The Construction And Experimental Study Of Learners' Cognitive Model

With the advent of the era of intelligent education,the application of learners' physiological and psychological data is the trend of learning analysis research,which can provide more cognitive characteristics of learners.Combined with artificial intelligence technology,it can provide more accurate explanation models for learning behavior and study result.The language environment of elementary English education in China is very poor.Computer-assisted language learning is an important channel to solve this problem and improve students' English acquisition.However,when using these computer-assisted resources for learning,it is still necessary to explore how individual differences in cognitive abilities would affect the acquisition.Using cognitive data to model,we can perceive the corresponding between cognitive characteristics and learning effect,and better intervene learners' learning state.Therefore,this study uses a cognitive tool(eye tracker)to collect learners' cognitive data in the process of computer-assisted language learning,then builds cognitive computational models using learning analysis technology,and proposes strategies of learning intervention.The specific research work is as follows:First of all,this paper extracts the main elements needed to build the learners' cognitive model,and constructs a framework and technical process for building the model that meets practical requirements.The framework includes cognitive data collection,data preprocessing,preliminary exploration of relationships,modeling,interpretation and evaluation.Secondly,the correlator is used to construct the relevance model between visual attention and phonological short-term memory in the learning process.The cognitive model provides evidence that the performance of attention control is flexible and provides direction for learning intervention: Attention intervention.The principle of intervention is determined: to suppress interference information,highlight the target characteristics of second language,draw conscious attention to the target characteristics.Finally,this research attempts to use regression and classifier to build cognitive computational models with sentence pronunciation as the target task.The methods and strategies summarized in this study can provide reference and basis for later researchers to analyze learners and formulate educational intervention strategies.All in all,with the continuous improvement of rule extraction technology,it will bring more new breakthroughs to the field of learning analysis.Cognitive computational modeling would integrate learners' cognitive data to form more complete user models,which will lay a foundation for learners' personalized learning.