Skip to main content

Research and Application of Capability Evaluation Model Based on BP Neural Network Educational Technology—Taking the Problem-Based Learning Teaching Process Learning as an Example

Buy Article:

$107.14 + tax (Refund Policy)

Since 1980s, the United States scientists Rumelhart and Williams proposed the error back propagation algorithm, and BP neural network was widely applied to the design of computer systems. Based on the BP neural network system, this paper designs the education technology capability evaluation model. The preliminary carries out investigation and data analysis of the school students based on the educational technology ability, the medium term carries out the learning diagnosis and evaluation based on the PBL teaching method, and the later stage establishes the evaluation model, test and evaluation, in order to promote the development of students’ educational technology ability.

Keywords: BP Neural Network; Evaluation; Model; PBL

Document Type: Research Article

Affiliations: China Yangtze Normal College of Education and Science, Chongqing, 408199, China

Publication date: 01 September 2016

More about this publication?
  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
  • Editorial Board
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content