An Unobtrusive Approach to Emotion Detection in E-Learning Systems

Abstract

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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