New Perspectives in Science Education

Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focused on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through education process. Such mentality is coupled with tough curriculum that do not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study the, the focus is on developing and a model that can systematically be implemented by school administrations to prevent possible dropout scenario. At the elementary level, especially the lower grades, a child perception on education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models be installed in every educational system with a view to help prevent an imminent school dropout just before it happens. In a competency-based curriculum which most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduces rate of dropout. Data management challenges notwithstanding, a compelling model needs to be stable, reliable, valid and pragmatic.

Keywords: Dropout, elementary school, predictive models, machine learning, data management, risk factors.

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