Evaluating competency development and academic outcomes: insights from six semesters of data-driven analysis

Competency-Based Education (CBE) has been widely studied since the 1970s, yet it remains innovative due to its challenges across disciplines and cultures. Tecnol & oacute;gico de Monterrey, a Mexican private institution, implements CBE through its Tec21 model, which emphasizes challenge-based learning to develop disciplinary and transversal skills. Since its launch in 2019, Tec21 has generated extensive data, offering an opportunity to assess its performance and ensure quality. This study analyzes data from six academic semesters in the School of Engineering and Sciences to address key quality assurance questions. First, we evaluate whether initially enrolling in a generic area before selecting a specific program improves long-term student outcomes. Second, we examine competency development, identifying challenges in achieving certain skills and their links to dropout rates and module difficulty. Third, we explore the relationship between final grades, module credit allocation, and Tec weeks to assess curriculum alignment with academic performance. Using data from over 550,000 evaluations of 4500+ students, our analysis provides robust quality metrics. Findings suggest that students who start in generic areas perform better long term and highlight the complex interplay between competencies, module characteristics, and academic success. These insights deepen the understanding of CBE implementation and offer recommendations to improve educational strategies and quality assurance within competency-based frameworks.