Interaction Analysis: Methodological Perspectives on Learning and Communication

Organising units: NATED track 2 in cooperation with CHANGE research group and TransAction research group

Organisers: Ass. prof. Ingvill Rasmussen, Ass. prof. Hans Christian Arnseth, Dr. Anniken Furberg

Lecturers: Invited lecturers.

Dates and hours

1) 20 August 2013 (full day: 9:15 - 16)
2) 1 October 2013 (full day: 9:15 - 16)
3) 10 December 2013 (full day: 9:15 - 16)

Place: Infra, at InterMedia (map)

Understanding how student learning occurs in interactions between teachers and peers is an important field in the educational sciences. One way of studying these phenomena is to study social interaction as it emerges. In order to create accounts of learning and teaching, detailed studies of communication and action are required. This could be called data-driven analysis. This means that the researchers first analyze the data from the perspective of the participants. In these processes, the researchers use techniques that analyse the turns involved in talk and action, thereby creating themes and using sensitizing concepts from relevant studies. The next step is to connect the analysis to the theoretical perspectives used and the reviewed literature. In the course we will also show examples of studies based on predefined categories and the use of coding systems.

The increasing availability of affordable video equipment has made it easier to capture accounts of people’s participation in activities; however, the deeper-level insights are found in the long-developed theoretical and methodological discourses that can be traced in the literature. A large body of video-based studies now exists that examine social interaction in educational and work settings in many different knowledge domains (mathematics, science, language, history, etc).

  • The course is aimed at PhD students who, in their research, are working with questions about learning and communication and who rely on the use of interaction data (such as video- and/or voice-recordings and interviews).
  • The course is oriented towards learning how to analyze interactions in detail.
  • A variety of different approaches are addressed, including sociocultural perspectives, ethnomethodolgy, and argumentation theory. These provide the theoretical foundations for the course.

Learning outcome:

Target group: PhD students in the educational sciences and ?

Work format: The course will include lectures and hands-on training with empirical data. Based on the number and types of contributions we receive, the course will be organized more like a workshop,  which includes plenary sessions, plenary workshops, and group workshops.

Extent: 21 hours F2F meetings. A course diploma will not be issued if the student attends to less than 80% of the course hours.

Conditions for participation
Participants must present prepared data, i.e., material that is ready for analysis. The material will be presented and discussed during the course. You may choose one of the three following types of texts to prepare:

  1. A article that are almost finished. We will read the article, focusing on methodology, data selection, data presentation, data analysis, and the discussion.
  2. Paper in progress. These papers should present substantial pieces of data so that an external reader can derive ‘meaning’ from the data. When reading the papers, we will focus on methodology, data selection, data presentation, data analysis, and the discussion (if the discussion has been written and included in the paper).
  3. Presentation of data. Here, you can frame the data, which means you can present how the data is collected, the types of data that are collected, and the research problem that you want to address, and then you can give a presentation of the data. The length of the section on framing could be around one-to-two pages, and then the section that presents the data should not be more than four pages, if that data includes findings on the dense interactions between students. However, if the data includes findings obtained from the interviews and is, therefore, less dense, this section could be up to eight pages in length. If the data include a combination of student interactions and interviews, this section could also be up to eight pages.

Credit points: 3 studiepoeng (creditpoints equivalent to ECTS) with paper, 1 studiepoeng without

Language: English

Cost: Free

Number of participants: maximum 15

Registration: Within 1 June.

Ph.d.-students from the University of Oslo apply through Studentweb

Others apply through the application form


Course literature

These books create the foundation for the course:

Lemke, J. (1990). Talking Science: Language, Learning, and Values. Norwood, NJ: Ablex Publishing.

Linell, P. (2009). Rethinking Language, Mind and World Dialogically: Interactional and contextual  theories of human sense-making. Charlotte: NC: Information Age Publishing.

Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to medicated action. . Cambridge: MA: Harvard University Press.


Benwell and Stokoe (2006) Discourse and identity. Edinburg univ press. ch. 2 , p. 48-86.

