Social and Communicative Factors in Learning

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During PSLC’s first four years, its Interactive Communication Cluster has studied interactions between a student and a tutor (either human or computer) or, less frequently, two students interacting with each other. Most of the experimental manipulations and subsequent analyses have focused on the cognitive content of interaction through learning space analyses, in other words, the what and when of instruction. Study results investigating the effect of interaction, although somewhat mixed, have largely supported the hypothesis that focused interaction promotes cognitive aspects of learning such as attention to the most important knowledge components in a domain, deeper cognitive processing, and increased engagement with the content. VanLehn and colleagues (2007) present a thorough review of this literature as well as results from recent investigations. These results encouraged early PSLC efforts to “unpack” the nature of communicative interaction in instruction and learning. Rummel and colleagues (Diziol, Rummel, Kahrimanis, et al., 2008a, 2008b), for example have recently evaluated interactions with a rating scheme analysis that quantifies the quality of an interaction on a number of dimensions. This work represents an important step towards the type of up close inspection of communication that many scholars believe is necessary if we are to understand, and be able to manipulate for instructional purposes, how communication works to produce robust learning.

In our re-named Social-Communicative Factors thrust, we propose now to expand our investigations of communication as a core enabler of robust learning to include detailed study of patterns of interaction, the role of conversation and structured talk in initiating and sustaining learning, and the effects on motivation, self-attribution and commitment to a learning group that are associated with learning through social-communicative interaction. Specifically, we propose to investigate how human linguistic interaction works in instruction and learning, and how participants in learning exchanges (both teachers and students) can best be taught productive forms of interaction. We draw from our extensive prior work related separately to classroom discourse (Chapin & O’Connor, 2004; Bill et al., 1992; Resnick et al., 1992) and collaborative learning (Gweon et al., 2007; Joshi & Rose, 2007; Rummel & Diziol, 2008). We note that, although the classroom discourse and collaborative learning communities have proceeded mainly independently from one another, the conversational processes identified as valuable within these two communities are strongly overlapping.

Investigations of valuable conversational contributions have been conducted both within communities exploring the cognitive foundations of group learning and the sociocultural community. Regardless of the theoretical framework, the same ideas have surfaced under a number of different names including Accountable Talk (Michaels, O’Connor & Resnick, 2007; Resnick, O'Connor, & Michaels, 2007), transactivity (Berkowitz & Gibbs, 1984; Teasley, 1997; Weinberger & Fishcer, 2006; King, 1999), productive agency (Schwartz, 1999), and uptake (Suthers, 2006), and have been demonstrated to predict learning both in collaborative learning contexts (Azimita & Montgomery, 1993; Joshi & Rose, 2007) and classroom contexts (O’Connor et al., 2007). For example, one cognitive justification for the value of transactive conversational behavior is its connection with cognitive conflict (Piaget, 1985), where transactive conversational moves highlight differences between the mental models of collaborating students. 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. It has the potential to elicit elaborate explanations from students that are associated with learning (Webb, Nemer, & Zuniga 2002). From the sociocultural perspective, based on Vygotsky’s seminal work (Vygotsky 1978), we can similarly argue that when students who have different strengths and weaknesses work together, they can provide support for each other that allows them to solve problems that would be just beyond their reach if they were working alone.

We will proceed with two interacting research strategies: one, expanding capacities for recording, coding and analyzing interactive communication that can be at least partially automated; and two, conducting in vivo experiments on ways of teaching participants the most promising patterns of interactive communication and testing the effects of these patterns on measures of robust learning.

In the first thread of our proposed work, we will work toward a common conceptual framework that unifies the classroom discourse, collaborative learning and instructional tutoring communities. To this end, we plan to develop a concrete and precise formalization on a linguistic level of what counts as performing these valued conversational moves. This concrete formalization will provide a common language for documenting and investigating the specific ways in which social-communicative practices can promote (or hinder) learning of complex mathematics and science content and reasoning skills.

