Robotic - Cognitive Adaptive System for Teaching and Learning (R-CASTLE)
Resumo
One of the biggest current challenges in education is to positively impact the teaching-learning process with technology. Two common reasons are the teachers’ lack of preparation and the students’ attention span. Several Human-Robot Interaction (HRI) studies are approaching these issues. However, very few of them are considering both teachers and students in a one and only application. Thus, the presented thesis had two objectives: to provide a unique and intuitive HRI tool for education and to evaluate its impact on the users. The resulting architecture was a Robotic - Cognitive Adaptive System for Teaching and Learning (R-CASTLE). R-CASTLE aims to provide customized interactions and personalized learning to the students through machine learning for autonomous vision and dialogue interactions. The methods are configured by the teachers in the windows of the system’s graphical interface. Teachers can also have access to the system’s evaluations in chart mode of the students’ collective and individual performances. In end-to-end experiments, teachers and students claimed to experienced a sensitive potential of the system to support them. R-CASTLE was tested with other interactive devices in different applications and the results showed high performances in their activities design optimization. From the best of our knowledge, it is an innovative proposal implemented with collaborative assistance of institutes from Portugal, Italy and Japan. R-CASTLE is currently being adapted to support teachers in their remote classes during COVID-19 pandemic.
Referências
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Tozadore, D., Romero, R.: Comparison of image recognition techniques for application in humanoid robots in interactive educational activities. from portuguese: Comparação de técnicas de reconhecimento de imagens para aplicação em robô humanoides em atividades interativas educacionais. In: XXII Conferência Internacional sobre Informática na Educação, Fortaleza CE (2017).
Tozadore, D., et al.: Wizard of oz vs autonomous: children’s perception changes according to robot’s operation condition. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). pp. 664–669. IEEE (2017).
Tozadore, D., et al.: Effects of emotion grouping for recognition in human-robot interactions. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS). pp. 438–443. IEEE (2018).
Tozadore, D., et al.: Towards adaptation and personalization in task based on human-robot interaction. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). pp. 383–389. IEEE (2018).
Tozadore, D., et al.: Matching sentences in semantic and syntax level for humanrobot dialogues. To be published. (2019).
Tozadore, D., et al.: Project r-castle: Robotic-cognitive adaptive system for teaching and learning. IEEE Transactions on Cognitive and Developmental Systems 11(4), 581–589 (2019).
Tozadore, D., et al.: When social adaptive robots meet school environments (2019). https://doi.org/https://aisel.aisnet.org/amcis2019/cognitive_in_ is/cognitive_in_is/4/
Tozadore, D.C., Romero, R.A.F.: Facing the challenges of artificial intelligence in education through adaptive systems and social robotics. Computer and Education (2020).
Tozadore, D.C., Romero, R.A.F.: A fuzzy decision-making system using multimodal objective measures for educational adaptive systems. Expert Systems with Applications (2020).
Tozadore, D.C., Romero, R.A.F.: Graphical user interface for educational content programming with social robots activities and how teachers may perceive it. Revista Brasileira de Informática na Educação 28, 191 (2020).
