Contribution: A competency assessment framework that enables learning analytics for course monitoring and continuous improvement. Our work fills the gap in systematic methods for competency assessment in higher education. Background: Many institutions are shifting toward competency-based education (CBE), thus encouraging their educators to start evaluating their students under this paradigm. Previous research shows that structured assessment models are fundamental in guiding educators toward this adoption. Intended Outcomes: An assessment model for CBE that is easy to adopt and use, while facilitating the application of learning analytics techniques. Application Design: The new framework considerably extends a prior model we proposed three years ago. Two engineering competency-based courses used the framework for assessment. Assessment rubrics were prepared and used for evaluating and collecting the students' data progressively, thus enabling the use of learning analytics for decision-making. Findings: Thanks to the model: 1) students received a detailed report of their achievements, including a thorough explanation and justification of the evaluation criteria and 2) instructors could improve the course and provide objective evidence of their actions to quality assurance agencies. As a result, the framework is presently being used in 15 courses taught at eight different university degrees at the Pontifical Catholic University of Valparaiso (PUCV).