In the second thread of our proposed work, we will examine causal connections between these communicative processes and learning by running in vivo experiments in which specific social-communicative practices are introduced into well-defined mathematics and science units of study. We will begin by replicating and extending a series of in vivo experiments on the effects of Accountable Talk in low-income urban classrooms with high proportions of English language learners in Chelsea, Massachusetts (O’Connor et al 2007; NHSF REC 0231893, PI: O’Connor). In a tightly controlled series of three-day studies in 5th and 6th grade classrooms, O’Connor’s group sought to determine whether it was possible to get evidence supporting a hypothesized causal relationship between selected discourse-intensive instructional practices and student mathematics learning. In previous non-experimental studies in Chelsea, students had shown large gains on standardized tests after a year or more of discourse-intensive instruction, but it was not possible to test the specific features of the intervention that produced these effects. Thus it was possible that cognitive and metacognitive abilities might improve over months of practice in clarifying, justifying and describing mathematical ideas, whether or not explicit transactive communication strategies were employed. Similarly, student motivation might have improved due to long-term participation in an intensive mathematics program, without a specific impact of particular forms of linguistic participation.

We will design and run in vivo experiments to test more specific hypotheses concerning specific Accountable Talk moves. Subsequent studies will test a larger intervention that includes training in the most effective conversational moves and collaborative scripts with implementation in a number of classrooms. The studies will focus on math and science learning topics. These studies will make use of techniques from automatic collaborative learning process analysis (Rose et al., in press; Wang et al., 2007; Donmez et al., 2005) and script-based support for productive collaboration (Dillenbourg & Jermann, 2007; Kollar, Fischer, & Hesse, 2006; Rummel & Spada, 2007; Diziol, Rummel, Kahrimanis, Spada & Avaris, 2008; Diziol et al., 2008; Walker, Rummel, McLaren & Koedinger, 2007) to carefully manipulate these properties of conversation in highly controlled and context sensitive ways.

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References

  • Chi, M.T., Roy, M., & Hausmann, R.G. (March, 2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science: A Multidisciplinary Journal, 32:2, 301-341. Click to download
  • Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in the Philosophy of Education, 27(4), 283-297.
  • Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2, 63-86. Click to download
  • Resnick, L., O'Connor, C., and Michaels, S. (2007). Classroom Discourse, Mathematical Rigor, and Student Reasoning: An Accountable Talk Literature Review. Click to download
  • Rose, C., et al. (2007). Analyzing collaborative learning processes automatically: Exploiting the advance of computational linguistics in computer-supported collaborative learning. Click to download
  • Walker, E., Rummel, N., & Koedinger, K. (2008). A Research-Oriented Architecture for Providing Adaptive Collaborative Learning Support Click to download
  • Yamakawa,Y., Forman, E., and Ansell, E. (2005). The role of positioning in constructing an identity in a third grade mathematics classroom. Click to download

Link title== Other recommended readings on the role of classroom dialogue in learning and development==

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  • Asterhan, C. S. C. & Schwarz, B. B. (in press). Online human guidance of small group discussions: The case of synchronous e-argumentation in a diagram-based discussion space. International Journal of Computer-Supported Collaborative Learning.
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  • Chi, M. T. H., de Leeuw, N., Chiu, M., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
  • Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectivness from vicarious learning. Cognitive Science, 33, 301–341.
  • Chi, M.T.H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. (2001). Learning from human tutoring. Cognitive Science, 25, 471-534.
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Meeting Notes

Social and Communicative Factors Thrust Workshop on Coding and Analysis of Classroom Dialogue, Pittsburgh May 26-27, 2011

  • Effects of Social Metacognition on Micro-Creativity: Statistical Discourse Analyses of Group Problem Solving - Ming Ming Chiu Click to download
  • Dialogue Analysis to Inform the Development of a Natural-language Tutoring System for Physics - Sandra Katz, Michael Ford, Pamela Jordan, Diane Litman
  • Temporal patterns of knowledge construction: Statistical discourse analysis of a role-based online discussion - Ming Ming Chiu & Alyssa Wise Click to download
  • Analyzing Teacher-Led Talks: A Talk Map Representation - Gaowei Chen
  • Towards Academically Productive Talk Supported by Conversational Agents - Carolyn Penstein Rosé, Lauren Resnick, Gregory Dyke, Iris Howley, Rohit Kumar Click to download
  • What (if anything) about framing? Is it significant? Do we need to consider it?- Jim Greeno Click to download
  • Measuring Classroom Discussions - Rip Correnti, Moddy McKeown, Jimmy Scherrer, Peg Smith, Mary Kay Stein, Kevin Ashley, Jim Greeno
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