首页|Student Academic Performance Predictive Model Based on Dual-stream Deep Network
Student Academic Performance Predictive Model Based on Dual-stream Deep Network
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Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students'academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students'academic performance through learning behavior da-ta,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic per-formance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evalua-tion model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE)indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness,which provides a powerful help for the practical application of learning effect evaluation and prediction.
Blended teachingExpert perspective indicatorsTwo-stream information fusion model
Center for Biomedical-photonics and Molecular Imaging,Xi'an Key Laboratory of Intelligent Sensing and Regula-tion of trans-Scale Life Information,School of Life Science and Technology,Xidian University,Xi'an 710126,China
Engineering Research Center of Molecular and Neuro Imaging,Ministry of Education,Xi'an 710126,China
Innovation Center for Advanced Medical Imaging and Intelligent Medicine,Guangzhou Institute of Technology,Xi-dian University,Guangzhou 510550,China
National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Young Talent Program,Shaanxi Young Topnotch Talent ProgramKey Research and Development Program of ShaanxiXi'an Science and Technology ProjectFundamental Research Funds for Central Universities
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NSTL
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