Resumo

EKUBO, Ebiemi Allen  e  ESIEFARIENRHE, Bukohwo Michael. Using machine learning to predict low academic performance at a Nigerian university. AJIC [online]. 2022, vol.30, pp.1-33. ISSN 2077-7213.  https://doi.org/10.23962/ajic.i30.14839.

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.

Palavras-chave : machine learning; educational data mining; student academic performance; university; cross-industry standard process for data mining (CRISP-DM); Waikato Environment for Knowledge Analysis (WEKA); classifier algorithms; J48 decision tree; logistic regression (LR); multilayer perceptron (MLP); naïve Bayes (NB); sequential minimal optimisation (SMO); Nigeria; Niger Delta University.

        · texto em Inglês     · Inglês ( pdf )