teams (Stahl, 2009c). It is illustrated especially in Chapters 6, 7, 8 and 9 of that volume (Çakir, 2009b; Sarmiento-Klapper, 2009b; Toledo, 2009; Zhou, 2009). The method involves data sessions using the VMT Replayer to engage a group of experienced researchers in the conversation analysis of an excerpt from an online session to define the linguistic, visual and indexical work being carried out by the group and the group cognition thereby accomplished. The method is rigorous, generalizable and reliable, as discussed in Chapter 28. (loc: 300-306)

the analysis of group cognition explores how small groups engage as a group (i.e., at the group unit of analysis) in the accomplishment of cognitive tasks. These include such tasks as: intersubjective meaning making, interpersonal trains of thought, shared understandings of diagrams, joint problem conceptualizations, common references, coordination of problem-solving efforts, planning, deducing, designing, describing, problem solving, explaining, defining, generalizing, representing, remembering and reflecting. (loc: 307-11)

More complex learning is supposed to occur in “spirals of reciprocity,” where learners are intensely engaged with one another (Salomon and Perkins 1998). In particular, learners can attain new levels of understanding during interactions where more complex cognitive activities occur, such as analytical thinking, integration of ideas and reasoning. These include activities such as elaborating on content (e.g., Webb 1989), explaining ideas and concepts (e.g., Chi et al. 1994), asking thought-provoking questions (e.g., King 1998, 1999), argumentation (e.g., Kuhn 1991), resolving conceptual discrepancies (e.g., Piaget 1985) and modeling one another’s cognitive states. (loc: 364-68)

We have focused on a property known as transactivity (Rosé et al., 2008; Wang, Rosé, & Joshi, 2007; Joshi & Rosé, 2007), an important property of collaborative discourse. Participants in a collaborative setting are said to have transactive discussions when they elaborate, build upon, question or argue against the ideas presented by their partners in the process of working towards a common understanding of the task and reaching a shared solution. This process of understanding the partners’ ideas, comparing them to one’s own understanding, arguing and forming a common ground upon which a solution can be built collaboratively has been shown as important for learning (Teasley, 1997; Azmitia & Montgomery, 1993). (loc: 427-32)

Piaget’s model is a particularly key component of the theoretical underpinnings of our proposed work since it provides a framework for characterizing the difference between simply managing existing pieces of knowledge, as one might characterize work so far on macrocognition in the CKI community, and more major cognitive restructuring that can occur at certain points within an assimilation/accommodation cycle. (loc: 434-37)

Digging into the details a little more deeply, at the heart of Piaget’s theory of learning is the assimilation-accommodation cycle in which students encounter stimuli in the world that provide data either in support of or in conflict with their own internal model of the world. During assimilation, a student imposes his own model on the stimuli he sees, interpreting everything in that light, and rejecting what does not fit. During accommodation, a student is open to considering a model of the world that may be imposed from the outside. When these processes are in balance, the conditions are most favorable for a student to notice a gap or deeper flaw in his own mental model. When a student becomes aware that stimuli from the world reveal a gap, that student may then choose to search for a revised model of the world that accounts for the new data, which may even require a dramatic paradigm shift or major reorganization of knowledge. (loc: 437-44)

It is important to note that an important ingredient in Piaget’s theory is the equality of power or partnership between students working together that is important for creating an environment in which assimilation and accommodation are in balance. Note that equal power does not imply equality in knowledge. Based on Piaget’s foundational work (Piaget 1985), one can argue that a major cognitive benefit of collaborative learning is that when students bring differing perspectives to a problem-solving situation, the interaction causes the participants to consider questions that might not have occurred to them otherwise. This stimulus could cause them to identify gaps in their understanding, which they would then be in a position to address. This type of cognitive conflict has the potential to lead to productive shifts in student understanding. Examining the discourse between students in a collaborative-learning setting can reveal evidence of the power relationship between students, the exchange of views and evidence of the opportunity for cognitive conflict in the socio-cognitive conflict that is manifested in the argumentation that occurs between students. The impact of socio-cognitive conflict on learning has been noted especially in connection with difficult-to-learn content (Azimitia & Montgomery, 2005; Russell, 2005). (loc: 444-53)

the topic of what makes group discussions productive for learning and community building has been explored with very similar findings, perhaps with subtle distinctions, and under different names such as transactivity (Berkowitz & Gibbs, 1983; Teasley, 1997; Weinberger & Fischer, 2006) in the cognitive learning community and uptake (Suthers, 2006), group cognition (Stahl, 2006) or productive agency (Schwartz, 1998) in the socio-cultural learning community. Despite differences in orientation between the cognitive and socio-cultural learning communities, the conversational behaviors that have been identified as valuable are very similar. Building on these common findings, the field of Computer-Supported Collaborative Learning has emerged where support for collaborative learning has been developed that addresses observed weaknesses in conversational behavior related to this phenomenon. (loc: 468-75)

