Research on evaluation of MOOC distance learning effect based on a BP neural network.

Citation metadata

Authors: Jiefeng Wang and Henry Loghej
Date: June 28, 2022
Publisher: Inderscience Publishers Ltd.
Document Type: Brief article
Length: 140 words

Document controls

Main content

Abstract :

At present, the evaluation method of the massive open online course (MOOC) learning effect has the problems of large evaluation error and low evaluation efficiency. Therefore, this paper proposes an evaluation method based on a back propagation (BP) neural network. We select the evaluation index, and use the grey correlation analysis method to optimise the evaluation index, then use the entropy weight method to calculate the index weight. The BP neural network model is constructed, which is used as the evaluator of the MOOC distance learning effect. The sample data to be identified is input to minimise the accumulated evaluation residual, and the output is the evaluation result of the MOOC distance learning effect. After testing, the error rate of the design method is only 0.9259%, and the evaluation time is always less than 1. Byline: Jiefeng Wang, Henry Loghej

Source Citation

Source Citation   

Gale Document Number: GALE|A709927471