Application Of Computer-assisted Language Learning Technology Based On Visual Input Enhancement In Possessive Grammar Acquisition

Due to the extremely poor second language(English)environment in China,learners cannot be successfully aware of sufficient language features and lack grammar knowledge necessary for second language development.However,with the help of the computer-assisted language learning technology based on visual input enhancement,learners can implicitly perceive and acquire target features according to the “attention hypothesis”.The technology is used to make the syntactic objective of the tasks more salient through external manipulation to attract learners’ attention and promote them to acquire the syntactic objective of the tasks.At the same time,during their learning process,a large amount of procedural data will be generated,which can be integrated to build explainable cognitive computing models between learners’ learning behaviors and learning effects and help teachers better understand the cognitive process of learners to achieve personalized differential teaching.Therefore,a cognitive data collection tool(Tobii)is used for the intervention of the computer-assisted language learning in this study.Also,three modes of presentation of visual input enhancement technology are integrated,respectively one-by-one input without semantics,batch input without semantics and batch input with semantics,to explore the respective impacts of these three different modes on promoting young second language learners to acquire second language features of the target.In this experiment,the possessive grammar is selected as the target feature,and visual enhancement methods such as color block,font color addition,italics and animation are adopted for the target feature.The experiment includes pre-test and real-time test.By using T test,multi-factor analysis of variance,Kullback-Leibler divergence and other methods,descriptive statistical analysis and preliminary exploration of the relationship are made and modeling exploration is carried out by machine learning.Finally,particle swarm optimization is used to calculate the optimal parameters so as to obtain the calculation model with parameters.Conclusions drawn from the experiment are as follows:(1)These three modes of presentation of visual input enhancement technology can attract learners’ attention and promote learners’ acquisition of possessive grammar to different degrees.However,in terms of the effect,batch input with semantics>one-by-one input without semantics>batch input without semantics;(2)The effect of visual input enhancement technology is not absolute and prior knowledge is a significant influencing factor.If the resources required for prior knowledge are more than those possessed by learners themselves,their cognitive load will be caused.The way to ensure that learners can continue to absorb knowledge is to add new stimulus conditions to the stimulus materials,so that learners can re-establish a bridge of understanding between existing knowledge and second language grammar;(3)Learners’ cognitive characteristics and environmental factors are integrated through learner’ cognitive computing model.From the model,it’s found that in addition to the prior knowledge,the difficulty of knowledge points is the second significant factor affecting the learning effect.For difficult knowledge points,learners need to study about four times to reach a stable level and usually two times for simple knowledge points.This study provides a reference for teachers to design teaching materials with visual input enhancement,and a basis for teachers to make teaching analysis and achieve personalized difference teaching.