Analysis of Student Performance Differences in Computer Network Courses with Learning Modes in Multimedia Nusantara University

Global academic environments have been significantly impacted by the change in educational delivery techniques brought about by the COVID-19 pandemic. This study examines the variations in student performance in Multimedia Nusantara University's Computer Network course across on-site, hybrid, and online learning modes. 526 students data (2019–2022) were evaluated using a variety of statistical techniques, such as the Kruskal-Wallis test, tests for homoscedasticity and normality, pairwise comparison, and post-hoc Dunn's analysis. By taking into consideration the various circumstances and difficulties presented by each learning mode, these techniques guaranteed a thorough assessment of performance variances.

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Publication Details

Journal
IJNMT (International Journal of New Media Technology)
Published
2025-06-30
DOI
https://doi.org/10.31937/ijnmt.v12i1.3947
Primary Topic
Experimental Learning in Engineering
Type
article
Field-Weighted Citation Impact
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article

Analysis of Student Performance Differences in Computer Network Courses with Learning Modes in Multimedia Nusantara University

Fatimah Wati Halim, Livia Junike, Karen Yapranata
IJNMT (International Journal of New Media Technology)
Experimental Learning in Engineering
article

Analysis of Student Performance Differences in Computer Network Courses with Learning Modes in Multimedia Nusantara University

Fatimah Wati Halim, Livia Junike, Karen Yapranata
article en

Abstract

Global academic environments have been significantly impacted by the change in educational delivery techniques brought about by the COVID-19 pandemic. This study examines the variations in student performance in Multimedia Nusantara University's Computer Network course across on-site, hybrid, and online learning modes. 526 students data (2019–2022) were evaluated using a variety of statistical techniques, such as the Kruskal-Wallis test, tests for homoscedasticity and normality, pairwise comparison, and post-hoc Dunn's analysis. By taking into consideration the various circumstances and difficulties presented by each learning mode, these techniques guaranteed a thorough assessment of performance variances.

IJNMT (International Journal of New Media Technology)Vol. 12(1)
Multimedia Nusantara University (ID)
Openalex Percentile: Top 70%
Experimental Learning in Engineering
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