Data analysis in the context of teacher training: code sequence analysis using QDA Miner\(^{\circledR }\)

This article is the fruit of a study carried out in the field of training teachers in the French Community of Belgium. The current teacher-training course is founded on a decree promulgated by the French Community of Belgium in February 2001 outlining thirteen skills to be developed in the initial training program. One of these, which, in our view, is especially crucial, is training young teachers to look critically at their own practices. Getting student–teachers to critique themselves continually challenges both those who train teachers (How to encourage them to do so?), and researchers (How to measure it?). The response to these two questions is essential if we are to move beyond thinking of self-evaluation as merely a sort of training “slogan” (Fendler, Educ Res. 32(3):16–25, 2003). In an attempt to create a research tool that can aid in the measurement of self-evaluation events, we have turned to computer assisted data analysis tools. After a brief description of the context in which this study has been carried out, this article will present the theoretical foundations underlying the data analysis in some depth, and an overview of two data analysis software packages: Nvivo\(^{\circledR }\) and QDA Miner\(^{\circledR }.\) Then we will take a closer look at the main subject of this article; i.e., one of the functions in QDA Miner\(^{\circledR },\) the Code Sequence Analysis Function, and its application in the analysis of two specific aspects of interaction between a student–teacher and his/her supervisor. More specifically, we shall examine the results obtained from applying code sequencing to student–teachers’ reflective processes, and to the thematic continuity/discontinuity that occurs during an interview. Finally, the conclusions outline the most significant results obtained as well as some other elements linked to data analysis tools which potentially merit further reflection.