Deep Learning-Driven Optimization Strategies for Teaching Decisions in Smart Classrooms

Abstract

With the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.