Dealing with a large number of students and inequality when teaching programming in higher education - Xará, Gabriel; Moraes, Laura O.; Delgado, Carla A. D. M.; Freire, João Pedro; Farias, Claudio Miceli de (Anais do Workshop de Informática na Escola (WIE), 2023)
Considering the characteristics found in the post-pandemic scenario of public higher education in Brazil, we must address issues related to equity in access to educational resources. We present the new features of Machine Teaching, a web system used in introductory programming classes to support students and instructors. The innovations aim to adapt the system to post-pandemic conditions and the diverse public resulting from policies to democratize access to Brazilian universities. We present functionalities to mitigate problems related to many students per instructor, such as student dashboards and alerts indicating disengaged students who are likely to drop out. We also address computer and internet access difficulties by transforming the architecture from client-based to remote-based.