An Improved Ant Colony Optimization Algorithm for Recommendation of Micro-Learning Path
Qin Zhao, Yueqin Zhang, Jian Chen · 2016
This paper proposes an approach of recommending micro-learning path based on improved ant colony optimization algorithm. Micro-learning is a new learning style, which can be used to support learning in short time because of its micro-learning units. Each micro-learning unit consists of a small knowledge unit that can be learned at fragmented time. Meanwhile, micro-learning is more flexible than other learning styles in organizing or reorganizing learning path according to the transition of learner. In order to improve learning efficiency, a suitable learning path contained a sequence of micro-learning units is recommended to learner, which is optimized according to his/her transition. During the process of micro-learning, the proposed algorithm can detect learner's learning transitions of knowledge level, knowledge area and learning goal according to the operation of learner. In this study, the premature problem of ant colony algorithm is solved by optimizing the mechanism of initialization and update of pheromone. The experimental results show that the algorithm has high efficiency in micro-learning path recommendation.