This paper aims to report on the development of a methodological tool to analyse transcript data based on videorecordings of students engaged in collaborative problem solving activities in mathematics. The data reported in this paper comes from a project, the Social Unit of Learning project, on collaborative problem solving, which administered open-ended mathematical tasks to a class of 12-13 years old students (26 students in total). Members of the ICCR research team, at the University of Melbourne, designed the experiment. In the project, students worked individually, in pairs and in small groups, in a laboratory classroom equipped with 10 built-in video cameras and 16 audio recorders. The data collected are being analysed by scholars from Australia, Finland and Spain. The purpose of the methodological tool described in this presentation is to understand how dialogic and non-dialogic interaction works when students solve an open-ended task in small groups. Research in mathematics education suggests that social interaction mediates students’ learning. Educational researchers created concepts such Zone of Proximal Development (ZDP; Vygotsky, 1978), scaffolding (Wood, Bruner & Ross, 1976), dialogic learning (Flecha, 2000) or distributed cognition (Hutchins, 2006), to characterise that process, recognizing that social interaction plays a central role in student learning. However, not all kinds of social interaction produce the same result in terms of learning. This paper addresses the question: “Can we design a methodological instrument to analyse social interactions in educational settings such as mathematics classrooms by combining the theory of argumentation developed by Habermas, the theory of dialogic learning proposed by Flecha, and the analysis of the illocutionary force of the statements described by Austin and Searle (and also discussed by Soler and Flecha)?” To answer this question, this study applied the proposed instrument to analyse the social interactions recorded in the experimental environment designed for the Social Unit of Learning project. In a previous study, I created a taxonomy to characterize similar interactions within learning episodes in mathematics classrooms (Díez-Palomar & Cabré, 2015; Garcia-Carrión & Díez-Palomar, 2015). Drawing on this earlier work and with the advantage of the level of detail provided by the new laboratory classroom, this presentation will provide the details of the new instrument, through a form of coding manual, report some examples of how it was used to code actual transcripts from the videorecording, and provide critical evaluative comment on the efficacy of the instrument and the validity of the resultant analyses.

Austin, J. L. (1975). How to do things with words. Oxford university press. Díez-Palomar, J., & Cabré J. (2015). Using dialogic talk to teach mathematics: The case of interactive groups. ZDM, 47(7), 1299-1312. Flecha, R. (2000). Sharing words: Theory and practice of dialogic learning. Lanham, Boulder, New York, Oxford: Rowman & Littlefield. García-Carrión, R., & Díez-Palomar, J. (2015). Learning communities: Pathways for educational success and social transformation through interactive groups in mathematics. European Educational Research Journal, 14(2), 151-166. Habermas, J. (1984). The theory of communicative action, volume 1: reason and the rationalisation of society. Beacon, Boston. Hutchins, E. (2006). The distributed cognition perspective on human interaction. In E.A. Schegloff, N.J. Enfield, and S.C. Levinson (Eds.), Roots of human sociality: Culture, cognition and interaction (pp. 375-398). Oxford, New York: Berg. Searle, J. R., Soler, M., & Castro, M. (2004). Lenguaje y ciencias sociales: Diálogo entre John Searle y CREA. El Roure. Soler, M., & Flecha, R. (2010). From Austin's speech acts to communicative acts. Perspectives from Searle, Habermas and CREA. Revista Signos, 43, 363-375. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of child psychology and psychiatry, 17(2), 89-100.