Details

Autor(en) / Beteiligte

• Ekubo, Ebiemi Allen
• Esiefarienrhe, Bukohwo Michael

Titel

Using machine learning to predict low academic performance at a Nigerian university

Ist Teil von

• The African journal of information and communication (Online), 2023, Vol.30, p.1-33

Ort / Verlag

LINK Centre, School of Literature Language and Media (SLLM), Faculty of Humanities, University of the Witwatersrand

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• This study evaluates the ability of various machine-learning techniques to predict low academic performance among Nigerian tertiary students. Using data collected from undergraduate student records at Niger Delta University in Bayelsa State, the research applies the cross-industry standard process for data mining (CRISP-DM) research methodology and the Waikato Environment for Knowledge Analysis (WEKA) tool for modelling. Five machine-learning classifier algorithms are tested-J48 decision tree, logistic regression (LR), multilayer perceptron (MLP), naïve Bayes (NB), and sequential minimal optimisation (SMO)-and it is found that MLP is the best classifier for the dataset. The study then develops a predictive software application, using PHP and Python, for implementation of the MLP model, and the software achieves 98% accuracy.