Title: Learning effects evaluation of the WPBL based on BP neural networks model
Abstract:Objective To study the applicability of BP neural networks model in the Learning effects evaluation of the WPBL. Methods In this study, based on the data collected through questionnaires, we built the...Objective To study the applicability of BP neural networks model in the Learning effects evaluation of the WPBL. Methods In this study, based on the data collected through questionnaires, we built the BP neural network model according the test samples and measured training samples according the model. Results The average error between the network output scores and the composite scores when using the test samples to build the model is less than the specified error ( E=0. 000031592〈0. 0001). For the training samples, the average error between the network output scores and the composite scores very small. Conclusions BP neural network model can evaluate the WPBL learning effects accurately and quickly.
Key words:
Error back propagation algorithm(BP) ; Neural networks model; Web problem-basedlearning; Learning effects evaluationRead More
Publication Year: 2014
Publication Date: 2014-02-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot