Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN
The COVID-19 pandemic significantly impacted higher education systems, leading many institutions to adopt hybrid learning models. This research investigates the relevance of hybrid learning methods in the post-pandemic education context using structural equation modeling (SEM) and deep neural networ...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Education |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/2331186X.2025.2458930 |
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| author | Alvin Muhammad ‘Ainul Yaqin Ahmad Kamil Muqoffi Sigit Rahmat Rizalmi Faishal Arham Pratikno Remba Yanuar Efranto |
| author_facet | Alvin Muhammad ‘Ainul Yaqin Ahmad Kamil Muqoffi Sigit Rahmat Rizalmi Faishal Arham Pratikno Remba Yanuar Efranto |
| author_sort | Alvin Muhammad ‘Ainul Yaqin |
| collection | DOAJ |
| description | The COVID-19 pandemic significantly impacted higher education systems, leading many institutions to adopt hybrid learning models. This research investigates the relevance of hybrid learning methods in the post-pandemic education context using structural equation modeling (SEM) and deep neural network (DNN) approaches. We tested the model at a public university in Indonesia that had implemented a hybrid learning system post-pandemic but reverted to a full offline system due to doubts about the benefits and student interest. Data were collected through questionnaires evaluating key factors, including social influence, perceived interactivity, perceived usefulness, ease of use, facility conditions, attitude, satisfaction, and user intention. SEM validated the conceptual model, confirming all eight hypotheses as statistically significant (p-value of ≤ 0.05) and achieving good model fit with a comparative fit index (CFI) of 0.901 and a root mean square error of approximation (RMSEA) of 0.069. Meanwhile, DNN achieved a high prediction accuracy of 82.40%, significantly outperforming logistic regression (baseline) models. The DNN demonstrated its ability to capture complex, nonlinear relationships and provide actionable insights into factors driving student interests. This research provides valuable empirical evidence to inform education policymakers, institutions, and stakeholders navigating the evolving landscape of post-pandemic learning environments. |
| format | Article |
| id | doaj-art-b716a9e581c141aab0c1d246e7844ae6 |
| institution | Kabale University |
| issn | 2331-186X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Education |
| spelling | doaj-art-b716a9e581c141aab0c1d246e7844ae62025-02-05T15:20:04ZengTaylor & Francis GroupCogent Education2331-186X2025-12-0112110.1080/2331186X.2025.2458930Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNNAlvin Muhammad ‘Ainul Yaqin0Ahmad Kamil Muqoffi1Sigit Rahmat Rizalmi2Faishal Arham Pratikno3Remba Yanuar Efranto4Systems Modeling and Optimization Research Group, Department of Industrial Engineering, Institut Teknologi Kalimantan, Balikpapan, IndonesiaSystems Modeling and Optimization Research Group, Department of Industrial Engineering, Institut Teknologi Kalimantan, Balikpapan, IndonesiaDepartment of Industrial Engineering, Institut Teknologi Kalimantan, Balikpapan, IndonesiaDepartment of Logistics Engineering, Institut Teknologi Kalimantan, Balikpapan, IndonesiaDepartment of Industrial Engineering, Universitas Brawijaya, Malang, IndonesiaThe COVID-19 pandemic significantly impacted higher education systems, leading many institutions to adopt hybrid learning models. This research investigates the relevance of hybrid learning methods in the post-pandemic education context using structural equation modeling (SEM) and deep neural network (DNN) approaches. We tested the model at a public university in Indonesia that had implemented a hybrid learning system post-pandemic but reverted to a full offline system due to doubts about the benefits and student interest. Data were collected through questionnaires evaluating key factors, including social influence, perceived interactivity, perceived usefulness, ease of use, facility conditions, attitude, satisfaction, and user intention. SEM validated the conceptual model, confirming all eight hypotheses as statistically significant (p-value of ≤ 0.05) and achieving good model fit with a comparative fit index (CFI) of 0.901 and a root mean square error of approximation (RMSEA) of 0.069. Meanwhile, DNN achieved a high prediction accuracy of 82.40%, significantly outperforming logistic regression (baseline) models. The DNN demonstrated its ability to capture complex, nonlinear relationships and provide actionable insights into factors driving student interests. This research provides valuable empirical evidence to inform education policymakers, institutions, and stakeholders navigating the evolving landscape of post-pandemic learning environments.https://www.tandfonline.com/doi/10.1080/2331186X.2025.2458930Higher education systemshybrid learningpost-pandemicstructural equation modelingdeep neural networkHigher Education |
| spellingShingle | Alvin Muhammad ‘Ainul Yaqin Ahmad Kamil Muqoffi Sigit Rahmat Rizalmi Faishal Arham Pratikno Remba Yanuar Efranto Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN Cogent Education Higher education systems hybrid learning post-pandemic structural equation modeling deep neural network Higher Education |
| title | Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN |
| title_full | Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN |
| title_fullStr | Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN |
| title_full_unstemmed | Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN |
| title_short | Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN |
| title_sort | hybrid learning in post pandemic higher education systems an analysis using sem and dnn |
| topic | Higher education systems hybrid learning post-pandemic structural equation modeling deep neural network Higher Education |
| url | https://www.tandfonline.com/doi/10.1080/2331186X.2025.2458930 |
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