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An Empirical Comparison of Students’ Performance in Online vs Offline Platforms Using Ensemble Learning Models

Published: 11 August 2022 Publication History

Abstract

Online learning is a paradigm shift from traditional offline education; recently there has been a remarkable surge in e-learning platforms due to Covid 19 outbreaks. There is a significant difference in students’ performance on both platforms. The primary focus of this study is to investigate how the students perform in both learning methods. Moreover, five ensemble-learning approaches are compared to predict student performance in online and offline education platforms. Ensemble learning is a prominent machine learning meta-approach that integrates predictions from several models to improve prediction. Students' performance data for both offline and online platforms were extracted from a private university's student database. Five ensemble-learning methods were applied to both datasets for predictive analysis. According to the findings of this study, students do better on online platforms than in traditional education systems. Furthermore, XGBoost, Gradient Boost, and Stacking KNN fared better for online data, whereas stacking neural networks and stacking random forest performed better for offline data. The findings of this study will assist educational instructors to concentrate more on students' performance based on their particular learning system.

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  1. An Empirical Comparison of Students’ Performance in Online vs Offline Platforms Using Ensemble Learning Models

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    ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
    March 2022
    543 pages
    ISBN:9781450397346
    DOI:10.1145/3542954
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 11 August 2022

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