Which educational interventions have been implemented to support clinical competencies among primary healthcare professionals? A scoping review.

Teachers and researchers usually agree on one thing: assessment and feedback matter. They sit close to the center of learning, shaping how students build knowledge, notice weaknesses and move toward intended outcomes. In online and blended courses, assessment is also expected to do extra work. It should not only certify performance at the end, but also steer adaptive learning paths and support more personalized teaching. In practice, we still see something much simpler. On many e-learning platforms, feedback appears as a single score and a short, generic remark. Large logs of clicks, submissions and interactions are collected, but are rarely integrated in ways that meaningfully support instructors. Part of the problem lies in how learner progress is modelled. Many systems rely on fixed thresholds or black-box machine learning models to decide who is doing well and who might be at risk. These approaches can hit reasonable accuracy on a test set, but they seldom represent the links between learning objectives, assessment tasks and a learner’s evolving knowledge state. When we tried to interpret such models on LMS data, we often ended up with predictions that were difficult to explain to a tutor in concrete, pedagogical terms. This lack of interpretability limits their usefulness in real educational settings, where instructors need to understand not only what the system predicts, but also why those predictions are made. A second difficulty is the way data and models are fragmented. Information about learners is scattered across different tools and databases, sometimes even within the same institution. That makes it hard to reuse knowledge across courses, and also hinders the generation of feedback that is personalized, context-aware and clearly justified at scale. Semantic web technologies, especially ontologies and knowledge graphs, offer one route out of this fragmentation. Ontologies introduce a shared vocabulary for a domain, spelling out competencies, assessments and pedagogical strategies in a machine-readable form. Knowledge graphs then tie together heterogeneous data (student interactions, performance records, course structure) into a linked network that can support automated reasoning. Used together, they can align observed performance with intended learning outcomes and highlight learning patterns that are hard to see in flat tables or dashboards.