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

TL;DR

This paper develops a supervised hybrid statistical classification system to fairly assess students who have taken at least three out of four exams, using Gambian national exam data over seven years.

Contribution

It introduces a novel hybrid classification approach combining nearest neighbors and statistical rules for educational assessment.

Findings

The system effectively classifies students with incomplete exam data.

It demonstrates improved fairness in grading students with missing exams.

The approach is validated on seven years of Gambian national exam data.

Abstract

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.