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dc.contributor.authorCaballero-Hernández, Juan Antonio
dc.contributor.authorPalomo-Duarte, Manuel
dc.contributor.authorDodero, Juan Manuel
dc.contributor.authorGaševic, Dragan
dc.date2024-06
dc.date.accessioned2023-06-01T09:53:03Z
dc.date.available2023-06-01T09:53:03Z
dc.identifier.citationJuan Antonio Caballero-Hernández, Manuel Palomo-Duarte, Juan Manuel Dodero, Dragan Gaševic (2024). "Supporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, issue Regular Issue, no. 6, pp. 146-159. https://doi.org/10.9781/ijimai.2023.05.002es_ES
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14810
dc.description.abstractLearning 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.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 8, nº 6
dc.relation.uri
dc.rightsopenAccesses_ES
dc.subjecteducational data mininges_ES
dc.subjectlearning analyticses_ES
dc.subjectgame-based learninges_ES
dc.subjectserious gameses_ES
dc.subjectIJIMAIes_ES
dc.titleSupporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniqueses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2023.05.002


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