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Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets

by God’swill Theophilus, Christopher Ifeanyi Eke
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: God’swill Theophilus, Christopher Ifeanyi Eke
10.5120/ijais2023451951

God’swill Theophilus, Christopher Ifeanyi Eke . Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets. International Journal of Applied Information Systems. 12, 41 ( Sep 2023), 23-32. DOI=10.5120/ijais2023451951

@article{ 10.5120/ijais2023451951,
author = { God’swill Theophilus, Christopher Ifeanyi Eke },
title = { Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2023 },
volume = { 12 },
number = { 41 },
month = { Sep },
year = { 2023 },
issn = { 2249-0868 },
pages = { 23-32 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number41/machine-learning-based-e-learners-engagement-level-prediction-using-benchmark-datasets/ },
doi = { 10.5120/ijais2023451951 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-14T17:32:45.357208+05:30
%A God’swill Theophilus
%A Christopher Ifeanyi Eke
%T Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 23-32
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The wide adoption of e-learning especially during and after the pandemic has given rise to the concern of learners’ motivation and involvement. E-leaner engagement level recognition over time has become critical since there is little to no physical interaction. In this paper, a benchmark dataset was utilized in predicting learners’ engagement levels in a blended e-learning system. Information Gain feature ranker was leveraged to ascertain the significance of the features. This study performed a comparative study on some machine learning algorithms including; Decision Tree, Naïve Bayes, Random Forest, Logistics Regression, Stochastic Gradient Descent, LogitBoost, Sequential Minimal Optimization, Voted Perceptron, and AdaptiveBoost. Each model was accessed using the 10-fold cross-validation. We measure the performance of the models before and after feature selection. The predictive results show that Sequential Minimal Optimization outperformed other models by attaining an accuracy of 90% with precision, recall, and f-measure values of 0.895, 0.897, and 0.895 respectively.

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Index Terms

Computer Science
Information Sciences

Keywords

Data science applications in education E-learning Machine learning Engagement prediction Learning strategies.

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