Role of Recurrent Neural Networks in the Education Sector in Developing Countries

Abstract

This study focuses on the ability of Recurrent Neural Networks (RNNs) in solving education problems within the developing countries milieu. A lack of resources in education systems of these regions together with high dropout rates and ineffective means of students‟ individualization make RNNs effective since these networks process sequential data and patterns. As such, the study makes use of qualitative secondary data where research findings and data from the published literature, case studies, and reports inform the analysis of the use and effects, as well as the challenges to using RNN-based solutions in various contexts.

The studies show the application of RNNs in upgrading learning interfaces, prompt dropout prediction models, and resource management systems. For example, RNNs have shown a cut in the dropout rate by 15% in South Asia, an increase in student performance by 20% in Sub-Saharan Africa. However, challenges like inadequate access to Web services, high costs, inadequate teacher training, and culture pose a major barrier to utile use.

Therefore, there is the need to have an appropriate framework to uphold government, school, and providers‟ cooperation to enhance RNN technology implementation. Overcoming these barriers, RNNs can be continued as a tool for different groups with different learning needs and learning environment aspirations to get the kind of education that they would like to have in order to contribute to sustainable development in developing zones.