Research On E-learning Resource Personalized Recommendation And E-learning Path Planning

Along with the arrival of big data age,obtaining of e-learning resource is becoming more and more convenient,which makes people get access to knowledge whenever and wherever possible without restriction of time and space.At present,Massive Open Online Courses(MOOCs)as a kind of emerging forms of education receives liking of learners gradually because of its characteristics such as resourceful,convenient and swift,cheap and so on.Many enterprises and colleges excavate the high score talent one after another from the e-learning platform.Nevertheless,massive e-learning resource has caused the information overload problem.Most of the e-learning platforms have not addressed this problem completely by simple keyword search and popular recommendation function.In addition,the e-learning platforms have high requirements for users' consciousness.Due to the lack of humanistic care and professional guidance of teachers in traditional face-to-face education,most learners cannot plan their learning path.Thus,it leads to high dropping rate and low course passing rate of e-learners.To solve the problems mentioned above,this paper aims at solving the information overload problems in e-learning platforms,focusing on the two reasons of high dropping rate and low course passing rate of e-learners.More attention is paid on how to break through difficult problems caused by “one to many” pattern of conventional teaching,and more study is devoted into the hybrid e-learning resource personalized recommendation techniques and the e-learning path planning techniques based on swarm intelligence.The main innovation of this paper is as follows:1.We propose an e-learning resource personalized recommendation model based on users' career goals.This paper uses users' career goals as a starting point,considering users' similarities from multiple aspects,which effectively alleviate the cold-start problem of traditional collaborative filtering techniques.We also pay attention to the correlation between career goal and resources and user's self know-how,which makes recommendation results more professional and specific.It effectively solves the information overload problem that occurs in e-learning area.2.Sequential pattern mining technique is applied to personalized recommendation method of e-learning resources,paying close attention to the relevance between e-learning resources.We use sequential pattern mining algorithm PrefixSpan to dig user's behavioral data,thereby analyzing the relevance between e-learning resources.This will help to improve the validity of personalized recommendation,and also make the planning of e-learning path more in line with the actual process.3.We propose an adaptive e-learning path planning mode based on swarm intelligence technique.First,the index of learning style questionnaire proposed by Felder and Silverman has been adopted in this paper in order to calculate users' similarity of learning styles,rather than generally dividing user's category.Then,users' similarity has been considered into the planning of e-learning path.The optimum e-learning path will be obtained for different users by modifying the original ant colony system algorithm.This will help users accomplish their career goals efficiently and improve user's whole satisfaction,which will further solve the problem of learner's high dropping rate and low course passing rate.Finally,we conduct comparative experiments from two aspects to evaluate the feasibility and effectiveness of the e-learning path planning method combining with e-learning resources personalized recommendation method respectively.Simultaneously,we conduct an e-learning resource personalized recommendation system prototype to illustrate the practicality of method above.The experiment results show that the e-learning resource personalized recommendation method proposed in this paper can effectively solve the information overload problem of e-learning resource,and the e-learning path planning method can help users accomplish their career goals efficiently in order to meet the demand for talent in many enterprises.Finally,it will gradually improve the high dropping rate and low course passing rate of learners.It has certain guiding and practical significance in improving the e-learning quality and the whole education level in our country.