Using Big Data to Determine Potential Dropout of Students in Some Selected Tertiary Institutions in Kebbi State, Nigeria

Being the essential component of modernity Big data has drawn a lot of interest from practitioners, scholars, and businesses. Given the significance of the education sector, there is a current trend to investigate how big data might be used in this industry to forecast learning results. Student dropout is a significant issue in higher education, affecting both universities and polytechnics. Time to graduation (TTG), which has a direct correlation with student dropout, is one of the key measures of university achievement even if there is no universally accepted way to measure the quality of education (Pineda Lezama, O., & Gómez Dorta, R. 2017). This declining rate indicates a percentage that results in losses of millions to billions of dollars on a global and state level. Yet, as society demands the contributions made by the population with higher education, such as: innovation, knowledge production, and scientific discovery, dropping out has an impact not only on the nation's economy and educational quality but also on the advancement of society. This offers a straightforward method for predicting potential dropouts based on their academic and demographic traits using fundamental statistical learning techniques. The study will be carried out at a few chosen tertiary institutions in Kebbi State.

Keywords:- Big Data, Demography, Dakin-Gari