<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students’ academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT), <italic>K</italic>-Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (<italic>k</italic> = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew’s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students’ academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students’ academic performance.</p>
</abstract>

The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students’ academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT), K-Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (k = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew’s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students’ academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students’ academic performance.

Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features

Department of Computer Science and Informatics

University of Energy and Natural Resources

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and...

Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 122 - 131

DOI: https://doi.org/10.2478/acss-2021-0015

Schlüsselwörter
Academic performance, AdaBoost, artificial neural network, decision tree, educational data mining, k-nearest neighbour, logistic regression, machine learning, naïve Bayes, random forest, support vector machine

© 2021 Bridgitte Owusu-Boadu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 122 - 131

DOI: https://doi.org/10.2478/acss-2021-0015

SchlüsselwörterAcademic performance, AdaBoost, artificial neural network, decision tree, educational data mining, k-nearest neighbour, logistic regression, machine learning, naïve Bayes, random forest, support vector machine

© 2021 Bridgitte Owusu-Boadu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Academic performance, AdaBoost, artificial neural network, decision tree, educational data mining, k-nearest neighbour, logistic regression, machine learning, naïve Bayes, random forest, support vector machine