Didabot: sistema de auxílio para estudantes em plataformas

Abstract

The increase in distance learning through EAD (distance learning) platforms has become a growing phenomenon in recent years, especially accelerated by the COVID-19 pandemic. EAD platforms have established themselves as a common practice in higher education and have recently attracted more attention due to the search for more personalized and scalable solutions. The objective of this research was to develop a viable technological solution to automate the monitoring of students on EAD platforms, improving their engagement and performance throughout their study journey. The solution focused on creating a personalized platform that interacts with students and provides automated support. The research used both qualitative and quantitative approaches, collecting information about the routines of teachers and tutors in managing courses and tools, as well as interacting with students to measure engagement. It is worth noting that the research was conducted before entering the master's program, with the data collected during a process prior to the start of the program, meaning that the research did not have specific ethical approval for the master's studies. The applied nature of the study focused on developing a practical solution: the "DidaBot" platform, which automates actions based on student performance and specific events within the EAD platform. The research sought strategies to increase student engagement and improve retention by personalizing monitoring according to each student's needs. The result was the development of a prototype SaaS platform called DidaBot, which connects with EAD platforms like Moodle via API. The platform automates the monitoring of student performance and personalizes interactions based on specific events, such as logins and grades, to enhance engagement and reduce dropout rates. The developed prototype already includes automated features for tracking student performance, generating reports, and personalizing content. While an AI-based advisory system is a planned feature, it has not yet been implemented in the current version of the prototype. The automation of recurring processes, such as monitoring activities and sending personalized notifications, aims to optimize the management of EAD courses. By reducing the workload of tutors and managers, the platform enables more humanized and personalized support for students in the future, using artificial intelligence to enhance academic advising and guidance.