Research On Dynamic Evaluation And Implementation Method Of Online Learning Effect Based On Knowledge Graph

Education informatization is an important initiative for the country to realize education modernization,and it is related to the development of China’s education,which is evolving in tandem with the arrival of di gitalization,networking,and intelligence.With the advancement of education informatization,the traditional learning mode of learners has undergone a significant change from offline to online,and the usage of a single learning effect evaluation clearly cannot meet the learner’s online learning needs.In view of this,with the premise of personalized test paper-pushing,this thesis aims to investigate a dynamic evaluation and implementation method of online learning effectiveness based on knowledge graph.The method assesses the effectiveness of learners’ learning using multiple evaluation methods.The method is critical for improving learners’ motivation to learn online and timely perceiving the current learning status.The specific innovative content is as follows:Firstly,in order to conveniently represent the association between subject knowledge points and fulfill the demand for reasoning and mining of knowledge points in learning effect evaluation,this thesis refers to the construction method of domain knowledge graph and constructs the knowledge graph based on middle school mathematics subject.The knowledge sources required for the construction of the knowledge graph are obtained,then nam ed entities and entity relationships are identified for completing the extraction of the knowledge sources,and knowledge fusion is utilized to update and expand the knowledge graph.Following the quality assessment of the knowledge graph by the domain experts,the construction of the knowledge graph of junior high school mathematics is completed,which provides the basic conditions for the subsequent research work.Secondly,in order to address the problems that the existing personalized test paper-pushing methods do not model the learners’ learning behavior data adequately and the algorithm is easy to fall into local optimum in the optimization process,this thesis designs a personalized test paper-pushing method based on the improved discrete artificial raindrop algorithm.The method establishes the learner model and the personalized paper model on the basis of learning behavior data and the constraints of the paper grouping conditions then resolve the model optimally by using the improved discrete artificial raindrop algorithm.Through simulation analysis and preliminary application research,the artificial raindrop algorithm is proved to be effective in applying the group paper optimization problem and has advantages in solving the quality of test papers in comparison to other optimization algorithms.Finally,for addressing the problems of single results of existing online learning effect evaluation methods and the lack of information displayed on learners’ learning status,this thesis proposes a new online learning effect evaluation method.The method uses personalized test paper-pushing to examine learners’ knowledge mastery status and builds a Bayesian network model based on knowledge graph,so as to uncover the underlying reasons for learners’ weak knowledge points and provide learners with multiple evaluation results.In this thesis,learners’online learning effectiveness has been evaluated in three aspects:learning quality evaluation,feedback on weak knowledge points,and learning compensation suggestions,respectively.This method enriches the online learning status information of learning compared with other evaluation methods and points out the direction for learners’ online learning.This thesis designs and implements an adaptive learning system based on the aforementioned research on online learning effect evaluation,which verifies the rationality as well as the effectiveness of the online learning effect evaluation method in this thesis.The knowledge graph-based online learning effect evaluation method can improve learners’enthusiasm for online learning to a certain extent,provide guidance and suggestions for learners’ online learning,while also bringing new ideas for research on learners’ online learning effect evaluation.