Context. Given the need for digital transformation in education during the pandemic, this study focused on the Dell Accessible Learning (DAL) platform and its adaptation to the metaverse environment to improve learning for People with Disabilities (PwD). Goal. To analyze the viability of the metaverse environment in providing collaborative and accessible interaction for DAL students. Method: We conducted a prior literature review to align requirements with the State of the Art, considering collaborative learning and accessibility. Interviews and usability analysis were conducted with 9 PwD, DELL employees, corroborating the information in the literature. After usability evaluations and interviews, it was found that the third-party metaverse environment did not fully meet the accessibility criteria and usability. In response, DALverse was developed, a proof of concept integrated with DAL. Result. The results indicate benefits, including increased resources, multimodal interactions, and student retention. Future research should explore the use of DALverse in parallel with DAL courses, aiming to measure changes in indicators such as interaction, engagement, and performance. Furthermore, we provide an architectural model used in DAL and DALverse and implications for education, researchers, industry professionals, students and tutors.

Adaptive teaching, mediated by technology, has become a reality and a great ally in distance teaching and learning. Artificial intelligence, through machine learning, has been an important tool for the development of innovative solutions for education. One of the approaches for adaptive teaching has been the student emotion analysis in distance learning to propose didactic solutions that best fit the profile of each student. In this sense, this work aims to present the preliminary studies carried out with SVM models and the AffectNet database in order to propose a solution for the analysis of emotions in the context of distance learning. As a result of this study, it is expected to propose an additional resource for the Homero platform (A Learning Management System) capable of adapting content based on information about the students' emotional states. In addition, this information can also be used to predict potential evasion risks. The development and preliminary tests point to a result with an average accuracy of 73% for emotion recognition based on video. With the tests, it was also possible to verify some necessary adjustments for the development of an efficient model for the recognition and analysis of emotions.

This Innovate Practice Full Paper document presents a technological ecosystem for classroom management that we call GSA. GSA is meant to be used in any class/ of any subject/k-college and supports electronically mediated in class, engaging activities learning strategies to reduce dropout rates and create more learning opportunities. In the previous study, we interviewed 123 8th and 9th-grade students and 15 teachers who had been using the GSA for two weeks. The results were promising. An increase in the level of motivation was observed. Being able to mediate in real time, teacher/student interactions create opportunities to capture valuable data that can be used to feed performance/dropout predictors. In this paper, we discuss the introduction of these predictors and how they can be used to foster preemptive measures to mitigate the problem.