To ensure the validity of an assessment programme, it is essential to align it with the intended learning outcomes (LO). We present a model for ensuring assessment validity which supports this constructive alignment and uses learning analytics (LA). The model is based on LA that include a comparison between ideal LO weights (expressing the prioritization of LOs), actual assessment weights (maximum assessment points per LO), and student assessment results (actually obtained assessment points per LO), as well as clustering and trace data analysis. These analytics are part of a continuous improvement cycle, including strategic planning and learning design (LD) supported by LO prioritization, and monitoring and evaluation supported by LA. To illustrate and test the model, we conducted a study on the example of a graduate-level higher education course in applied mathematics, by analysing student assessment results and activity in a learning management system. The study showed that the analyses provided valuable insights with practical implications for the development of sound LD, tailored educational interventions, databases of assessment tasks, recommendation systems, and self-regulated learning. Future research should investigate the possibilities for automation of such LA, to enable full exploitation of their potential and use in everyday teaching and learning.