Examining Post-Pandemic Higher Education Systems: Predicting Students’ Interests in Hybrid Learning Using Deep Neural Network

DOI

10.2991/978-94-6463-554-6_3How to use a DOI?

Keywords

Higher Education; Hybrid Learning; Pandemic; Deep Neural Network; Variable Importance

Abstract

The advancement of technology has had a significant impact on the learning systems. That has resulted in the rise of hybrid learning, a learning system that combines face-to-face and online elements, following the decrease of the COVID-19 pandemic. In its implementation, hybrid learning requires effective technology integration to support the teaching and learning process. Successful implementation of hybrid learning depends on infrastructure, technology, and student acceptance. Therefore, this study uses the deep neural network (DNN) method and variable importance analysis to analyze students’ interest in hybrid learning and identify significant predictors. Through the questionnaires, we evaluated a range of indicators encompassing social influence, perceived interactivity, perceived usefulness, ease of use, facility conditions, attitude, satisfaction, and user intention. The results of DNN analysis with various model variations showed that the highest accuracy achieved was 82.40%. Moreover, the results of variable importance analysis highlighted the role of satisfaction, which has a significant influence in shaping the variability of model output. This research provides insights to enhance the learning experience in industrial engineering through effective hybrid learning strategies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Alvin Muhammad ‘Ainul Yaqin
AU  - Ahmad Kamil Muqoffi
AU  - Sigit Rahmat Rizalmi
AU  - Faishal Arham Pratikno
PY  - 2024
DA  - 2024/11/29
TI  - Examining Post-Pandemic Higher Education Systems: Predicting Students’ Interests in Hybrid Learning Using Deep Neural Network
BT  - Proceedings of the 4th International Conference on Education and Technology (ICETECH 2023)
PB  - Atlantis Press
SP  - 23
EP  - 34
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-554-6_3
DO  - 10.2991/978-94-6463-554-6_3
ID  - Yaqin2024
ER  -