Carla Denise Castanho

Cybersickness (CS) is one of the main challenges for the adoption of Virtual Reality (VR), manifesting through symptoms such as nausea, dizziness, and eye strain, particularly in Head-Mounted Display (HMD) devices. Although subjective measures, such as questionnaires, are widely used to assess CS, they do not allow for real-time user feedback. This study investigates the role of biosignals in identifying the causes and indicators of CS in VR games, employing Symbolic Machine Learning (SML) to classify the most relevant factors. Our approach combines Electrocardiogram (ECG), Electrodermal Activity (EDA), and Accelerometer (ACC) data with game metrics and user profile attributes. Data were collected from two VR games: a car game and a flying game. Decision Trees and Random Forests were used to build interpretable models, and the results showed that integrating biosignals and game data significantly improves CS prediction, with Random Forest achieving an AUC of 0.95. The findings highlight that exposure time, motion intensity, and electrodermal activity are among the key predictors of CS, reinforcing the importance of physiological monitoring in VR research. Furthermore, the study demonstrates the potential of SML in creating explainable models, contributing to more effective strategies for mitigating CS.

O objetivo da pesquisa é investigar o processo de concepção de jogos enquanto ferramenta para situar práticas de auxílio ao aprendizado de Português como Língua Estrangeira (PLE). A ação parte de uma demanda do Ministério de Relações Exteriores do Brasil (Itamaraty) e parceria da Universidade de Brasília com o Centro Cultural Brasil-Finlândia. A equipe foi norteada pela abordagem do Diamante Duplo, em uma resolução de problemas contemplando análises e sínteses. Os princípios de design dos jogos seguiram a tétrade elementar de Schell, com a inclusão do Conteúdo de Aprendizagem para garantir relevância pedagógica. Dez jogos foram desenvolvidos para atender momentos específicos da trajetória do aluno no curso.

Cybersickness (CS) is one of the main obstacles to the use of Virtual Reality (VR), often caused by Head-mounted Displays (HMDs). Its symptoms, which can last from minutes to hours after exposure, include nausea, vertigo, eye strain, and headache. Researchers generally use subjective measures, such as the Virtual Reality Sickness Questionnaire, to assess CS. Studies indicate that CS significantly impacts physiological signals, but there is little research on the application of Symbolic Machine Learning to understand the causes of CS in VR games. This study investigates the use of biosignals to identify the causes of CS in VR. Our main hypothesis is that the combination of quantitative and subjective assessments, along with Symbolic Machine Learning techniques, allows for the creation of a ranking of the main causative or indicative factors of CS. To validate this hypothesis, software was developed to record the biosignals and self-reported symptoms of participants during experiments with two VR games. Physiological signals (ECG, EDA, and body movements extracted from an Accelerometer - ACC) and game data were collected. The results show a strong relationship between physiological changes and CS symptoms, with a model that includes biosignals achieving an AUC of 0.95. The rankings of the main factors, both for the model without and with the inclusion of biosignals, confirmed previous research described in the literature. As far as we know, our work is the first to use Symbolic Machine Learning models to detect the causes of CS.

Cybersickness represents one of the main obstacles to the use of Virtual Reality, often triggered by the use of Head-mounted Display devices. The symptoms associated with cybersickness can vary among individuals and include nausea, dizziness, eye strain, and headache, which may persist for minutes or even hours after exposure to Virtual Reality. According to the literature, cybersickness has a considerable impact on physiological signals such as delta waves in the Electroencephalogram; Heart Rate and Heart Rate Variability, derived from the Electrocardiogram; Electrodermal Activity; and Electrogastrography, all of which show a significant correlation with this condition. In this study, we investigated the use of biosignals to identify the possible causes associated with cybersickness in Virtual Reality. Our main hypothesis is that the combination of quantitative and subjective assessments, combined with Symbolic Machine Learning techniques, is effective in creating a ranking of the main causative/indicative factors of this condition. The results of this study highlight significant contributions to the understanding of factors influencing cybersickness symptoms. Statistical analyses confirmed the relationship between physiological changes and cybersickness symptoms. By including biosignals in our model, we achieved a significant gain, with an AUC of 0.95. The rankings of the main factors, both for the model without and with the inclusion of biosignals, confirmed previous research described in the literature. To the best of our knowledge, this is the first work to employ Symbolic Machine Learning models combining data from user profiles, game, and biosignals to detect the causes of cybersickness and generate a ranking of the most relevant factors.

De acordo com o relatório da ACM intitulado Retention in computer science undergraduate programs in the US: Data challenges and promising interventions, sobre retenção em cursos de Ciência da Computação, a primeira disciplina de programação, chamada de CS1 (Computer Science 1) no relatório, pode influenciar a permanência do aluno em um curso de computação. Na Universidade de Brasília (UnB), a primeira disciplina de programação tem um alto índice de reprovação. Neste contexto, foi criado um novo Programa de Monitoria para Algoritmos e Programação de Computadores (APC), a primeira disciplina de programação dos cursos de computação na UnB. Este novo programa é composto por atendimentos aos sábados, atendimento individual via agendamento, busca ativa pelos alunos com baixo rendimento, aulões de revisão aos sábados, e acompanhamento em aulas práticas. Neste artigo é apresentado o relato de experiência de quatro semestres do programa, sendo a sua primeira edição no segundo semestre de 2020, durante a pandemia, com ensino remoto, até o primeiro semestre de 2022.1, o retorno ao ensino presencial. Neste artigo, é descrita a metodologia do programa, análise dos resultados desses dois anos de aplicação do programa, e as lições aprendidas.

Los trabajos presentados en el congreso «INCLUYE», organizado por el Grupo de Investigación Reconocido (GIR) en Multiculturalidad, Innovación y Tecnologías Aplicadas (MITA) de la Universidad de Salamanca, reúne a la comunidad investigadores para discutir y reflexionar sobre tres ejes temáticos relevantes y urgentes para nuestra educación actual, en las diversas latitudes del mundo: equidad e igualdad en educación escolar y superior; educación intercultural; métodos, metodologías y evaluación inclusivas. En el primero se presentan reflexiones y praxis educativas, que intentan alcanzar la equidad e igualdad, dejando atrás las etiquetas que lejos de unir, marcan las diferencias en el alumnado con diversidad de género y necesidades especiales. En el segundo se aborda la educación intercultural y multicultural en diversos contextos educativos en el contexto de los fenómenos migratorios que nos encontramos viviendo a nivel mundial. Tiene especial énfasis las competencias y principios pedagógicos para la acción educativa intercultural, como en las intervenciones educativas para prevenir situaciones de conflicto. En el tercer eje se exponen trabajos que abordan la innovación educativa para la inclusión a través de metodologías activas y emergentes para que el alumnado logre el máximo nivel de desarrollo personal y la adquisición del mayor grado de conocimientos y destrezas de acuerdo con sus propias posibilidades. Este tema, está dirigido especialmente a aquellas propuestas que recojan estrategias pedagógicas que tengan como finalidad la inclusión de todo el alumnado en la comunidad educativa, especialmente de los grupos con más riesgo de exclusión.