Competencias

Since this study adopted the qualitative research approach, data was analysed using themes and sub-themes. According to Ryan and Bernard (2003: 87), themes are abstract, often fuzzy, constructs which investigators identify before, during, and after data collection. Themes mainly emerge from the empirical data during the process of investigation and later when it is being analysed. Therefore, thematic analysis is a method for identifying, analysing and reporting patterns (themes) within data (Braun and Clarke, 2006: 6). Nevertheless, the researcher may have certain preconceptions of the topic usually from literature and from the researcher’s prior theoretical understanding of the phenomenon being studied (Ryan and Bernard, 2003). These should help the researcher to notice the merging themes with ease especially in terms of making sense of the data. The situation on the ground should be able to dictate this operation.

The process of analysing the data followed the established methods in qualitative research approach. According to Cohen et al. (2007: 461), Qualitative data analysis involves making sense of data in terms of the participants’ definitions of the situation, noting patterns, themes, categories and regularities. This process was facilitated by the two data generation instruments that I used namely, interviews and document analysis.

As an investigator I expected to interact with data throughout the interview processes with the students’ focus groups and with the individual lecturers. This gave me a glimpse of how the responses from the interviews correlated with the information from the documents that I had analysed. Thus, I was analysing the data as I interacted with it. According to Ratcliff (2008: 120), data collection and data analysis in qualitative research form a cycle that repeats itself over and over until the data stops giving new information. Coleman, (2012: 262) contends that this should be on-going from the start of the interview process as the interviewer reflects on what they are hearing.

As such there was need for a well-coordinated way of data capturing and presentation. There are suggestions from various scholars as to how qualitative data may be organised and

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presented for analysis. One of these is by Ratcliff (2008: 122). He argues that the initial analysis of data should be organised and presented as follows:

1. Review of the Data- for the current study this is data that would have been written or collected through the day during interviews or document analysis.

2. Determining the Unit of Analysis and Coding the Data- I intended to develop a number of codes in the interviews as well as document analysis.

3. Developments of Categories- the various codes that I developed were grouped to form categories.

4. Connecting Categories, Identifying Themes, and Creating Hypotheses- in this study I connected the categories in order to identify and create themes and sub- themes.

These four suggestions reminded me as a researcher of the importance of developing codes and categories. This is how they were then connected in order to identify and create themes.

Another suggestion takes note of the importance of the respondents, the issues that arise and the instruments being used in the research. Cohen et al., (2007: 467) proposes the following ways of organising and presenting data analysis:

1. By groups- in the case of the present study this would mean organising the data by each Focus Group from the four core courses (modules).

2. By Individuals- in the present study there were four individual lecturers who were interviewed.

3. By issue- a number of issues did arise from the interviews as well as from the various documents that were collected for scrutiny.

4. By research question- there were three sub-questions which were expected to respond to the main research question in this study as pointed out in section 4.1. 5. By instrument- in the case of this study data was organised according to the Focus

Group Interview Guide for Students, the Lecturers’ Interview Guide, the Course Outline Analysis Schedule and the Quizzes, Tests and Assignments Analysis Schedule.

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Following on from the two sets of suggestions above and using interviews and document analysis as the two data generation instruments, I proceeded as follows:

A. Interviews:

Stage 1: During data generation: at the end of each interview session, I identified emerging themes, information gaps, reflected on own questioning techniques and planned to revise ways in the next session wherever needed.

Stage 2: After completing all interviews: I identified emerging themes and grouped data accordingly; I identified common responses within each question; I identified differences in views; then I identified patterns, did other similar processes and recorded accordingly.

Stage 3: I scrutinised themes in relation to research questions; I identified contradictions and shared responses; I did other similar processes and recorded accordingly.

B. Documents

Stage 1: I grouped data according to source such as course outlines and the themes therein.

Stage 2: I identified themes across document sources. Stage 3: I scrutinised data in relation to research questions. For each stage above I recorded accordingly.

C. Documents

Stage 1: I grouped data according to source such as course outlines and the themes therein.

Stage 2: I identified themes across document sources. Stage 3: I scrutinised data in relation to research questions. For each stage above I recorded accordingly.

D. All Data Sources Together

Stage 1: I identified any common themes, contradictions, differences, inter alia. Stage 2: I scrutinised all data in relation to research questions.

108 Stage 3: I made meaning of the data.

Stage 4: I created themes for the data presentation chapter (see Sections 5.1 and 5.2)

I then recorded accordingly for each stage above.