10 months ago


816 R. Chitiyo, S. W.

816 R. Chitiyo, S. W. Harmon study, others are allowed and expected to emerge throughout the study’’ (Altheide 1987, p. 68). The benefit of this thematic approach to analysis is that it directly represents the perspective of the participants (emic view) rather than that of the researcher (etic view). In this study, analysis was ongoing, and involved the simultaneous coding of raw data and the construction of categories that captured relevant characteristics of the data being collected. As a means to interpret the data, Miles and Huberman’s (1994) data analysis was used. In this approach, analysis consisted of three concurrent flows of activity which started with data reduction, followed by data display and the drawing up of conclusions or verification. These streams of activity, as Miles and Huberman (1994) point out, form an interactive model in which the activities are ‘‘interwoven before, during and after data collection in parallel form, to make up the general domain called analysis’’ (p. 12). Data reduction—which was a continuous process from the beginning of the research right up to the writing up of the report—included the process of selecting, focusing, simplifying, abstracting, and transforming the data in written-up field notes or transcripts. As Miles and Huberman (1994) maintain, data reduction enabled the researchers to sharpen, sort, focus, discard and organize data in such a way that conclusions drawn from the analysis could be verified. However, they also warn, ‘‘It is important not to strip the data at hand from the context in which they occur’’ (p. 11). This was particularly true in this study where considerable emphasis was on understanding the context, since much of the meaning was in understanding the realities of the given situations. According to Miles and Huberman (1994), codes are efficient data-labeling and dataretrieval devices that empower and speed up analysis. The researchers started by creating a list of codes for each of the data sources that were used in the study. The list of codes helped the researchers to tie the research questions directly to the data. In this selective process of handling all this information from interviews, documents and questionnaires, some words and phrases had to be ‘‘hung on to throughout data analysis’’ (Miles and Huberman 1994, p. 56) because they rendered more meaning to given situations and contexts. Initially, descriptive codes, that is, ones that entailed little interpretation were used. Here, a class of a phenomenon (code), for example, ‘‘lecturers’ qualifications,’’ was attributed or attached to a segment of text. The same segment of text could also be interpretatively coded by, for example, looking at whether the lecturers’ qualification included any special training in educational technology and naming that code ‘‘lecturers’ special ET training.’’ As data collection commenced, and working more inductively by waiting for codes to emerge from the collected data, the researchers redefined and discarded codes that were not applicable or those that were ill-fitting. They persistently made sure the codes related to one another and to the structure of the research questions and that they were distinct from others in meaning (Miles and Huberman 1994). Data display enabled the researchers to organize a compressed assembly of the data collected and facilitate the drawing up of conclusions. Miles and Huberman (1994) say that by display, they mean a visual format that presents information systematically, so that the user can draw valid conclusions. This study used matrices to display data from the mass of text that was written or transcribed. As with data reduction, the process of displaying data was part of the interactive nature of the data analysis. In building the displays, see Table 1 for example, the row codes, for example, budgetary constraints/lack of funding, were drawn from the different explanations of constraints to instructional technology integration given by lecturers at the three institutions, and in 123

An analysis of the integration of instructional technology 817 Table 1 Display of all constraints faced by lecturers in using technology for instructional purposes Category of constraints Explanations given by the lecturers Institution A Institution B Institution C 1. Budgetary constraints/lack of funding 2. Poor Internet access and connectivity 3. Lack of relevant/ appropriate expertise 4. Absence of appropriate staff development 5. Unreliable electricity supply 6. Large class and/ or group sizes 7. Cultural and contextual relevance 8. Absence of ICT policy and technology integration framework 1. Poor/inconsistent availability of hardware and software 1. Narrow bandwidth 2. ‘‘Slow connection’’ 3. ‘‘Internet down’’ 1. ‘‘Lack of technological knowledge’’ 2. Lack of technology integration skills 3. Lack of appropriate technology use attitudes and awareness 1. Absence of appropriate staff development 2. Poor quality of the limited training 1. ‘‘Electricity blackouts’’ 2. ‘‘Electricity load shading’’ 1. Absence of resources 2. Failure to pay for technology and related expenses 3. ‘‘Failure to replace old computers with efficient ones’’ 1. Limited access 2. Slow dial-up 3. ‘‘Internet down sometimes’’ 1. Limited/lack of know-how, skills and knowledge in technology integration 1. ‘‘We do not know the basics’’ [of technology integration] 2. ‘‘Need for training on use of PowerPoint and upcoming programs’’ 1. ‘‘Frequent power outages’’ 1. Large numbers of students, limited supplies of technology 2. ‘‘Up to 60 students competing to use 3 or 4 computers’’ 1. ‘‘Software needs to be adapted to suit local curriculum’’ 2. ‘‘Majority of local schools do not have technological gadgets’’ 1. Absence of ICT policy and technology integration framework 1. Absence of resources 2. Absence of physical structures/infrastructure 3. ‘‘Failure to replace outdated technology’’ 4. ‘‘Failure to acquire required software’’ 1. ‘‘Poor connectivity’’ 2. ‘‘Very slow Internet speed’’ 3. ‘‘Not enough computers connected to Internet’’ 1. Lack of knowledge on technology integration 1. Absence of higher education institution offering degree-level training in ET 2. Absence of platform for sharing ideas 1. Absence of technological content relevant to own life and cultural experiences 2. ‘‘Available software has got Western biases’’ 1. Absence of policy on technology integration for student teachers response to the research questions. Entries in the table cells include summary phrases as well as direct quotes from the participants. Conclusion drawing and verification involved the noting of regularities, patterns, explanations, possible configurations, causal flows and propositions. To achieve this, the 123

Overview and Analysis of African Regional Networks - Science ...
Crisis in Zimbabwe - The Choices Program
5418 ISS Monograph 174.indd - Institute for Security Studies
COVER PAGE MDI 19-10-11 - Management Development Institute
Women at UCL Presence and Absence International Women’s Day 2016
Weather Integration Opportunities - RAL
HEARTY WELCOME to the new - Vellore Institute of Technology
Magazine October 2010 - Higher Education Commission
Download Document - Office for Learning and Teaching
Beneficiaries are actors too.pdf - Southern Institute of Peace ...
Higher Education Monitor - CHE
THE MYTH OF SOCIAL COST.pdf - Institute of Economic Affairs
Commons, resilience and relational legal pluralism - Juridicum
AR 2013 Optimized.pdf - W.E.B Du Bois Institute - Harvard University
Doctor of Business Administration - Namibia Business School
Governance of Financial Institutions in Southern Africa: Issues for an ...
African Higher Education Summit
ipeds data feedback report - Institutional Effectiveness & Analysis ...
Calestous Juma - Wallace Chair for Sustainable Agriculture - Iowa ...
module 4 - Institute for Security Studies
Annual Report 0809 August.cdr - AAU Resource Center ...
The role of institutional design and organizational practice for health ...