(Design-Based Research Collective, 2003). (loc: 1010)

In a similar vein, the learning sciences literature tells us that scaffolding should be faded over time (Collins et al., 1991), that over-scripting is detrimental to collaboration (Stegmann et al., 2004), and unnecessary support is demotivating (Dillenbourg, 2002). However, in order to fade collaboration scaffolding as a watchful parent, we must do so using technology that is sensitive to collaborative behavior in the environment. Thus, a major goal of our research is to support collaboration in a way that is responsive to what is happening in the collaboration rather than behaving in a “one size fits all” fashion, which is the case with state-of-the-art static forms of support. (loc: 1177-82)

Much research shows the value of drawing out student reasoning in the form of elaborated explanations. In particular, one of the best substantiated educational findings in cognitive science research related to education is the educational benefit of explanation, and in particular, the self-explanation effect (Chi et al., 1989; Chi et al., 1994; Chi, 2000). (loc: 1185-87)

previous discourse analyses of collaborative conversations reveal that the majority of conversational interactions between students do not display the “higher order thinking” that collaborative learning is meant to elicit (Webb & Mastergoerge, 2003; Webb, Nemer, & Zuniga, 2002), (loc: 1188-89)

Specifically, what we seek to learn from our investigations in the on-line VMT environment is how the contextual variables that distinguish that environment from the lab and the classroom environments may interfere with or change the effects of interventions on student behavior. Such variables include the time of the interaction (e.g., during school hours, in the evening, on the weekend, during the summer), location (co-located or distributed teams), reward structure (e.g., in class assignment, homework assignment, extra credit, or voluntary), group composition (e.g., same grade cohort, mixed grade/age), collaborative pre-disposition (e.g., students coming from schools where collaborative learning is encouraged and frequent versus schools where collaboration is not part of regular instruction), and experience in the environment (e.g., new to the on•line VMT environment versus having participated for a long time). (loc: 1278-84)

What methods are used systematically by small groups in online, text-based environments for taking turns, keeping interaction flowing, repairing mistakes or misunderstandings, opening and closing sessions, constituting the group as a collectivity, etc.? (loc: 1770-71)

studies of online learning have begun to suggest that these contexts can lead to new senses of possibility (Markus & Nurius, 1986; Shumar & Renninger, 2002), because they enable learners to explore and to shift their identities as learners (Linehan & McCarthy, 2000). Learners through online scaffolded support are better able to see themselves as competent in a particular area of study and relinquish their incompetent identity (Renninger & Shumar, 2002. (loc: 1776-79)

A complex systems approach has been foreshadowed throughout the years by Bransford et al.’s (Bransford, Brown, & Cocking, 2000) developing analyses of learning environments as learner-centered, assessment-centered, and knowledge-centered within larger social contexts. Activity theory (Engeström, 1999, 2001), in which the smallest unit of analysis is an entire complex activity system, has become an increasingly important theoretical lens for studies of learning with new media. Arrow, McGrath and Berdahl (2000) have also proposed a research agenda for the study of small groups as complex systems, with wide implications for the field of experimental social psychology (loc: 1787-92)

Online learning as a complex system is explicitly addressed in the work of Derry and colleagues (Derry, in press; Derry & Hmelo-Silver, in press) who have used the activity field construct to analyze complex interactions among student, task, facilitator and tools within online learning environments they created for teacher professional development. (loc: 1792-95)

Derry and Hmelo-Silver (in press) developed psychometrically validated concepts-in-use rubrics to score the quality of pre-service teachers’ discourse and products created in this environment. (loc: 1805-6)

Instruction organized around authentic problems may not honor the structure of the discipline being taught; problems bring concepts together in varied combinations. Thus online environments that engage students in authentic problem solving must provide well-designed facilitation and scaffolding to help students make sense of problems in terms of their previous, current, and future experiences (Salomon & Perkins, 1989). (loc: 1822-25)

Researchers and educators concerned with transfer have long used problem solving to help connect classrooms to problem contexts that are far removed from the instructional setting. However, an alternative that is often preferable to simulated problem solving is learning through engagement with the actual context of practice. (loc: 1828-30)

