Research On Online Open Course Teaching Platform Based On WeChat

At present,online learning has become a common way of learning,in which learners are not confined to the physical classroom,but can break through the traditional time and space to choose to learn freely.In the past few years of the development of MOOC,online learning platforms have emerged one after another,which has brought great convenience to people.However,we also notice a sharp increase in knowledge redundancy in the face of massive amounts of learning.Under the surface of abundant knowledge supply,there are inevitable phenomena such as learning to trek and knowledge of overload,which cause the real problem of learners' learning cost soaring in the online learning environment.Therefore,the paper puts forward a personalized online learning system model based on Wechat.This system tries to integrate three levels,including knowledge structure organization model,learning resource recommendation algorithm and Wechat platform development technology,and gives a technical solution to the above problems.The main work of the paper includes:(1)The paper presents a knowledge map model with heat feature.Aiming at the phenomenon of learning trek,the paper fully excavates the connection between knowledge points based on relevance theory,knowledge map theory and heat map theory.A knowledge map model with heat feature is constructed with the introduction of thermal map theory.The model emphasizes the integrity of knowledge frame and makes clear the relation between knowledge points.In particular,it helps learners solve the problem of learning trek by increasing the heat of the learning path to express the strength of the relationship between knowledge points.(2)The paper proposes multi-layer perceptron improved deep neural network learning resource recommendation algorithm.Aiming at the phenomenon of knowledge overload,the paper introduces the personalized recommendation mechanism according to the personalized recommendation strategy,and uses MLP to improve the way of scoring prediction in the model by studying the recommendation algorithm of deep neural network.The experimental results show that the improved model can better fit the scoring,enhance the recommendation performance and solve the problem of knowledge overload.(3)The paper designs and implements a personalized online learning system based on Wechat.The system completely relies on the ecological characteristics of Wechat 's 1 billion users and follows the object-oriented software engineering method.The front-end subsystem uses Mini Program technology,and the back-end subsystem uses B/S architecture and Java development language.Considering the integrity of the system,the paper designs a functional module of online assessment to optimize the teaching design on the basis of the innovative function--knowledge map and resource recommendation.To sum up,the personalized online learning system based on WeChat adds navigation elements for learners through the knowledge map model with heat feature and overcomes learning trek.The deep neural network learning resource recommendation algorithm improved by MLP can meet the personalized needs of learners and solve the problem of knowledge overload.According to the feedback of the initial application of the system,the target of knowledge acquisition is greatly increased,which effectively improves the learning efficiency and reduces the learning cost.