Imre Fekete: Technology in English teaching • The Hungarian university context
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3. Research Methods
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3.3. Study 3: The Learners’ Interview Study
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3.3.4. Methods of Data Collection and Analysis

3.3.4. Methods of Data Collection and Analysis

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Data was collected through Zoom, Microsoft Teams and Skype recordings depending on the preference of the participants. The interviews were preceded by short email correspondences about the aim of the interviews, which was to inquire into the preferences of the participants concerning their technology use in learning (and teaching) practices. Participants’ consent was asked so that the interviews could be recorded, their words transcribed and used for research purposes anonymously. Participants were also reassured that once the transcripts were ready, the interview recordings would be permanently deleted. After consent was given to record the interview, the recording was started, and consent was asked to be repeated by the participants as part of the recording. When the interviews were recorded, interview transcripts were created using Microsoft Word. Table 13 provides an overview of the duration and time of the interviews, and the length of the interview transcripts.
 

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Table 13 An Overview of the Learner Interview Data
Participant
Time of recording
Duration
Length of transcript
András
February 2021
33 mins
4,018 words
Andrea
November 2020
32 mins
3,895 words
Ágota
May 2021
34 mins
4,400 words
Diána
November 2020
31 mins
3,906 words
Flóra
February 2021
29 mins
2,951 words
Iván
April 2021
39 mins
4,469 words
Klára
April 2021
24 mins
2,791 words
Léna
November 2020
31 mins
4,218 words
Olga
November 2020
33 mins
4,361 words
Sándor
November 2020
41 mins
5,388 words
Tímea
November 2020
36 mins
5,186 words
Zsóka
September 2020
41 mins
5,396 words
 

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During the interviews, the researcher noted some frequently emerging themes in the data and began initial, open coding (Friedman, 2012), thus starting the content analysis of the interview texts (Xu & Zammit, 2020). Once some initial codes were established, coding continued with the help of QDA Miner Lite (https://provalisresearch.com/products/qualitative-data-analysis-software), a free qualitative data mining software. Initial codes were entered into the programme, and relevant interview quotations were linked to the codes manually. Axial coding began as more and more themes emerged, and previously coded interview segments were continuously revisited to revise coding (Friedman, 2012). Once the final codes were established, all relevant interview segments were selectively coded (Friedman, 2012) to fit into the final codes. The 12 interviews proved to be enough to reach data saturation because no new additional themes emerged when the final coding was finished.

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Before answering the research questions, two additional reliability measures were taken. First, 10% of the interview transcripts were re-coded a month later using the established final codes to enhance intra-rater reliability (Creswell, 2009; 2015). Additionally, another 10% of the interview texts were co-coded by a fellow PhD student from the same programme following a debriefing of the established code system to enhance inter-rater reliability (Creswell, 2009; 2015). Both quality control measures confirmed that the final code structure was a valid system of coding the interview data.

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In the end, 5 main codes and 45 subcodes were assigned to a total number of 452 interview segments. Table 14 summarizes final codes, subcodes and the frequencies of codes of the learner interview study. A sample coded segment and some additional label code samples are provided in Appendix E.
 

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Table 14 An Overview of the Final Codes and Coding Frequencies of the Learners’ Interview Study
Main code
(Sum: 5)
Subcode
(Sum: 45)
Times code was used (Sum: 452)
% of all codes
(Sum: 100%)
Number of participants
(N = 12)
% of all participants
1. Technology in general
1.1 Worldwide presence
10
2.2%
10
83.3%
1.2 Personal attitude
10
2.2%
7
58.3%
1.3 Origins of interest
3
0.7%
3
25.0%
1.4 Own devices
6
1.3%
5
41.7%
1.5 General advantages
13
2.9%
7
58.3%
1.6 General disadvantages
10
2.2%
7
58.3%
1.7 Free time technology use
8
1.8%
5
41.7%
1.8 Digital divide
9
2.0%
5
41.7%
1.9 Time consuming habits
8
1.8%
6
50.0%
2. Technological knowledge
2.1 Own level of knowledge
7
1.5%
7
58.3%
2.2 Knowledge development through technology
12
2.7%
7
58.3%
2.3 School vs real life expectations
3
0.7%
2
16.7%
2.4 Critical skills, criticality
11
2.4%
6
50.0%
2.5 Secondary education
8
1.8%
6
50.0%
3. Technology in studying
3.1 University’s expectations
11
2.4%
6
50.0%
3.2 Advantages for students
10
2.2%
7
58.3%
3.3 Examples of use (anecdotes)
21
4.6%
8
66.7%
3.4 Examples of apps, websites, devices used
16
3.5%
8
66.7%
3.5 University subject integrating technology
20
4.4%
10
83.3%
3.6 University instructors’ technology use
9
2.0%
7
58.3%
3.7 What the university lacks
16
3.5%
10
83.3%
3.8 Own language development
16
3.5%
7
58.3%
3.9 Own methodology development
3
0.7%
2
16.7%
3.10 Extramural language learning
9
2.0%
5
41.7%
3.11 Organisation and sharing
12
2.7%
7
58.3%
3.12 Thesis-related use
5
1.1%
3
25.0%
3.13 Use for homework
5
1.1%
2
16.7%
3.14 Reference websites
6
1.3%
5
41.7%
3.15 LMS in learning
6
1.3%
4
33.3%
3.16 Changes because of Covid
12
2.7%
6
50.0%
3.17 Peer rejection towards technology
3
0.7%
2
16.7%
3.18 Teaching peers about technology
3
0.7%
3
25.0%
4. Technology in teaching
4.1 What technology is used
24
5.3%
8
66.7%
4.2 How technology is used
41
9.1%
10
83.3%
4.3 Why technology is used
12
2.7%
5
41.7%
4.4 Online teaching practice
10
2.2%
6
50.0%
4.5 Online testing
2
0.4%
2
16.7%
4.6 On-site teaching practice
12
2.7%
7
58.3%
4.7 Private teaching / tutoring
12
2.7%
6
50.0%
4.8 LMS in teaching
2
0.4%
2
16.7%
4.9 Unreliability of technology in the classroom
2
0.4%
1
8.3%
5. Technology in the future
5.1 Development areas
18
4.0%
10
83.3%
5.2 Non-technological development
8
1.8%
3
25.0%
5.3 What stays after Covid
6
1.3%
4
33.3%
5.4 What goes back after Covid
2
0.4%
1
8.3%
 
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