Automatic Detection of Learner’s Learning Style

Learning style is crucial in assisting students in better understanding things learnt and in helping them retain them for extended periods of time. Utilising surveys, learning styles in traditional and online environments are identified. To automatically determine a learner’s preferred learning style without upsetting them, it is becoming more popular. Machine learning is a branch of artificial intelligence that allows systems to learn and develop without being explicitly programmed. A learning style is the way that makes one learns best. It is based on the tastes and traits of the individual. Because different students learn in different ways, individual learning styles are vital to consider in effective teaching. Learning style is important in assisting students in remembering topics for extended periods of time and improving their knowledge of concepts. The way that kids learn is crucial in helping them retain information for longer periods of time and advance their conceptual understanding. Surveys are used to determine learning preferences both offline and online. The goal is to recognise and apply characteristics that will automatically and unobtrusively determine a learner’s preferred learning style. Using Multi-nominal Naive Bayes the data acquired from students in various classes is used to train a model, which may then be used to predict a student’s learning style based on the suggested traits. The information gathered from learners is used to trained model, and it could be used for appropriately predict the learner’s learning style based on the recommended qualities. The accuracy in each dimension for the different machine learning algorithms was calculated using the data obtained for the VARK model (Visual, Aural, Read/Write, and Kinesthetic).