Intelligent Psychology Teaching System Based on Adaptive Neural Network

Area of Science:

• Artificial Intelligence
• Cognitive Psychology
• Educational Technology

Background:

• Intelligent psychology systems require advanced computational models for understanding and simulating human cognition.
• Adaptive neural networks offer a flexible framework for modeling complex psychological processes.
• Existing learning methods need evaluation for their efficacy in educational psychology applications.

Purpose of the Study:

• To propose and analyze an adaptive neural network model for intelligent psychology systems.
• To compare the performance of the proposed model with the ICAP (Interactive, Constructive, Active, Passive) learning method.
• To investigate the impact of increasing interactive learning elements on network performance and classification accuracy.

Main Methods:

• The study introduces the basic structure of neural networks within a teaching system.
• It details a psychological teaching algorithm based on adaptive neural networks and four learning methods (P, A, C, I).
• Network models like GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2 were analyzed for their module configurations and performance.

Main Results:

• Advanced adaptive neural network models with more interactive learning elements demonstrated higher classification accuracy.
• The ICAP learning method showed a performance increase of 8%-10% in learning materials science texts.
• Increasing educational psychology learning elements in adaptive neural networks continuously improved network learning levels and classification accuracy.

Conclusions:

• Adaptive neural networks, particularly those with enhanced interactive learning elements, show significant potential for intelligent psychology systems.
• The ICAP learning method provides a measurable improvement in learning performance.
• Further integration of psychological learning elements into neural networks can lead to more sophisticated and accurate intelligent systems.