Interactive Design Model in Computer-Aided Environmental Teaching and Learning

This article is based on a clustering algorithm framework and conducts experimental verification on a student learning behaviour dataset in a computer-aided teaching environment.We have developed a training and testing algorithm framework specifically designed for analyzing student behaviour data, with the aim of exploring student behaviour patterns through clustering algorithms.During the training process, we utilized neural network convolution testing to evaluate the stability and effectiveness of the constructed model.In the analysis of experimental results, we paid special attention to the stability of the model and compared the accuracy and recall of neural time networks (networks used to capture time series data) and classical clustering algorithms in identifying student behaviour patterns.The experimental results show that the algorithm model proposed in this paper exhibits more significant advantages in identifying student behaviour recall compared to traditional clustering methods while maintaining efficient computational speed.