Integrating flow framework into learning and development systems

This study explores the integration of the flow framework into a Learning and Development Systems (LDS) setting, with the aim of enhancing personalized learning experiences. We propose an adaptation of the flow framework to better reflect the dynamics of LDS environments. The model is tested by analysing a learning path generated by an existing recommendation algorithm, suggesting that traditional approaches based on a priori difficulty might fail to sustain optimal experience. We then implement an adapted recommendation that maintains the flow dynamics throughout the learning materials. The results highlight a trade-off between preserving alignment with flow and ensuring full content coverage. This work suggests that flow-informed design can enhance the personalization of mathematics learning within LDS and highlights the need for further developments in learner profiling and feedback integration.