Early-Warning Dropout Visualization Tool for Secondary Schools: Using Machine Learning, QR Code, GIS and Mobile Application Techniques

Abstract

: Investment in education through the provision of secondary school to the community is geared to develop human capital in Tanzania. However, these investments have been hampered by unacceptable higher rates of school dropouts, which seriously affect female students, since most schools do not have effective mechanisms for quality data management for immediate and effective decision making. Therefore, this study aims to solve the problem of data management from the school level in order to assist higher levels to receive appropriate and effective data on time through the use of emerging technologies such as machine learning, QR codes, and mobile application. To implement this solution, the study has explored the predictors of school dropout using a mixed approach with questionnaires and interview discussion. 600 participants participated in problem identification in the Arusha region. Through the use of design science research methodology, Unified Modeling Language, MYSQL, QR codes and mobile application techniques were integrated with Support Vector Machine to develop the proposed solution. Finally, the evaluation process considered 100 participants, and the results showed that an average of 89% of participants provided positive feedback on the functionalities of the developed tool to prevent dropouts in secondary schools in Africa at large.