Transcription

The first step in qualitative data analysis is to transcribe data. Transcription is a process, which is theoretical, selective, interpretive, and representational. It is how spoken language is transformed into written text (Jenks, 2011). Transcription represents language that has been transcribed from tapes or videos (Davidson, 2009). Interviews and observational notes were transcribed to produce written texts from which I can understand perceptions of lecturers and students. I transcribed all interviews and observational notes alone to ensure consistency and accuracy of representation of video-recorded conversations (Lapadat cited in Chitera, 2009). I first formulated a transcription protocol to ensure consistency in the transcription process (Stuckey, 2014) (see Table 14).

Table 14: Conventions used in the Data Transcription Process

Symbol What the symbol stands for

……….. Silence, repetitions, pause, and hesitations of the respondent aaa aaa Missing words or mannerism

Hmmmm In agreement

() Laughing of the respondent

To minimise errors and maximise the quality of the transcription, I reviewed each transcript produced. The transcripts were compared to video-recordings by listening to the videos with transcription (Appendix, J) in hand to ensure that what was written in the transcripts was the true reflection of what was said. The common errors were found to be misunderstood words, misspelled words, gaps, omission and miscommunication such as variation in pitch, volume, voice and length of silences. I reduced these errors by including some of the non-verbal cues wherever it was necessary in order to understand the text (Stuckey, 2014). To confirm the veracity of transcriptions, I conducted a member checking exercise soon after transcription before the respondents forgot their interview responses (Long & Johnson, 2000). Member checking is a technique used for exploring the credibility of results (Walter et al., 2016). Member check allows the researcher to validate and strengthen the validity of the results (Burnard, 1991). However, data that was in Chichewa was translated to English during the transcription process. In the observational notes Chichewa phrases were translated into English in real time. Chichewa is a vernacular language that can only be understood by Malawians. The next section outlines how I used the Cultural Historical Activity Theory (CHAT) and a content analysis approach to analyse the research data.

According to Hettinga (1998), CHAT examines the relation between object and community, which is mediated by the division of labour. The Division of labour in CHAT provides for distribution of actions and operations among a community of workers (Hyland, 1998). The basic unit of analysis in CHAT is an activity, which is used to understand individual or group actions (Jones, 2007). Leont’ev provides a good example of this paradox for primitive hunters when undertaking a collective hunt. The tactic was to incorporate two groups: one group, which would beat the bushes and scare the prey, and the other group would trap the scared animals and

conclude the hunt (Kuutti, 1996). For this hunting activity to be successful, both groups divided equally their roles or actions towards the catching of the prey. The group, which was beating the bushes and scaring the prey contributed to the group that caught and killed the prey. Therefore, this example implies that the activity is the exercise of hunting prey, one of the actions is to scare the animals, and shaking the branch of a tree is the operation. The activity will have a motive in this case, the team is motivated by the need to catch food. The action will have a goal, in this case, to make as much noise and disruption as possible (Jones, 2007). Emanating from Leont’ev example, in the teaching and learning activity, different groups or individuals should divide roles, actions or tasks among themselves for the use of computer in teaching to be successful. For example, the Ministry of Education and parents should conduct their tasks of providing computer technological resources to colleges and students respectively. The task will have a motive, in this case, motivating lecturers to use the computer in teaching. Lecturers should perform the task of demonstrating commitment to using computer technology. Students should also play their part of having the ability in computer use, which will help to support lecturers in using the computers for mathematics teaching. The subjects’ motivation was analysed based on different tasks, which the community perform toward an activity as part of their labour. In the field of education, CHAT provides the different lens for analysing learning processes and their outcomes on human activities. For example, it can be used to analyse how people do things (elements of activities) and the relationship between them (togetherness) with the assistance of tools in a complex environment (Crawford & Hasan, 2006). In this study, CHAT was used to analyse the tasks or actions of individuals that influence the lecturers’ perceptions on computer usage. For instance, the actions that the Ministry of Education was obliged to complete were considered to be the tasks (i.e. to provide computer policy in colleges), which the ministry should fulfil in order to motivate lecturers to use the computer in the classroom instructions. The analysis was based on the perception of whether tasks were fulfilled; tasks which would lead to the lecturers’ motivation and subsequent positive attitudes toward computer use. If the task was not fulfilled, I assumed that demotivated lecturers had negative attitudes toward computer use. CHAT was also used to analyse the subjects (lecturers and students) as they are part of the community. Consequently, when lecturers and students believe that computers are important learning tools or provide an indication that they are

interested in the use of computers, they have positive attitudes toward computer use. The activity system allowed me to explore the beliefs/perceptions of the lecturers, students and other members of the community, such as parents.

