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Enhancing Personalized Education through an Adaptive Framework: Assessing the Impact of an Optimized Planning Generator.

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Τίτλος: Enhancing Personalized Education through an Adaptive Framework: Assessing the Impact of an Optimized Planning Generator.
Συγγραφείς: Elfirdoussi, Selwa, Kabaili, Hind, Alaoui, Najoua, Firdoussi, Larbi El
Πηγή: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p25263-25269, 7p
Θεματικοί όροι: Blended learning, Student engagement, Evaluation methodology, Individualized instruction, Program generators (Computer programs), Digital learning, COVID-19, Dynamical systems
Περίληψη: The COVID-19 crisis has exposed the inefficiencies of many e-learning platforms, highlighting the importance of face-to-face interactions between students and professors. To address these challenges, this study proposes an adaptive blended learning framework that considers the unique profiles of students, professors, and university policies. The proposed framework consists of three main components: (i) a program generator that generates a list of sessions with an optimal blend of face-to-face and e-learning modes, (ii) an evaluation model that proposes scheduling planning that is optimal for both students and professors, and (iii) an AI model that predicts student engagement in courses using the output of the evaluation model. [ABSTRACT FROM AUTHOR]
Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Enhancing Personalized Education through an Adaptive Framework: Assessing the Impact of an Optimized Planning Generator.
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  Data: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p25263-25269, 7p
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  Data: The COVID-19 crisis has exposed the inefficiencies of many e-learning platforms, highlighting the importance of face-to-face interactions between students and professors. To address these challenges, this study proposes an adaptive blended learning framework that considers the unique profiles of students, professors, and university policies. The proposed framework consists of three main components: (i) a program generator that generates a list of sessions with an optimal blend of face-to-face and e-learning modes, (ii) an evaluation model that proposes scheduling planning that is optimal for both students and professors, and (iii) an AI model that predicts student engagement in courses using the output of the evaluation model. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.48084/etasr.9438
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        Text: English
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      – SubjectFull: Student engagement
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      – SubjectFull: Evaluation methodology
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      – SubjectFull: Individualized instruction
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      – SubjectFull: Program generators (Computer programs)
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              M: 08
              Text: Aug2025
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              Y: 2025
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