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Purpose The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static. Design/methodology/approach The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review. Findings The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation. Originality/value The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

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.

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