Authors :
Bashar Badamasi Lailaba; Shamsu Sani; Saifullahi Ahmad Tijjani; Hassan A
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/59rbfsn6
Scribd :
https://tinyurl.com/2h74zb8a
DOI :
https://doi.org/10.5281/zenodo.10099653
Abstract :
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
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
