dc.creatorRubio-Manzano, Clemente
dc.creatorLermanda-Senoceaín, Tomás
dc.creatorMartínez Araneda, Claudia
dc.creatorVidal-Castro, Christian
dc.creatorSegura-Navarrete, Alejandra
dc.date2020-10-06T19:19:00Z
dc.date2020-10-06T19:19:00Z
dc.date2019-12
dc.date.accessioned2022-10-18T12:07:44Z
dc.date.available2022-10-18T12:07:44Z
dc.identifierJournal of Intelligent & Fuzzy Systems vol. 37, No 6 (2019) 8397–8415
dc.identifier1064-1246
dc.identifierhttp://repositoriodigital.ucsc.cl/handle/25022009/2087
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4441723
dc.descriptionArtículo de publicación ISI
dc.descriptionExecution traces comprehension is an important topic in computer science since it allows software engineers to get a better understanding of the system behavior. However, traces are usually very large and hence they are difficult to interpret. Parallel, execution traces comprehension is a very important topic into the algorithms learning courses since it allows students to get a better understanding of the algorithm behavior. Therefore, there is a need to investigate ways to help students (and teachers) find and understand important information conveyed in a trace despite the trace being massive. In this paper, we propose a new approximation for execution traces comprehension based on fuzzy linguistic descriptions. A new methodology and a data-driven architecture based on linguistic modelling of complex phenomenon are presented and explained. In particular, they are applied to automatically generate linguistic reports from execution traces generated during the execution of algorithm implemented by the students of an introductory course of artificial intelligence. To the best of our knowledge, it is the first time that linguistic modelling of complex phenomenon is applied to execution traces comprehension. Throughout the article, it is shown how this kind of technology can be employed as a useful computer-assisted assessment tool that provides students and teachers with technical, immediate and personalised feedback about the algorithms that are being studied and implemented. At the same time, they provide us with two useful applications: they are an indispensable pedagogical resource for improving comprehension of execution traces, and they play an important role in the process of measuring and evaluating the “believability” of the agents implemented. To show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of the authors, and it has been incorporated into the process of assessment of an introductory artificial intelligence course. Finally, an empirical evaluation to confirm our hypothesis was performed and a survey directed to the students was carried out to measure the quality of the learning-teaching process by using this methodology enriched with fuzzy linguistic descriptions.
dc.languageen
dc.publisherIOS Press
dc.sourcehttps://doi.org/10.3233/JIFS-190935
dc.subjectComputational intelligence
dc.subjectLinguistic descriptions of data
dc.subjectLinguistic modelling of complex phenomena
dc.subjectComputer game bots
dc.subjectTuring test
dc.subjectComputer-assisted assessment
dc.titleFuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence
dc.typeArtículos de revistas


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