Design and evaluation of planning and mathematical models for generating learning paths

Education research has emphasized the need to develop instructional design tools to facilitate the generation of learning paths for students. Learning paths are important because they enable the personalization and optimization of the learning process. In this work, we present a flexible conceptual framework that allows the representation of curricula information as Artificial Intelligence Planning and Mathematical Programming models to facilitate the generation of learning paths by domain independent algorithms. The resulting models consider a rich set of properties from the education domain, like hierarchical learning structures, enabling conditions, temporal actions, mandatory activities, quality accumulation functions, and metric information. We show that the proposed mathematical models return optimal solutions very efficiently if we relax the total ordering constraints of learning paths. These relaxations allow evaluating greedy planning algorithms to identify the properties from the models that increase the complexity of solution synthesis. We expect that the results of this research can be helpful to education researchers and computer scientists in the quest of scalable systems that capture more flexible standards to model learning and compute more informed learning paths for students.