STUART: An Intelligent Tutoring System for Increasing Scalability of Distance Education Courses - Damasceno, Adson Roberto Pontes; Martins, Alan Rabelo; Chagas, Matheus Lima; Barros, Eudenia Magalhães; Maia, Paulo; Oliveira, Francisco (Anais do Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (IHC); 2020: Anais do XIX Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, 2020)
Context: Distance Education Courses in Virtual Learning Environments (VLE) employ tutors for pedagogical support, monitoring of students, and detecting possible dropouts. However, when courses have a higher number of students, there can be a work overload on human tutors, impacting their work quality. To mitigate this problem, artificial smart tutors can be used not only to increase the capacity to meet students’ needs but also to improve the monitoring of their performance. Increasing the scalability of online courses can be achieved using an intelligent artificial tutor. Goal: Analyze the viability of an intelligent tutor in meeting the main tutoring demands made by students on Dell Accessible Learning platform. Method: We developed an artifact called STUART that monitors DAL and interacts with students, providing automation, intelligence, and support to the teaching and learning process. We programmed STUART to meet reactively and proactively students’ main demands, based on the corpus of interactions scenarios on previous courses. Fourteen participants attended two classes of a Distance Education Course, with and without STUART, where interaction data were collected. Results: For 76% of the participants, STUART helped solve the more frequent problems in the pedagogical, technical, and content levels. There was an average reduction of 87% in the requests for a human tutor and a reduction of 27% in the time needed to finish tasks. That resulted in the preference of STUART compared to the human tutor for 85% of the students. SUS (usability assessment) scored 86.