The prediction of student’s learning style using neural network

Personalized and adaptive learning are pivotal in enhancing student engagement and academic achievement, as they facilitate a customized educational experience tailored to each learner's distinct abilities and performance levels. By leveraging the VARK model—Visual, Aural, Read/Write, and Kinesthetic—educators can develop targeted strategies that resonate deeply with students’ individual learning preferences. This study advances the field by employing sophisticated multilayer neural networks trained on the extensive Mendeley open-source dataset to accurately predict students' learning styles based on critical parameters such as reading proficiency, writing skills, and listening capabilities. The evaluation of this model reveals an impressive accuracy rate of 98.4%, underscoring the transformative potential of data-driven methodologies in refining educational practices and significantly improving student outcomes. This approach not only personalizes the learning journey but also empowers educators to effectively address the diverse needs of their students.