Learning style detection in E-learning systems using machine learning techniques.

• Learning style helps to personalize learning experiences. • Attributes identified to deduce the learning style of learners. • Machine learning models trained and validated for accurate results. • Interesting patterns involving learning styles observed and discussed. Learning style plays a vital role in helping students retain learned concepts for a longer time and also improves the understanding of the concepts. Learning styles in offline and online scenarios are recognized using questionnaires. The recent trend is to identify and use attributes to detect the learning style of the learner automatically without disturbing the learner. The paper is an extension of the authors' earlier work with some changes to the methodology. In this paper, the authors have identified new attributes and scaled-down the attributes identified earlier, which would help identify the learner's learning style. The authors implemented classification algorithms and compared the accuracy of the different algorithms on the dataset. Various interesting patterns are observed in learner's behaviour while learning different types of concepts in different situations. [ABSTRACT FROM AUTHOR]