A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance
As educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner’s learning style have also been created. One of the core elements of such systems is the ability to recommend the...
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2025-01-01
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| author | Anju Kalwar Deepika Shekhawat Sandeep Joshi Amit Kumar Bairwa |
| author_facet | Anju Kalwar Deepika Shekhawat Sandeep Joshi Amit Kumar Bairwa |
| author_sort | Anju Kalwar |
| collection | DOAJ |
| description | As educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner’s learning style have also been created. One of the core elements of such systems is the ability to recommend the right content that improves the learner’s experience. The adaptative learning environment and content recommendation with the help of genetic algorithms are investigated in this paper. GA, based on the principles of Natural Selection, provides stability-based search and solving capacity needed to tackle the inherent messiness and diversity of PBL. In our approach, each learner’s context information, as well as performance metrics, preferences and learning goals, are dynamic and fed into the GA. The algorithm genetically generates a random vote of potential content sequences as the initial population that improves through creation by selection, crossover, or mutation. The fitness function assesses an individual sequence in terms of its conformity to the learner’s profile and, thus, helps to increase an LMS usage rate, select appropriate content, and achieve targeted educational objectives. The proposed system is an application of GA that constantly updates feedback from the learner’s generated performance data and adjusts the recommended content in real time. This dynamic creates a positive learning environment capable of enhancing learner interaction and improving learning achievements. When the proposed GA-based solution is applied in a specific case with an instructional design within an online learning context, enhancement in learner satisfaction and performance are substantial, sufficiently proving the approach’s applicability. Thus, this study expands the existing literature on adaptive learning technologies by including the usage of GA in education. Future work will extend this by investigating how GAs can be mixed with other AI approaches, GA applicability to larger datasets, and real-time adaptation improvements. |
| format | Article |
| id | doaj-art-51f92848596940f99e4888523e36d66a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-51f92848596940f99e4888523e36d66a2025-08-20T03:21:34ZengIEEEIEEE Access2169-35362025-01-011310281510282910.1109/ACCESS.2025.357886711030457A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and PerformanceAnju Kalwar0Deepika Shekhawat1Sandeep Joshi2Amit Kumar Bairwa3https://orcid.org/0000-0003-1830-0661School of Computer Science and Application, JECRC University, Jaipur, IndiaDepartment of Computer Science and Engineering, Amity University Mumbai, Mumbai, Maharashtra, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, IndiaDepartment of Artificial Intelligence and Machine Learning, Manipal University Jaipur, Jaipur, IndiaAs educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner’s learning style have also been created. One of the core elements of such systems is the ability to recommend the right content that improves the learner’s experience. The adaptative learning environment and content recommendation with the help of genetic algorithms are investigated in this paper. GA, based on the principles of Natural Selection, provides stability-based search and solving capacity needed to tackle the inherent messiness and diversity of PBL. In our approach, each learner’s context information, as well as performance metrics, preferences and learning goals, are dynamic and fed into the GA. The algorithm genetically generates a random vote of potential content sequences as the initial population that improves through creation by selection, crossover, or mutation. The fitness function assesses an individual sequence in terms of its conformity to the learner’s profile and, thus, helps to increase an LMS usage rate, select appropriate content, and achieve targeted educational objectives. The proposed system is an application of GA that constantly updates feedback from the learner’s generated performance data and adjusts the recommended content in real time. This dynamic creates a positive learning environment capable of enhancing learner interaction and improving learning achievements. When the proposed GA-based solution is applied in a specific case with an instructional design within an online learning context, enhancement in learner satisfaction and performance are substantial, sufficiently proving the approach’s applicability. Thus, this study expands the existing literature on adaptive learning technologies by including the usage of GA in education. Future work will extend this by investigating how GAs can be mixed with other AI approaches, GA applicability to larger datasets, and real-time adaptation improvements.https://ieeexplore.ieee.org/document/11030457/Adaptive learningcontent recommendationgenetic algorithmpersonalized educationeducational technologyoptimization |
| spellingShingle | Anju Kalwar Deepika Shekhawat Sandeep Joshi Amit Kumar Bairwa A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance IEEE Access Adaptive learning content recommendation genetic algorithm personalized education educational technology optimization |
| title | A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance |
| title_full | A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance |
| title_fullStr | A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance |
| title_full_unstemmed | A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance |
| title_short | A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance |
| title_sort | genetic algorithm approach to adaptive learning enhancing learner satisfaction and performance |
| topic | Adaptive learning content recommendation genetic algorithm personalized education educational technology optimization |
| url | https://ieeexplore.ieee.org/document/11030457/ |
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