Dewey (1914) in his now classic work, Interest and Effort, points to the power of interest to support students to engage, or make connections, to materials to be learned. He says that a person can not be made to have interest, but can be supported to develop interest. He also observes that where there is interest, effort follows. (loc: 1879-81)

In a forthcoming review of the literature, Hidi and Renninger (2005) note that interest—the predisposition to reengage particular content over time— differs from other motivational variables in at least four ways. First, interest has both affective and cognitive components, a position supported by neuroscientific research (LeDoux, 2000a, 2000b; Panksepp, 1998, 2003). Second, both the cognitive and affective components of interest have biological roots (Hidi, 2003; Davidson, 2000; Panksepp, 1998). Third, interest is an outcome of interactions between a learner and particular content. Finally, interest is always content specific rather than applying across all activity. (loc: 1881-86)

A proposal for a Science of Learning Center for the study of engaged learning in online communities. This proposal will describe an appropriate intellectual, organizational, technical and physical infrastructure to support rigorous, scientific study of this topic. The Center will bring together researchers with diverse, multidisciplinary approaches, in partnership with active online communities and schools across the country and around the world. (loc: 1961-64)

The collaborative learning theory will describe the phases of group interaction, such as: group formation, task specification, brainstorming, proposals, negotiation and publication of results. (loc: 2485-87)

The emphasis on the group unit has methodological implications. The analysis of what takes place in project experiments will rely heavily upon interaction analysis (Duranti, 1998; Garfinkel, 1967; Heritage, 1984; Jordan & Henderson, 1995; Sacks, 1992) (Stahl, 2002e) and community ethnography (Renninger & Shumar, 2002). (loc: 2539-42)

Diverse theories of collaboration stress the power of heterogeneity: cognitive dissonance (Festinger, 1957), perspectives (Boland & Tenkasi, 1995; Goldnam-Segall, 1998; Stahl & Herrmann, 1999), interdependence (Johnson & Johnson, 1989), zone of proximal development (Vygotsky, 1930/1978), cognitive flexibility (Feltovich et al., 1996). (loc: 2608-10)

This grant was instrumental in the PI’s turn from earlier work on organizational memory to support for collaborative learning. The OMOL project started from a model of computer support for organizations as Domain-Oriented Design Environments (DODEs) in which both domain knowledge and local knowledge are stored in the form of artifact designs and associated design rationale (Fischer, 1994). This CSCW model evolved into one of Collaborative Information Environments (CIEs), that emphasized the interactive, asynchronous, persistent discussion of concepts and issues within an organization (Stahl, 1998; 2000a). Gradually, interest in organizational learning aspects led to involvement in CSCL and the model of collaborative knowledge-building environments (Fischer et al., 1999). A number of software prototypes were developed to explore the use of the Web as a communication and collaboration medium. Of these, the most important for the proposed work was WebGuide a prototype threaded discussion system that provided multiple perspectives on the discussion, comparison of perspectives and control over rearrangement of notes (Stahl & dePaula, 1998; Stahl et al., 1998). Deployment of WebGuide in classrooms raised serious issues of adoption and concerns of socio-technical and social informatics (Kling, 1999) issues: motivation, media competition, critical mass, social practices, seeding, management, re-seeding, convergence of ideas, peer-to-peer collaboration, deployment strategies. (loc: 2710-21)

If one carefully studies learning in school, workplace and home, one finds that most learning is a subtle mix of collaborative and individual effort (Fischer & Granoo, 1995; Stahl, 2002d). (loc: 3142-43)

In this project, we propose using teachers’ and their students’ original mathematical conversations as “didactic objects” (Thompson, 2002) designed to support “decentering” (Wolvin & Coakley, 1993) and “collective reflection” (Cobb et al., 1997) on particular aspects of their math cognition. (loc: 4072-75)

which analyze: how math problem solving can be effectively conducted collaboratively among students who have never met face-to-face; how the structure of text chat interaction differs from spoken conversation; how the media of graphical diagrams, textual narratives, and symbolic representations can be intimately interwoven to build deep math understanding; how deictic referencing is important to establishing shared understanding; how students co-construct a joint problem space; how collaborative meaning making and knowledge building are accomplished in detail; how online math discourse can be supported by a software environment that integrates synchronous and asynchronous media with specialized math tools; and how a methodology based on interaction analysis can be used for a science of group cognition. (loc: 4118-23)