Applications of Deep Learning Technology in Intelligent Learning Evaluation
With the accelerated penetration of an new round of technological revolution in the field of educational evalua-tion,deep learning,as one of the significant research directions of artificial intelligence,has shown great application poten-tial in the field of intelligent learning evaluation.By leveraging deep learning technology to construct complex neural net-work models and automatically extract multidimensional data features from the learning process,it is possible to accurately identify and evaluate students'learning states,progress,and outcomes.Furthermore,deep learning technology can provide personalized learning suggestions and resource pushes tailored to the diverse needs of students,thereby promoting stu-dents'autonomous learning and comprehensive development.Consequently,intelligent learning evaluation based on deep learning technology has not only substantial significance for China's educational evaluation reform,but also important theo-retical and practical value for promoting the digital and intelligent transformation of education.To facilitate the further ap-plication of deep learning in the realm of intelligent learning evaluation,this paper used the method of literature research to screen and statistically analyze 1,423 relevant research literatures on CNKI through manual filtering.After excluding litera-ture that is evidently unrelated to the topic and non-technical support for deep learning(i.e.,deep learning within the edu-cational domain),a final sample of 33 research articles was obtained.Based on this,the paper summarized three main research directions for the application of current deep learning technology in intelligent learning evaluation including the recognition and analysis of learning behavior and emotional characteristics,the collection,classification,and fusion of mul-timodal data,and personalized resource push and services.Further analysis of the current problems and challenges,including poor interpretability of evaluation results,insufficient generalization ability of models,and data update and confidentiality issues are conducted.Four development suggestions,including improving intelligent learning evaluation models,establishing a hu-man-machine collaborative evaluation system,collaboratively building a large-scale shared database,and promoting data security through technological integration are proposed,aiming to provide references and insights for subsequent related re-search.
deep learningintelligent learning evaluationbehavior recognitionmultimodal data
国家科技期刊平台
NSTL
万方数据
