Supporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques

Abstract

Learning experiences based on serious games are employed in multiple contexts. Players carry out multiple interactions during the gameplay to solve the different challenges faced. Those interactions can be registered in logs as large data sets providing the assessment process with objective information about the skills employed. Most assessment methods in learning experiences based on serious games rely on manual approaches, which do not scalewell when the amount of data increases. We propose an automated method to analyse students’ interactions and assess their skills in learning experiences based on serious games. The method takes into account not only the final model obtained by the student, but also the process followed to obtain it, extracted from game logs. The assessment method groups students according to their in-game errors and ingame outcomes. Then, the models for the most and the least successful students are discovered using process mining techniques. Similarities in their behaviour are analysed through conformance checking techniques to compare all the students with the most successful ones. Finally, the similarities found are quantified to build a classification of the students’ assessments. We have employed this method with Computer Science students playing a serious game to solve design problems in a course on databases. The findings show that process mining techniques can palliate the limitations of skill assessment methods in game-based learning experiences.