Clark, D. B., & Sampson, V. D. (2007). Personally-seeded discussions to scaffold online  argumentation. International Journal of Science Education, 29(3), 253-277.

Derry, S. J., Pea, R. D., Barron, B., Engle, R., Erickson, F., Goldman, R., et al. (2010). Conducting Video Research in the Learning Sciences: Guidance on Selection,

Analysis, Technology, and Ethics. Journal of the Learning Sciences, 19(1), 3-53.

Engle, R. A., & Conant, F. R. (2002). Guiding Principles for Fostering Productive Disciplinary Engagement: Explaining an Emergent Argument in a Community of Learners Classroom. Cognition and Instruction, 20(4), 399-483.

Erickson, F. (2012). Qualitative Research Methods for Science Education. In B. J. Fraser, K. Tobin & C. J. McRobbie (Eds.), Second International Handbook of Science Education. New York: Springer.

Furberg, A., & Ludvigsen, S. R. (2008). Students' Meaning-making of Socio-scientific Issues in Computer Mediated Settings: Exploring learning through interaction trajectories. Internal Journal of Science Education, 30(13), 1775-1799.

Gilje, Øystein. (2010). Multimodal Redesign in Filmmaking Practices: An Inquiry of Young
Filmmakers’ Deployment of Semiotic Tools in Their Filmmaking Practice. Written Communication, 27(4), xxx-494.

Gordon, T, Holland, J. & Lahelma, E. (2001). Ethnographic research in educational settings. I  P. Atkinson, A. Coffey, S.Delamont, J. Lofland, & L. Lofland (Eds.), Handbook of   Ethnography. London: Sage

Jordan, B. & Henderson, A. (1995) Interaction Analysis: Foundation and Practice. The Journal of the Learning Sciences, 4, 39-103.

Ivarsson, Linderoth og Säljö (2009): Representations in practices - a sociocultural approach to multimodality in reasoning. Jewitt, C. (ed) The Routledge Handbook of Multimodal analysis.

Lemke, J. (2012). Analysing Verbal Data: Principles, Methods, and Problems. In B. J. Fraser,  K. Tobin & C. J. McRobbie (Eds.), Second International Handbook of Science  Education. New York: Springer.

Lindwall, O. & Lymer, G. (2008). The dark matter of lab work. Illuminating the negotiation  of disciplined perception in mechanics. The Journal of the Learning Sciences, 17(2), 180-224.

Lund, Andreas & Rasmussen, Ingvill (2008). The right tool for the wrong task? Match and  mismatch between first and second stimulus in double stimulation. International  Journal of Computer-Supported Collaborative Learning, 3_(4), s 25- 51

Mercer, N. (2004). Sociocultural discourse analysis: analyzing classroom talk as a social  mode of thinking. Journal of Applied Linguistics,_ 1,2 , 137-168.

O'Connor, M. C., & Michaels, S. (1993). Aligning academic task and participation status through revoicing: Analysis of a classroom discourse strategy. Anthropology and Education Quarterly, 24, 318-338.

Perry, N.(1998). Young children’s self-regulated learning and contexts that support it.  Journal of Educational psychology, 90, 4, 715-729.

Reznitskaya, A., Kuo, L., Glina, M. & Anderson, R. C. (2009). Measuring argumentative reasoning: What’s behind the numbers? Learning and Individual Differences, 19(2),  219-224.

Warwick, P., Mercer, N., Kershner, R. and Kleine Staarman, J. (2010) In the mind and in the technology: The vicarious presence of the teacher in pupil’s learning of science in 
collaborative group activity at the interactive whiteboard. Computers and Education,  55, 350-362.

Wortham, S. (2004). The interdependence of social identification and learning. American Educational Research Journal, 41(3), 715.

Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. Journal of the Learning Sciences, 2(3), 235-276.

Sum pages: 600 + selected chapters in the books.

Publisert 15. okt. 2015 14:17 - Sist endret 4. feb. 2016 12:45