A Study Of Micro-learning Path Recommendation Based On Optimizing Pheromone Algorithm Of ACO

As a new learning style,micro-learning was born of online learning,and adapts to fragmented age.The main feature of micro-learning is that the content of learning unit is relatively small,and consists of multiple content,for example text,audio/video and picture,which can be learned at anywhere and anytime.Because of the above features,micro-learning can be flexible re-organized.Therefore,micro-learning is widely focused by many researchers after it was born.With the increase of learning contents,micro-learning face the problem of "information overload",which is similar to online learning.Learners have to spend a great deal of time in finding suitable learning units,thus affect the efficiency of learning.The mentioned issues make us to find a solution.In this paper,we try to integrate the principle and technologies of online learning with micro-learning,and propose an optimizing pheromone algorithm of ACO(Ant-ColonyOptimization)for recommending micro-learning unit and micro-learning path,in which,the micro-learning units are gradually recommended to target user.This algorithm combines the micro-learning features with ant colony algorithm,which can effectively improve the learning efficiency of learners in microlearning.After introducing the related concepts of recommending learning path,learning unit attributes,learner's characteristics,learning path and definitions,we describe the architecture of recommendation model.And then,we propose an optimizing pheromone algorithm of ACO,which is the core of this study.This paper introduces the overall design framework of the micro-learning path recommendation method and describes the functions of each module in the framework.Based on the above,this paper introduces the steps and specific processes of proposed algorithm,and expounds the procedure of monitoring the change of learners' learning situation.The pheromone concentration optimization method uses the adaptive function of pheromone concentration coefficient.MATLAB simulation environment is then used to get the parameter of the proposed algorithm.Finally,the experimental results show that the proposed solution can recommend suitable learning paths to target learners and improve the learning efficiency.Based on the analysis of the online learning and micro-learning,and combined with the features of micro-learning and the characters of ACO algorithm,this paper proposes an optimizing pheromone algorithm of ACO to recommend learning path in micro-learning based on the reference of methods in online learning study.The main innovations of this paper are shown as following.1.According to the analysis of characteristics of the micro-learning,the proposed method takes advantage of the feedback of ACO algorithm,and recommends learning path in the sizes of learning units rather than the whole learning path to further improve the recommended accuracy.2.This paper analysis the learning process and learning elements,which monitors learning process with the classification property of ant colony algorithm.In this way,the proposed method optimizes the recommendation strategy of micro-learning path according to the learning status changes of the learners to optimize the learning path and infer the changing needs of learners.3.This paper reviews the process of learning deeply,and optimizes the mechanism of the pheromone updating.By using pheromone concentration coefficients adaptive function to adjust the global and local pheromone concentration in method,optimize the recommendation results,and improve the learning efficiency.