Research And Application Of Adaptive Recommendation Of Learning Resources In Fragmented English Reading

With the development and popularization of mobile Internet and information technology,the learning resources of English reading are becoming more and more abundant,and the learning methods are more and more mobile and intelligent.The rapid growth and disordered distribution of information pose challenges to English learners' autonomous individualized learning anytime and anywhere.In mobile fragmented learning environment,how to quickly excavated from the huge amounts of data information weak link in the process of learners' learning in English reading,and recommend appropriate resources to them according to the relationship between learners' characteristics and resource characteristics,and give appropriate training for learners' weak links in learning,which has practical significance for improving learners' learning efficiency and learning effect.This research has mainly completed the following aspects:(1)This paper constructs an adaptive recommendation model for learning resources in fragmented English reading.Through the literature research,this paper designed the five-dimensional feature model of learners(learners' English reading ability,cognitive style and learning target,learning situation and learning result)and the three-dimensional feature model of English reading resources(question type,theme and difficulty of resources)in the fragmented learning environment.At the same time,in order to make learning resources conform to the fragmentary and discrete characteristics of time and space in fragmented learning environment,the reading resources of CET-4 are designed and segmented reasonably to meet the needs of mobile fragmented learning of learners.Then combined with the machine learning algorithm ID3(Iterative Dichotomiser3),The adaptive recommendation model of learning resources in fragmented English reading is constructed.(2)An adaptive recommendation algorithm for fragmented English reading resources based on ID3 algorithm is designed.When the resource is adaptively pushed,the attribute with the highest information gain is used as the classification attribute of the current node,and the classification is performed in order to generate a decision tree.According to the generated decision tree,the expression rules are extracted to realize the adaptive push of resources.(3)Designed and implemented a learning resource adaptive recommendation system in fragmented English reading.According to the principle of software engineering,the requirements,target users,feasibility of the system are analyzed,and the system architecture,function modules and database are designed.The system is developed by using Android Studio,Visual Studio,SQL Server and other tools to realize the adaptive learning resources in mobile fragmented English reading.And the learning resource adaptive recommendation app in the mobile fragmentation English reading is implemented to assist the learner to practice the careful reading module of CET-4.(4)The application effect of learning resource adaptive recommendation system in English fragmentation reading is analyzed.This paper analyzes the application effect of learning resource adaptive recommendation system in English piecewise reading.Learners who are ready to apply for CET-4 are selected to use the App for English reading learning one month.Finally,learners who are ready to apply for CET-4 are selected as test samples,and they are asked to use the App for one month's English reading study.The App automatically collects the data from learners during the learning process,and uses the correlation analysis,multi-factor analysis of variance,regression analysis,cluster analysis and other statistical analysis methods in spss to learn the learner model,resource model,resource adaptive recommendation model and The learning effect is analyzed.Discover,?1 In mobile fragmented learning environment,the proposed adaptive push strategy for English reading resources is effective,and it is feasible to use mobile terminal for fragmented English reading.?2 The question type dimension in the resource model and the English reading ability,cognitive style,learning target dimension and the setting of the value range in the learner model are more reasonable.?3 The setting of the range of theme and difficulty dimensions in the resource model,and the way to obtain the volume dimension data in the learning situation of the learner model need further study.This study shows that adaptive recommendation of learning resources in fragmented English reading can help teachers formulate future resource recommendation strategies through effective data sets,and adaptively push resources close to learners' individual needs.English learners use mobile terminals to learn reading in fragmented learning situations,which can help them train the weak links of English reading.In order to improve the different aspects of learners' reading ability and improve the passing rate of CET-4.