Competency-aware educational recommender systems enable personalized learning, but the absence of standardized benchmark datasets prevents systematic algorithmic comparison and limits progress. This thesis develops a collaborative platform to standardize the collection of ground-truth competency networks across institutions. Contributors map competency relations and link them to learning resources, producing an open benchmark dataset for the performance evaluation of recommenders. The initial release focuses on computer science to seed the dataset and integrates guided onboarding and light gamification to support sustained contributions.