Personalized learning in hybrid education
Abstract The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it rema...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03361-5 |
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| author | Alaa O. Khadidos Hariprasath Manoharan Adil O. Khadidos Mohammad N. Alanazi Fuhid Alanazi Shitharth Selvarajan |
| author_facet | Alaa O. Khadidos Hariprasath Manoharan Adil O. Khadidos Mohammad N. Alanazi Fuhid Alanazi Shitharth Selvarajan |
| author_sort | Alaa O. Khadidos |
| collection | DOAJ |
| description | Abstract The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students. However, many existing methods suggest different techniques for evaluating learners in a hybrid mode, where obtaining clear data sets can be difficult. In the teaching and learning approach, if the defined data set from experts is clear, decisions regarding the learning characteristics of students can be made in a shorter period. In the proposed method the PLA framework categorizes learners into four engagement-based clusters using a three-dimensional sensor model and machine learning classifiers. A dual-controller mechanism (master-slave) dynamically adjusts communication intervals and optimizes video transmission, reducing latency and packet loss. The methodology is validated using MATLAB-based simulations with a dataset of 1,700–5,000 learners, analyzing throughput, delay, packet loss, and cost efficiency. The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers. |
| format | Article |
| id | doaj-art-a0d251bde05e4528bd73f08485e62d91 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a0d251bde05e4528bd73f08485e62d912025-08-20T03:48:19ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-03361-5Personalized learning in hybrid educationAlaa O. Khadidos0Hariprasath Manoharan1Adil O. Khadidos2Mohammad N. Alanazi3Fuhid Alanazi4Shitharth Selvarajan5Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Electronics and Communication Engineering, Panimalar Engineering CollegeDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)Faculty of Computer and Information Systems, Islamic University of MadinahDepartment of Computer Science, Kebri Dehar UniversityAbstract The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students. However, many existing methods suggest different techniques for evaluating learners in a hybrid mode, where obtaining clear data sets can be difficult. In the teaching and learning approach, if the defined data set from experts is clear, decisions regarding the learning characteristics of students can be made in a shorter period. In the proposed method the PLA framework categorizes learners into four engagement-based clusters using a three-dimensional sensor model and machine learning classifiers. A dual-controller mechanism (master-slave) dynamically adjusts communication intervals and optimizes video transmission, reducing latency and packet loss. The methodology is validated using MATLAB-based simulations with a dataset of 1,700–5,000 learners, analyzing throughput, delay, packet loss, and cost efficiency. The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers.https://doi.org/10.1038/s41598-025-03361-5Teaching learningMachine learning (ML)Personalized learning approach (PLA)Video classes |
| spellingShingle | Alaa O. Khadidos Hariprasath Manoharan Adil O. Khadidos Mohammad N. Alanazi Fuhid Alanazi Shitharth Selvarajan Personalized learning in hybrid education Scientific Reports Teaching learning Machine learning (ML) Personalized learning approach (PLA) Video classes |
| title | Personalized learning in hybrid education |
| title_full | Personalized learning in hybrid education |
| title_fullStr | Personalized learning in hybrid education |
| title_full_unstemmed | Personalized learning in hybrid education |
| title_short | Personalized learning in hybrid education |
| title_sort | personalized learning in hybrid education |
| topic | Teaching learning Machine learning (ML) Personalized learning approach (PLA) Video classes |
| url | https://doi.org/10.1038/s41598-025-03361-5 |
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