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Researchers have proved that emotions play vital role in a human's life. They affect our way of living, making decisions and also our way of learning. There are many methods for emotion detection in e-learning. However, each of them comes with its own set of disadvantages discussed in the literature review. In this paper, the attributes that have been identified are purely unobtrusive in nature; attributes that do not interfere with the learner's activity and less is known to them that their emotions are being monitored. A methodology is presented to detect the emotions of the learner using keystrokes, mouse clicks, forum discussions and the results of assessments. Machine learning models have been trained and tested to predict the learner's emotions. The logistic regression performed fairly well in comparison to the other algorithms with an accuracy of about 85% and cross-validation score of 86%. During this study, interesting patterns are observed in learner's emotions that are discussed. Future directions include collecting diverse data to understand emotions of learners from various age groups and observing patterns in their emotional changes. [ABSTRACT FROM AUTHOR]

• 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]