Digital Media Teaching and Effectiveness Evaluation Integrating Big Data and Artificial Intelligence.

Area of Science:

• Educational Technology
• Artificial Intelligence
• Data Science

Background:

• Digital media technology is increasingly integrated into teaching and learning.
• Effective evaluation of digital media's impact on learning outcomes is crucial for educators.
• Current evaluation methods may lack the sophistication to capture the nuances of digital media effectiveness.

Purpose of the Study:

• To propose and validate a novel method for evaluating digital media teaching effectiveness.
• To leverage artificial intelligence and big data for a more scientific and reasonable assessment.
• To provide educators with a tool to optimize the use of digital media in instruction.

Main Methods:

• Development of an evaluation framework using a Radial Basis Function (RBF) neural network model.
• Utilizing big data analytics and artificial intelligence for model training and validation.
• Input variables included digital media teaching effectiveness metrics; output variables represented the degree of effectiveness.

Main Results:

• The proposed RBF neural network model exhibited strong generalization and extension capabilities.
• The model effectively evaluated the effectiveness of digital media in teaching contexts.
• The study provides a new, data-driven pathway for assessing educational technology impact.

Conclusions:

• The RBF neural network model offers a robust and adaptable solution for evaluating digital media teaching effectiveness.
• This approach facilitates informed decision-making for selecting and implementing digital media tools in education.
• The findings contribute to the advancement of objective and precise educational technology assessment.