摘要： 为帮助学习者提高学习效率,针对微学习的特点,提出一种信息素浓度逐次适应调整的蚁群优化算法,以此优化微学习路径的推荐。在微学习的整个过程中,通过学习者与系统的交互获取学习者的学习状态,并根据学习状态调整学习路径的推荐策略。在学习单元的粒度上调整学习路径,从而实现捕捉满足学习者的个性化需求,帮助学习者提高学习效率。

关键词: 蚁群优化算法, 学习路径, 微学习, 个性化推荐, 逐次适应

Abstract: This paper proposes an Ant Colony Optimization(ACO) algorithm for gradually adaptation and adjustment of pheromone concentration to improve the learning efficiency of learners in view of the characteristics of micro-learning,which optimizes the recommendation of micro-learning paths.In the whole process of micro-learning,the learning state of the learner is acquired through the interaction of the learner and the system,and the strategy of the learning path is adjusted according to the learning state.The learning path is adjusted in the granularity of the learning unit,thus achieving the goal of catching the individual needs of the learners and helping the learners improve their learning efficiency.

Key words: Ant Colony Optimization(ACO) algorithm, learning path, micro-learning, personalized recommendation, gradually adaptive

中图分类号: 

• TP301.6