In a context-aware ubiquitous learning environment, learning systems can detect students' learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that models the objectives and criteria for determining personalized context-aware ubiquitous learning paths to maximize the learning efficacy for individual students is formulated by taking the meaningfulness of the learning paths and the number of simultaneous visitors to each learning object into account. Moreover, a Heuristic Algorithm is proposed to find a quality solution. Experimental results from the learning activities conducted in a natural science butterfly-ecology course of an elementary school are also given to depict the benefits of the innovative approach. (Contains 7 figures and 9 tables.)