Utilizing Machine Learning Methods for Usability Evaluation in Learning Management Systems

The concept of usability refers to a user's capability to interact with a system to fulfill goals in terms of task completion (effectiveness), time measurement (efficiency), and positive attitude (satisfaction). The strategy for usability evaluation in software systems usually involves questionnaires, user testing, and heuristics. Although these methods have been widely used due to several benefits, there are challenges related to time consumption and embedded bias. In response to these challenges, this work proposes a hybrid approach based on usability questionnaire answers and machine learning algorithms to predict usability scores. We describe three different experiments with features extracted from a Learning Management System. These features were applied in the Machine Learning algorithms Linear Regression, Decision Trees, Random Forest, and Neural Networks in three experiments. Random Forest produces the best performance of average mean square error and root mean square error among machine learning algorithms. The results are promising, though there are alternatives for improvements for better performance of the System Usability Scale and UseLearn scores prediction. This approach has potential as a reliable predictive tool for usability scores, which would help create software systems that better satisfy users' needs.