CHAT is a theoretical framework, which can be used to analyse the human practices on the multiple dimensions of individual activities and social interaction (Kuutti, 1996). It is used to understand how people can do things together (Crawford & Hasan, 2006). CHAT emphases who does what in the activity system (the division of labor). In this study, CHAT was used to analyse how each part of the Malawian TTC community tasks (the division of labour) contributes to lecturers' perceptions and students' views on computer use in the mathematics classroom. The community, in this case, comprises of the Ministry of Education, colleges, lecturers, parents, and students. CHAT promotes the consideration of the practices of individual lecturers, students and institutions (Murphy & Rodriguez-Manzanares, 2014). I categorised the practices (tasks) of the institution (Ministry of Education), school (college), students, and family into two before I starting the coding process. For example, I coded all tasks, which indicated that the institution, college, students, and family, had fulfilled tasks, which may influence lecturers' perceptions and students' views positively, as category 1. The tasks, which indicated that the community under investigation did not fulfil any task and could lead to lecturers’ negative attitudes toward computer use, were coded as category 2.

In order to construct meaning from these categories and examine different outcomes, which have been influenced by the interaction within the system, I employed a content analysis approach. Content analysis is defined as “a method for inquiring into the social reality that consists of inferring features of a nonmanifest context from features of a manifest text” (Krippendorff, 1980: 15). I inferred the perceptions or views of the lecturers and students from the concerns expressed during interviews and observational notes, in two phases. In the first phase, I conducted directed content analysis to validate and extend concepts or variables of CHAT. Directed coding (Miles & Huberman, cited in Saldana, 2009) referred to as provisional coding, is the coding that begins with predetermined list of codes generated from the conceptual framework or previous research findings (Miles & Huberman, cited in Saldana, 2009). I identified key concepts of CHAT (community tasks) and findings from the quantitative study as my initial coding categories. In this

study, categories are six themes of perceptions, which served as the framework to identify the lecturers and students' perceptions on the use of computer technology. I used probes to explore participants’ experience from the text. I read and highlighted all text that at first impression appeared to represent the lecturers’ perceptions on computer use. I coded all the highlighted phrases and sentences using the predetermined themes. Codes are ways of discovering, identifying and labelling repeated evidence collected from qualitative data, surveys, interviews, observations, and focus group (Saldana, 2009). Coding linked the data to the idea, and the idea to all the data pertaining to the idea (Richards & Morse, 2007), in this case, the perceptions. To show that codes were linked to ideas, I presented codes with their exemplars (Nvivo coding whereby words are taken directly from the participants) (Saldana, 2009). I coded any text that would not be categorised with initial coding with a new code and analysed later to determine whether they represented a new category or subcategory of an existing code that was done in the second phase using conventional content analysis approach.

I coded the data using both directed and conventional content analysis by following six systematic guiding questions, as formulated by Krippendorff (1980), which should be addressed in every content analysis:

1) Which data are analysed? 2) How are they defined?

3) What is the population from which they are drawn?

4) What is the context relative to which the data are analysed? 5) What are the boundaries of the analysis?

6) What is the target of the inferences?

The data analysis phase focused all the data gathered the transcripts of face-to face interviews, focus group and observational notes. I did not sample from the data, instead, 49 pages were all included in the analysis because they contained the information that answered the research questions.

Participants were four mathematics lecturers (two females and two males) and 12 student teachers (six females and six males). Two lecturers came from two private

colleges and two came from one public college. They were purposively selected as they indicated in the questionnaire to have taught mathematics for more than 13 years. The participants also indicated that they use computer technology when teaching mathematics. They came from different colleges, which were under differing administrative structures that may influence their perceptions differently.

The interviews were semi- structured face-to-face and video-recorded and conducted using an interview guide. I conducted interviews in different location, where two of the interviews were conducted at the participants’ colleges, one at a participant’s home and the other one through a phone call interview. Interviewees were assigned pseudonyms, which were used in the research reporting (Mrs. Subira, Mrs. Chimubangala, Mr. Mwalubunju and Mr. Muliwaki).

Student teachers were purposively selected because they were doing their teaching practice in the second year. They had already spent one year in college learning theoretical mathematics in the classrooms. I assumed that they had enough experience on how mathematic is taught in colleges (see Chapter 1, Table 1). The students were divided into two groups of six females and six males as students are deployed in schools according to their gender. The video-recorded focus group interviews were conducted in their schools. The students were assigned numbers from 1-12, which were used in the analysis.

