Thematic Data Analysis

I used thematic analysis to systemically analyse the study data. This process was essential to identify the significant features inherent within the data (Creswell, 2008). Through a process of ongoing reading, sifting, grouping and regrouping of the data, themes were identified that allowed me to gain an understanding of the salient features of the participants’ experiences and their contexts (Braun & Clarke, 2006). I used both inductive and deductive reasoning to locate and identify themes in the data. I drew on the work of Ezzy (2002), who argued that, when undertaking data analysis, “the researcher should enter into an ongoing simultaneous process of deduction and induction, of theory building, testing and rebuilding” (p. 10).

In this study, I approached the thematic analysis with some themes in mind (a deductive analysis approach); these were derived from the literature in Chapter Two and Three. At the same time, I was open to any new themes or ideas that emerged from my reading of the data (inductive). Whist undertaking the deductive thematic analysis I kept in mind the three sociocultural points (See section 2.6.1), and the ideas within the models set out in Chapter Three.

Teachers’ Interviews: Thematic Analysis

Inductive thematic analysis was employed for the teacher interview data. While this process is a widely used method for qualitative analysis, Braun and Clarke (2006) point out that thematic analysis is often described in a vague manner and is an under-theorised tool. However, they argue that because thematic analysis is not wedded to any pre-existing theoretical framework, it is a flexible tool, which

allows the researcher to identify themes or patterns in the data. Creswell (2008) argued that identifying themes allows the complexity and depth of storytelling to emerge, which adds insight to understanding individual experiences. However, themes are ultimately determined by the researcher’s judgment as they are reshaped as the analysis proceeds (Punch, 2009).

Thematic analysis focused teacher beliefs about and use of ICT in their teaching practice. While the interview questions provided some structure to the interview conversations (see Appendix H) the following aspects from the literature reviewed in Chapters Two and Three provided a lens for the thematic analysis and interpretation: the relational nature of learning, learning as a socially and culturally mediated process, and ICT cultural tools as mediating learning within a context. To analyse the teachers’ perspectives, I read and reread the transcripts. The data were initially chunked into topics relating to different beliefs and practices and then patterns and ideas within these were colour coded on the hard copies of the data. In this way, coding involved “the process of segmenting and labelling text to form descriptions and broad themes in the data” (Creswell, 2008). Finally, categories were noted and the number of teacher comments on each theme were counted and checked off accordingly. In this way, a thematic analysis of the teacher data enabled me to capture their perspectives, similarities and differences. As the analysis proceeded, I identified five themes in the data: the nature of ICT use as part of pedagogy, subjective norms, relational trust, and ICT as a cultural tool. These are explained below.

The nature of ICT use as part of pedagogy was the strongest theme in the data. Aspects were identified in the first instance by focusing on practices identified in the literature review – See section 2.5. Of these assessment was the main one. Student teacher and practicing teacher commentary construed the use of ICT for the documentation of learning and assessment for learning was a key aspect in teacher pedagogical use of ICT. Other aspects of pedagogy raised by the interviewees included building and sustaining relationships, children’s literacy learning, supporting inquiry learning, emergent curriculum and using ICT for research purposes.

Subjective Norms was the second strongest theme identified in the data. The notion of subjective norms, as used in this study, was drawn from Fishbein and Ajzen’s (1977) Theory of Reasoned Action, (See section 3.3). Subjective norms are "the perceived social pressure to perform or not to perform” as particular the behavior (Ajzen, 1991, p. 188). The theme of subjective norms was identified in teacher commentary about use of ICT as ‘custom and practice’ in their contexts (See section 5.3). For student teachers, subjective norms were categorised from comments that reflected their view of ‘what we would have liked to learn”, in particular them developing an understanding of the pedagogical affordances of technology and/or a desire to learn about these (See section 7.2).

The theme relational trust is defined in this study as a positive three-way relationship between technology, teaching and learning (See section 2.6.2) Comments were categorised as relational trust when teachers included mention of them trusting technology supported teaching and learning (See section 5.4) and student teacher comments included mention of trusting the technology they used in their ITE programme would contribute to their developing pedagogical practice (See section 7.3). This theme featured as the third strongest theme in the data. The theme ‘ICT cultural tools’ describes software applications of technology and the Internet that teachers used in their teaching practice and student teachers used in their ITE programme (See sections 5.5 and 7.3).

The theme of enjoyment was identified when a teacher or student teacher mentioned a sustained interest in the use of ICT in their teaching, including their describing a sense of fun and enjoyment whilst using ICT tools (See sections 5.6 and 7.5)

Student Teacher Online Data: Thematic Analysis

Inductive/deductive thematic analysis was also used for the student teacher online data. I employed the same process as outlined above for the teacher data. That is, I employed the same colour-coding process that I used for the teacher interview data set, coding by topic and developing categories and then building a cumulative list of categories with subthemes. Some of the student teacher postings were counted in more than one category when a particular student teacher commented more than once on the same idea. Hence, counts represent the number

of contributions and not the number of student teachers making a point. As the analysis proceeded, the same five themes that were identified in the teacher data were evident in the student teacher data set: pedagogy, subjective norms, relational trust, ICT cultural tools and enjoyment.

Using the categories identified in both data sets as a tentative structure, I returned to each data set, extracting quotes from participants for illustrative purposes, and began to look for relationships (Braun & Clarke, 2006; Creswell, 2008). The resulting reports and analysis are presented in the following chapters. I did not undertake interviews with student teachers as the phase one student teachers had graduated by the time the data was analysed.