Research On The Influence Of Self-regulation Based On Media Interaction On Deep Learning

Designing high-quality digital learning resources is in line with the strategic demand of building an education power,and it is also an inevitable requirement to promote the development of educational informatization.At present,in terms of quantity and scale,Chinafs digital resource construction has ushered in blowout development,but it has been criticized in terms of quality and effect,and was once called the "breeding ground for shallow learning".How to carry out high-quality deep learning based on digital media resources has become the e-learning problem to be solved at this stage.As an internal psychological mechanism for individuals to climb and transform from shallow learning to deep learning,self-regulation is an effective way to promote deep learning,and it can build a bridge between media resources and deep leaming.Therefore,this study combines theoretical research with empirical research to explore how to support individuals to carry out self-regulation based on media interaction,so as to promote the occurrence of deep learning.Theoretically,starting from the planning stage,monitoring stage and evaluation stage of self-regulation,we construct the mechanism model of planning regulation,monitoring regulation and evaluation regulation affecting the cognitive processing and emotional experience of deep learning.On this basis,under the guidance of the theoretical framework of multimedia picture linguistics,and combined with the media representation methods in different stages of self-regulation,we constructed a theoretical model of self-regulation affecting deep learning based on media interaction.Empirically,around the above model,this study combines eye movement technology with subjective and objective questionnaires.We carried out the study on the impact of planning regulation on deep learning through media interaction support for students to plan their learning paths,the impact of monitoring regulation on deep learning through media interaction support for students5 self-questioning at a high level,and the impact of evaluate regulation on deep learning through media interaction support for students' reflect and attribution.The research of the impact of planning regulation made on deep learning supported by media interaction finds that,the learning path jump is more reasonable and the logic between knowledge nodes is clearer in the way that learners independently plan their learning paths.It can promote the eye movement cognitive input in the learning process,reduce the cognitive load,improve the deep motivation and strategy,and maximize the transfer application and innovative application performance.This suggests that when designing knowledge map support planning regulation,learners should be given as much autonomy as possible on the basis of fully considering the complexity of knowledge content.The research of the impact of monitor regulation made on deep learning supported by media interaction finds that,under the way of systematic control to present learners with self-question scaffold,students are able to raise critical and in-depth questions and trigger deep thinking on learning content.lt can promote the eye movement cognitive input,increase a certain cognitive load,enhance the deep motivation and strategy,and maximize the student's knowledge understanding,transfer application and innovative application performance.This suggests that we should pay attention to avoid additional cognitive load when designing self-questioning scaffolds to support monitor regulation.The research of the impact of evaluate regulation made on deep learning supported by media interaction finds that,under the way of systematic control to present learners with attribution scripts and self-assessment scales,students can develop correct attributions of success and failure and increase positive academic experience,improve the deep motivation and strategy,and then increase the eye movement cognitive input in the learning process,reduce the perceived cognitive load,and maximize the transfer application and innovative application performances.This suggests that the assessment scale and attribution script for learners can lead to positive and correct attributions of success and failure,which can bring about positive emotional experience and indirectly affect cognitive processing.