Project topics and materials
Get Complete Project Material File(s) Now! »
Interpretation of factors related to ICT implementation
An exploratory factor analysis is used in the description of the covariance relationships among the many variables in terms of a few underlying but unobservable, random quantities known as ‘factors’. Factor analysis is a special case of the principal component method in which the approximations are more elaborate. In the context of factor analysis, various methods can be used in the selection of variables that are contributing to the dependent variable of interest. The two most popular methods of parameter estimations are the principal component, its related principal factor method and the maximum likelihood method. As Richard and Dean (2002) pointed out, the solution from these two methods can be rotated in order to simplify the interpretation of the factors. The two approaches are discussed. The principal component solution of the factor component: In this case two methods can be used to determine the factor analysis solution, which is the number of factors that are significantly explaining suitable proportion of the total variance in the sample. These are mainly the eigenvalue tabulations and the scree plots. In particular, the eigenvalues ( l ) are real numbers representing the variation accounted for by each component (factor) and that satisfy the equation A− l x = 0 , where A is a correlation matrix calculated from the observations to be classified and x a non – zero solution vector. On the other hand a scree plot is a plot of all eigenvalues in their decreasing order. Hence as a rule of thumb, the number of factors are then given by those factors with l ³1 which are equivalent to the substantial elbow in the scree plot. These two methods can be used to supplement each other (concurrently), however we have only presented the results of the eigenvalue tabulation as those of the scree plot at times are difficult to determine exactly the position of the elbow in the plot.
On the other hand, the maximum likelihood estimates of the factor loadings and specific variance can be used when the factors (common and specific) are assumed to be normally distributed. It is also important to point out here that both of these methods were calculated based either on a sample covariance or a correlation matrix of the sample data. The maximum likelihood is more common in the estimation of the rotating factor loadings from a principal component analysis through the varimax procedure by Kaiser (1958), and will be used as such in this study.
In this study, several variables under various constructs are considered. These constructs are as follows: Principal and its relevant constructs, science teachers (see Chapter 5) and its relevant construct, and the ICT Technicians and their relevant constructs.
Digital Learning Materials
However, with respect to Digital Learning Materials, the PC analysis retains three factors with eigenvalues in the range of 3.329 1 l = and altogether they are accounting for about 61.2% of the total variation in the sample (Table 6.17). In addition, the results of the communalities and the varimax rotated factor loadings (Table 6.18) shows that factor 1 comprises variable: availability of equipment and hands-on material, availability of simulation software, availability of communication software, availability of mail accounts for teachers and availability of mail account for learners, factor 2 has a variable of availability of multi-media production tools, availability of digital resources and availability of mobile services, and with variable availability of office suite and availability of mail account for learners making up factor 3. These factors can therefore be referred to as software availability (factor 1) Digital resources (factor 2) and also software application (for factor 3).
Digital Learning Materials
However, with respect to Digital Learning Materials, the result of the PC analysis shows that four factors with eigenvalues ranging between 5.543 1 l = and 1.023 42 l = accounting for a cumulative percentage of about 67.1% of the total variation in the sample (Table 6.21) were retained. The respective communalities and the varimax rotated factor loadings (Table 6.22) show that factor 1 comprises variables on extended projects (2 weeks or longer), short-task projects, product creation, selfaccessed courses and/or learning activities, and scientific investigations. Factor 2 comprises variable exercises to practice skills and procedures, laboratory experiments with clear instructions and well-defined outcomes, discovering science principles and concepts, studying natural phenomena through simulations, and looking up ideas and information. Factor 3 takes on variables in field study activities and teachers’ lectures and processing and analyzing data making up factor 4
respectively. As a consequence one can therefore refer to these factors as Science projects (factor 1) Instructional learning (factor 2) Investigation of scientific principles (factor 3) as well as Data analysis (factor 4).
CHAPTER 1 INTRODUCTION
1.1 Introduction
1.2 The research problem and questions
1.3 The research aims and objectives
1.4 An overview of the research design
1.5 Significance of the research
1.6 Overview of the thesis
CHAPTER 2 CONTEXT OF THE STUDY
2.1 Introduction
2.2 Geographic, political and socio-economic status of Namibia
2.3 The Namibian Education system
2.4 Realising Vision 2030 through the Education and Training Sector
2.5 Description of the Namibian ICT Policy for Education
2.6 Conceptualisation of the problem
2.7 Importance of the study for the Namibian context
2.8 Conclusion
CHAPTER 3 LITERATURE REVIEW
3.1 Introduction
3.2 Definition of concepts and keywords
3.3 Rationale for use of ICT in education
3.4 General use of ICT in Education
3.5 ICT implementation in the developed world
3.6 ICT implementation in the developing world
3.7 Factors affecting ICT implementation at school and teacher level
3.8 Conceptual framework
CHAPTER 4 RESEARCH DESIGN AND METHODS
4.1 Introduction
4.2 Research paradigm
4.3 Research design
4.4 Methodological norms
4.5 Ethical issues
4.6 Conclusion
CHAPTER 5 ICT IMPLEMENTATION IN SCIENCE CLASSROOMS
5.1 Introduction
5.2 Biographical information of the science teachers
5.3 Description of ICT use in science classrooms
5.4 Case studies’ findings on ICT use in science classrooms
5.5 Cross case analysis
5.6 Conclusion
CHAPTER 6mFACTORS AFFECTING ICT IMPLEMENTATION IN RURAL SCHOOLS
6.1 Introduction
6.2 Background of respondents
6.3 Profile of rural schools and ICT use
6.4 Interpretation of factors related to ICT implementation
6.5 Factors predicting ICT implementation in rural areas
6.6 Findings of school level case studies
6.7 Conclusion
CHAPTER 7 ICT USE CONFERENCE FINDINGS
7.1 Introduction
7.2. Conference participants’ perceptions’ of ICT implementation in rural schools
7.3 Conference participants’ views about factors affecting ICT implementation
7.4 Summary of the negotiated findings for the study
CHAPTER 8 CONCLUSIONS AND RECOMMENDATIONS
8.1 Summary of the research
8.2 Summary of the research findings
8.3 Reflections
8.4 Conclusions and Recommendations regarding ICT implementation in rural areas
REFERENCES
