Recomendação automática de atividades e rastreamento de competências do sujeito complexo em ambientes virtuais de ensino e aprendizagem

Abstract

A persistent challenge in Virtual Learning Environments (VLEs) is the lack of mechanisms to track, over time, higher-order competencies—such as autonomy, cooperation and metacognition—despite the abundance of interaction traces recorded in these platforms. Such tracking can support formative and personalized study guidance. In this context, this thesis develops an automatic pedagogical activity recommender to monitor and foster complex subject competencies in Distance Education. The work was developed in two integrated stages. First, multiple Moodle activities were constructed and refined through competency labeling based on seven complex-subject competencies, supported by quantitative analysis, resulting in an activity×competency matrix validated by educators experienced with Moodle. Second, the M-COMPASS recommender was designed by adapting Dynamic Key-Value Memory Networks (DKVMN), incorporating dynamic memory and attention mechanisms. The main modifications include integrating students’ behavioral signals via SQL queries and introducing competency-level attention, which also grounds a new student model. Experiments conducted in three courses within a Computing Teacher Education program provided consistent evidence: (i) adherence to recommendations is associated with better performance (positive intra-module and overall correlations); (ii) competency-level dose–response patterns, with gains observed both in the short term and cumulatively across modules; and (iii) training stability, indicated by a regular decrease in loss and interpretable individual cases via competency attention and radar charts. The analyses also supported an instrument-improvement cycle (Ridge+LOO diagnostics over SQL queries), reducing redundancies and correcting constant columns. Overall, the thesis contributes a full Moodle→SQL→student model→explainable recommendation pipeline aligned with Complex Thinking Theory, and a DKVMN adaptation for multiple competencies per activity with pedagogical justifications.