Open AccessA KNN Classification Advisory System For Higher Institution Students’ Performance
Author(s) -
Royransom Nzeh,
AUTHOR_ID,
Collins N. Udanor,
Izuchukwu Uzo,
Nnamdi J. Ezeora,
Nnaemeka E Ogbene,
Caroline Asogwa,
Assumpta O. Ezugwu
Publication year - 2021
Publication title -
advances in multidisciplinary and scientific research journal
Language(s) - English
Resource type - Journals
ISSN - 2488-8699
DOI - 10.22624/aims/abmic2021-v2-p1
Subject(s) - computer science , institution , academic institution , k nearest neighbors algorithm , data mining , higher education , data science , database , machine learning , political science , library science , law
This study focuses on enhancing students' performance through data mining techniques to reduce the challenges of low grades associated with some students in higher institutions in Nigeria. Presently, the amount of data stored in most higher institutions' databases are increasing rapidly. Nevertheless, some higher institutions still rely on paper files to store students’ results. Even those institutions with databases do not know what to do with their data after graduating the students. Thus, this study proposes a method that will use data mining techniques such as the k-nearest neighbor's algorithm to extract the hidden knowledge available in the higher institution databases. The proposed method of classifying students' performance is useful in identifying the poor students by providing the framework that will guide them to acquire better grades or change to other departments where they may be better suited. Keywords: Advisory, classification, data mining, KNN, performance.