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- Computational Intelligence in Serious Games: a case study to identify patterns in a game for children with learning disabilitiesPublication . DOMINGUES, A. M.This work explores the application of computational intelligence techniques in a serious game (SG) for children with learning disabilities. Specifically, we apply Data Mining (DM) techniques such as Decision Tree and Apriori algorithms aiming to identify the existence of patterns that would allow a better understanding on the profiles of children involved in the game. The data analyzed are related to the interaction of twenty children with the considered SG, which consists of a three dimensional virtual zoo, developed with features that appeal to the preferences of children about nine years old in order to assist and motivate their learning. The results obtained in the conducted experiments revealed patterns in the profiles of the game's players under analysis, allowing to identify some characteristics that can help the psychopedagogical team. These findings can also enable the improvement of the game making it adaptable to different player profiles.
- Artificial Intelligence identifying patterns in the social interaction of senior individuals through online activities at Virtual Senior University in PandemicPublication . DOMINGUES, A. M.Objectives: Data Mining techniques applied to observe patterns in the social interaction of senior individuals during their participation in the online activities that were proposed by the Virtual Senior University during the pandemic. Methodology: Zoom and Facebook platforms were used to streamline conversations, videos with student performances and homework during the COVID-19 pandemic. The experiments conducted enabled the application of the Artificial Intelligence technique called Data Mining to perform statistical analyses and identify profiles based on the social interaction of the individuals who participated in the online activities. Participants answered about Gender, Age (years), Marital Status, Time attending Senior University and Participation in online activities. They also answered about their participation in these activities, informing if it increased, decreased, remained unchanged or if they did not participate. To carry out the analyses of the collected data, age groups were organized, being 50 to 64 years, 65 to 74 years and 75 or more years. Results and Discussion: It was observed that the majority of seniors who had been attending the Virtual Senior University since before 2020, aged between 50 and 64 years, reported that their participation in online activities had increased. According to the analysis that was carried out, it was possible to correctly classify 76.087% of the data. The accuracy described through the Kappa statistic coefficient showed reliability in the data showing a score of 0.6383 and corresponding to a substantial agreement (range between 0.61 and 0.80). Conclusions: The Data Mining technique applied in this study suggests that there was statistical significance for the analyses carried out based on the social interaction of senior individuals, in the age group between 50 and 64 years old, who participated in the online activities proposed by the Virtual Senior University during the pandemic. Different activities are suggested for the other age groups.