Analysis and discussion

As discussed in Chapter 4, the analytical framework for the initial interview analysis was deductively derived from Shulman‟s pedagogical content knowledge (PCK), see figure 4.2. This Chapter discusses the results of the initial analysis. During the initial analysis, new concepts emerged inductively from the interview data. These emergent concepts were used to carry out a second stage of analysis. The analysis using the emergent concepts highlighted the importance of the teachers‟ craft

knowledge of teaching (Leinhardt, 1990 and Burney 2004). This suggested that the emergent concepts could be reconceptualised to develop a new framework for teachers‟ knowledge of teaching. The new framework for teacher development is called Craft Pedagogy because it is based on teachers‟ craft knowledge and is discussed in section 5.5.

Most of the discussion in this Chapter is based on the analysis of the interviews for Teachers 1 to 6 with supporting evidence offered for this analysis using the

interviews with Teachers 7 to 15. This approach was adopted to reflect the number of interviews with individual teachers. Teachers 1 to 6 were the only teachers to take part in Interview 3 at the end of the Probationary Year. Four of these teachers took part in Interview 4 at the end of the data collection phase. Teachers 7 and 8 took part in Interviews 1 and 2, Most of Teachers 10 to 15 took part in Interview 1 only, and Teacher 9 took part in Interview 2 only.

The code used to identify the quotations from the teachers in the rest of this Chapter is based on the teacher number, interview number and question number. For example, T1.2.3c would refer to Teacher 1, Interview 2 and Question 3c. The interview schedule can be found in Appendix 4.1.

5.1 INITIAL ANALYSIS USING PCK AS THE

ANALYTICAL FRAMEWORK

This section analyses the data obtained from the teachers using PCK as the initial analytical framework. The teachers‟ content knowledge, curricular and pedagogical knowledge were analysed using interview data. In addition, the student teachers‟ content knowledge was analysed using data obtained from concept mapping and questionnaires. During this initial analysis, I derived additional analytical categories which are discussed in more detail in section 5.2.

5.1.1 Content knowledge

The student teachers‟ content knowledge at the start of the PGDE course drew on their own learning in school, at university, and potentially from their previous professional lives. Shulman‟s original 1986 conceptualisation of PCK assumed that the content knowledge of (beginning) teachers was equivalent to that of graduates in that subject. This was also the entry assumption for the PGDE course. However, applicants for the PGDE Physics course came with a wide range of degrees which had been deemed to contain sufficient relevant physics content. The minimum requirement was that applicants had first degrees which covered electricity and mechanics. The wide variety of the student teachers‟ background knowledge of electricity suggested that it would be appropriate to explore the student teachers‟ content knowledge about electricity, particularly in the light of the concerns raised about teachers‟ content knowledge in the literature by McDermott, et al. (2006) and Gunstone, et al. (2009) among others. Nevertheless, overall the student teachers seemed to be confident in their content knowledge about electricity and were considering how to make this knowledge accessible to pupils:

"This is where you get me confused [laughs] now because I know how things work myself, but then obviously how do you explain? How do you pass that information on [to pupils]?" [T1.1.3a]

The student teachers‟ content knowledge about electricity was investigated using a combination of concept mapping, the DIRECT 1.2 questionnaire and repeated semi- structured interviews. The rest of section 5.1.1 discusses the results and analysis of the concept mapping exercise, the DIRECT 1.2 questionnaire and some of the results from the repeated semi-structured interviews.

Concept mapping

Concept mapping was chosen as a method to explore the student teachers‟ content knowledge because it would allow the student teachers of physics, chemistry and biology to demonstrate the structure of their understanding through the links that they made between the concepts in their concept maps and allowed possible

differences in understanding between the subjects to be explored. A voluntary task, Appendix 5.1, was focused on 27 basic electrical concepts taken from the 5-14 Guidelines for Science (Scottish Executive, 2000). Overall, 49 out of 72 students submitted concept maps. Table 5.1 records the number of concepts maps returned by student teachers of different subjects. The “undeclared” row shows that 6 student teachers returned concept maps but had not recorded their teaching subject on the concept maps.

Table 5.1 Number of concept maps returned for each subject.

Subject Number of concept maps

Biology 19 Chemistry 10 Physics 14 Undeclared 6 (Non- submission) (23) Cohort 72

Structure of concepts maps

One of the most visible aspects of the concept maps was their structure (Kinchin, Hay and Adams, 2000). The initial expectation was that, as experienced learners, the student teachers would produce expert concept maps, characterised by a network structure with multiple links between concepts (Edmondson, 2005). In practice, the student teachers drew maps which combined aspects of chain and spoke concept maps rather than net concept maps. Figure 5.1 shows an example of a concept map by a student teacher of physics which was transcribed into the Cmap programme to aid analysis. The concept map is read from the top down. Concepts are contained in text boxes and joined by linking phrases. A proposition consists of two concepts joined by a linking phrase, for example “ELECTRICTY can operate COMPONENT (sic).” The concept map in Figure 5.1 contains three main branches (or spokes) each of which is mainly composed of chains. This example is unusual in that it contains more cross-links than a typical student teacher‟s map. Despite the relatively large number of cross-links in the concept map, it does not display the organisation of concepts or the rich web of cross-links typical of an expert concept map (Shavelson and Ruiz-Primo, 2005).

Figure 5.1 Exemplar concept map by student teacher of physics.

Statistical significance

A descriptive statistical analysis of the number of propositions in each concept map was carried out. This showed that the numbers of concepts contained in the concept maps were not normally distributed. The non-normal distribution meant that the analysis used non-parametric statistics, focusing on the median and semi-

interquartile range (SIQR). The data are displayed in Table 5.2 and Figure 5.2. Table 5.2 shows the median number of propositions and the SIQR in each concept map for each subject. The median was used rather than the mean because the median was used to construct the boxplot in Figure 5.2.

Table 5.2 Median number of propositions and semi-interquartile range for each subject. Subject Median number of propositions SIQR All 30.0 5.9 Biology 25.5 3.8 Chemistry 23.0 7.0 Physics 27.5 3.6

Figure 5.2 displays the non-normally distributed data in a boxplot. The box shows the spread of exactly half the propositions in each case and the distance between maximum and minimum points gives the spread between the maximum and minimum number of propositions included. The median scores of the physics, chemistry and biology student teachers were compared using Kruskal-Wallis tests. The results showed that there were no statistically significant differences between the groups and hence that it was not possible to distinguish the concept maps belonging to the different groups of student teachers.

0 10 20 30 40 50 60 70 80

ALL Biology Chemistry Physics

Number of propositions