Supervised association rules for adapting online Competency-Based Learning Path

Online Competency-Based Adaptive Learning Path pertains to a structure aimed at providing tailored learning experiences to students through personalized content, delivery methods, and learning speeds that align with their individual capabilities. This approach involves utilizing data analysis methods to identify learners' abilities or objectives, subsequently suggesting appropriate learning activities that cater to their specific aptitude or cognitive level. Within this paper, we introduce a model for an Online Competency-Based Adaptive Learning Path. To achieve this, we have formulated a multi-layered structure for breaking down competencies. We then devised an algorithm to explore example learning paths based on this structure. Following this, we harnessed these example paths by applying machine learning techniques, specifically supervised association rules, to adapt the learning paths for novel learner experiences.