The review study closely introspects’ on the prerequisites of evidence-based curriculum within the realms of specialized skills development agenda as pursued through higher education Institutions in Africa. Explicitly, the constraining factors that bedevil the leather sector are identifiable when appropriate research designs tools are applied. As such, in the process of identifying the constraints, renascence themes could, therefore, be beneficial in collecting evidence in support of developing curriculum. Such a developed curriculum stands higher chances of acceptability and aptly mitigates against challenges related to specialized skills development. The review succinctly indicates that in the process of identifying the themes, the scope of collecting evidence becomes attainable, thus, improving curricula that entails a participatory and transformative orientation. Indeed, during the review phase of the study, three main perspectives are depicted to be consequential in attaining a comprehensive, evidence-based curriculum, such as; action research, backward curriculum design perspective and theoretical perspective. Therefore, about this perspective, a reflection based on personal experiences and related to new knowledge with what they already know leads to constructivism. The relevancy of a constructivist strategy is observed to facilitate the observatory and evaluative stance during the development of evidence-based curriculum. Moreover, in consolidating and sustaining the benefit of such a developed curriculum, threshold concept was found during the review that it complements the process and strengthens the collecting evidence for curriculum development. Accordingly, therefore, the result of the review study indicate that Africa would position itself for initiating transformational changes in aspects of specialized higher education, fruition towards socio-economic benefits (e.g. employment, wealth creation and technology transfer), reversal of urban-rural or inter/intra continental migration flurry.
Model of Wireless Gas Sensors for Condition Monitoring of Electrical Transformers Based on Fog computing
