Open AccessA Supervised Hybrid Statistical Catch-Up System Built on GABECE Gambian Data
Author(s) -
Tagbo Innocent Aroh
Publication year - 2020
Publication title -
african journal of applied statistics
Language(s) - English
Resource type - Journals
ISSN - 2316-0861
DOI - 10.16929/ajas/2020.851.245
Subject(s) - statistical learning , modal , statistical analysis , computer science , statistical hypothesis testing , process (computing) , mathematics education , machine learning , artificial intelligence , statistics , psychology , mathematics , chemistry , polymer chemistry , operating system
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 five in a national exam, instead of completely dismissing these 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.