User Experience, Student Behaviour, and Learning Effectiveness: A Process Mining Approach in Microlearning

Microlearning applications, such as online short tutorials, are increasingly popular for their flexibility and accessibility. Given the brevity of microlearning experiences, it becomes all the more crucial that students focus on learning the content rather than how to navigate the system. However, even if the role of usability as a prerequisite for effective learning is widely recognized, it is still not clear whether user perceptions about their overall experience with a system (which includes user experience in the strict sense, perceived learning and usability) are related to different behavioral patterns and learning outcomes. To answer these questions, this study introduces a structured approach that integrates process mining techniques with user experience evaluation to analyze learning processes based on digital traces. The application of process mining allows for a detailed characterization of student behavior, enabling a deeper understanding of the interplay between students’ perceptions of their experience, actions and learning outcomes. We conduct a practical case study on an online tutorial about Python programming, employing event log analysis and predictive process monitoring to explore behavioral patterns and their correlation with user perceptions and learning performance. Results highlight that user perceptions about their experience with the system are related to markedly different ways of approaching the tutorial, for example in terms of duration and complexity of the interactions; on the other hand, early behavioral data allow to predict user perceptions with good accuracy. Additionally, we found that students’ assessments of usability and user experience are positively related to their learning outcomes. These insights may help promote the optimization of microlearning environments through data-driven methodologies.