Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into account

In a context-aware ubiquitous learning environment, learning systems are aware of students’ locations and learning status in the real world via the use of sensing technologies which provide personalized guidance or support. In such a learning environment that guides students to observe and learn from real-world targets, various physical world constraints need to be taken into account when planning learning paths for individuals. In this study, an optimization problem is formulated by taking the relevance of real-world learning targets and the environmental constraints into account when determining personalized learning paths in the real world to maximize students’ learning efficacy. Moreover, a hyper-heuristic approach is proposed to efficiently find quality learning paths for individual students. To evaluate the performance of the proposed approach, the teachers’ feedback was collected and analyzed based on the learning activities conducted in an elementary school natural science course; in addition, the performances of the proposed algorithm and other approaches were compared based on a set of test data.