Soumaila Dembélé | Searcharxiv

arXiv2020

Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process

As the first paper of a series of exploratory analysis and statistical investigation works on the Gambian \textit{GABECE} data based on a variety of statistical tools, we wish to begin with a thorough unsupervised learning process through descriptive and exploratory methods. This will lead to a variety of discoveries and hypotheses that will direct future research works related to this data.

stat.AP↗

arXiv2020

A Supervised Hybrid Statistical Catch-up System Built on Gabece Gambian Data

In this paper we want to find a statistical rule that assigns a passing or failing grade to students who undertook at least three exams out of four in a national exam, instead of completely dismissing them students. While it is cruel to declare them as failing, especially if the reason for their absence it not intentional, they should have demonstrated enough merit in the three exams taken to deserve a chance to be declared passing. We use a special classification method and nearest neighbors methods based on the average grade and on the most modal grade to build a statistical rule in a supervised learning process. The study is built on the national GABECE educational data which is a considerable data covering seven years and all the six regions of the Gambia.

stat.AP↗