基于BP神经网络的大规模在线教育学习者满意度测评模型构建

Construction of Learner Satisfaction Evaluation Model for Large Scale Online Education Based on BP Neural Network

为提升大规模在线教育学习者满意度测评效果,降低测评冗余值,构建了一种基于反向传播(Back Propagation,BP)神经网络的大规模在线教育学习者满意度测评模型。首先选取满意度测评指标,设定测评模型的路径系数图,其次估计路径系数图块结构参数,分析结构变量与观测变量间的关系,最后通过设计BP设计网络的结构并进行学习训练,实现大规模在线教育学习者满意度测评模型的构建。试验结果证明,构建模型能够取得更低的测评冗余值。

In order to improve the evaluation effect of learner satisfaction in large-scale online education and reduce the redundant value of evaluation,this paper constructs a large-scale online education learner satisfaction evaluation model based on Back Propagation(BP)neural network.First,select the satisfaction evaluation index,set the path coefficient map of the evaluation model,estimate the structural parameters of the path coefficient map,analyze the relationship between structural variables and observation variables,and achieve the construction of the large-scale online education learner satisfaction evaluation model by designing the structure of the BP design network and conducting learning training.The comparative test results show that the model can achieve lower evaluation redundancy value.