To enhance validity, environmental triangulation was conducted (Patton, 2002; Rolfe, 2006) and the data was collected from three different regions in the country: in the northern region from a college owned by the non-governmental organization. In the central region, data was collected from a college owned by the government and in the southern region from a college owned by a church. Environmental triangulation is the use of different locations, settings and other key factors related to the environment in which the study took place (Guion, Diehl, & McDonald, 2011). The data was also collected from lecturers and students using face-to-face interviews, focus group interviews, and observational notes.

All repetitions, silence, pause, and hesitations were included and represented in the form of lines or dots (see Table 14). Concomitant occurrences such as laughter were in brackets. For example, interviewee Hmmm (in agreement).

The observational notes were made when I visited one of the public and private colleges. I also made notes when I visited one of the focus groups at one of the primary schools. I wrote my observations and posed questions for clarification. The notes were also transcribed and coded.

5.2.1. Interviews and observational notes

The interviews and observational notes were intended to report on the participants’ perceptions/views, feelings on computer use in college mathematics classrooms. They were also to cover the situation of computer use, factors that may influence lecturers' perceptions of computer use and barriers that hinder computer use in the mathematics classrooms. I extracted meaning units from the texts that I used to arrive at words, phrases or statements on views, feelings, beliefs, values, and tasks, which were performed by the community including the participants. A meaning unit is the smallest unit that contains some of the insights the researcher needs, and it is a group of associated sentences or paragraphs containing aspects related to each other, answering the research questions, which were derived from the aim (Graneheim & Lundman, 2004). I also used sub-themes under each main theme to extract codes from the texts. Sub-themes are the smallest units based on meaning units, which sometimes are the same as the codes of the meaning unit (Graneheim & Lundman, 2004). Therefore, I employed six themes that were formulated from the factors, which were analysed in quantitative study before I started coding because the study followed a deductive reasoning design (see Chapter 3, section, 3.4.1: 71). These themes were: lecturers’ and students’ perceptions, institutional factors, school factors, student factors, family factors, and barriers. At first, I coded under lecturers and students' perceptions any word, phrase or statement that indicated that participants had positive attitudes toward computer use. Institutional factors in this study correspond to the Ministry of Education factors, which according to CHAT are tasks, which the community is supposed to do in order to influence the lecturers’ perceptions on computer use. Coding was based on positive and negative influences. For example, if the ministry performed the task, it would be seen as having influenced the lecturers’ perceptions positively. If the ministry did not perform the task, this task would be seen as having influenced the lecturers’ perceptions negatively in the use of computer technology. I followed the same procedure in the selection of codes from the texts for school factors, or college factors in this study,

student factors and family factors. However, family factor codes explained the views of students in the same way that institutional factors explained the views of lecturers. Codes for barriers that hinder computer use in mathematics classrooms were identified from the questionnaire items and interviews questions, which required the informants to state factors, which influence computer use in the mathematics classrooms. I used codes to answer the research questions through inferences drawn from the texts.

To have a complete understanding of the lecturers’ perceptions and students’ views on computer usage I used the abductive inferences also referred to as complementary (Blackstone, 2012) or combined (Elo & Kyngäs, 2008). Abduction is defined as a way of discovering meaningful understanding patterns, which make it possible to integrate surface and deep structures (Eriksson and Lindström, 1997, 1999). I inferred the texts from interviews and observational notes to the perceptions of lecturers and students’ views by moving back and forth between deductive and inductive approaches while answering the research question (Graneheim, Lingren & Lundman, 2017). The question examined the perceptions/views of Malawian mathematics college lecturers and students on the use of computer technology in mathematics classrooms. The second question determined how Malawian mathematics college students perceive the use of computer technology in the mathematics classrooms.

5.2.2. Coding process

I selected codes that may answer the research question by dividing the tasks of the community which lecturers and students are part of it into two categories according to the themes. I defined how each code was selected, providing examples and explaining the rules, which I followed when coding (see Appendix H). For example, when I was coding lecturers and students’ perceptions/attitudes, I divided the perceptions into two categories: positive and negative. Where lecturers responded that “I am interested in teaching using computer technology”, this was considered as a positive perception. Where they stated that “I am not interested in computer use”, this was seen as a negative perception. Where students indicated that learning by the use of a computer is important, the statement was coded as positive views toward computer use. Therefore, the codes from six themes were used to infer

perceptions of lecturers and students’ views on computer use in the mathematics college classrooms according to how I defined the codes, gave examples and explained the rules. The first theme in this directed content analysis coding phase is the lecturers and students’ perception/views.

5.3. First phase, Directed content analysis: Lecturers’ perceptions