Towards Distributed Intelligent Tutoring Systems Based on User-owned Progress and Performance Data

The use of recommendation engines to personalise students’ learning experiences can be beneficial by providing them with exercises tailored to their knowledge. However, the use of these systems often comes at a cost. Most learning or tutoring systems require the data to be stored locally within a proprietary database, limiting learners’ freedom as they move across different systems during their learning journey. In addition, these systems might potentially cause additional stress, as the learner might feel observed without knowing who has access to their learning progress and performance data. We propose a solution to this problem by decentralising learning progress and performance data in user-owned Solid Pods. We outline the proposed solution by describing how it might be applied to an existing environment for programming education that already includes research on how to align difficulty levels of exercises across different systems.