02236nas a2200241   4500000000100000000000100001008004100002260001200043653002800055653002300083653002400106653001800130100003800148700002500186700002300211700002000234245011300254856008100367300000900448490001300457520151000470022001401980       9998                            d  c05/202310aEducational Data Mining10aLearning Analytics10aGame-Based Learning10aSerious Games1 aJuan Antonio Caballero-Hernández1 aManuel Palomo-Duarte1 aJuan Manuel Dodero1 aDragan Gaševic00aSupporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ip2023_05_002.pdf  a1-140 vIn Press3 aLearning 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.  a1989-1660