Enhancing Practical Teaching of Applied Economics Through Web-Based Computational Algorithms

Abstract

In the new era, applied economics education is evolving with increasing emphasis on practical training. To cultivate high-quality talents equipped with both theoretical knowledge and practical skills, educational institutions must continuously improve their teaching strategies. This article analyzes the current state of applied economics education, identifies existing challenges, and highlights the importance of practical learning in enhancing students’ abilities and employability. It proposes strategies such as school-enterprise collaboration, simulation training, and real-world project integration. Moreover, advanced computational algorithms—such as machine learning and optimization techniques—are introduced into teaching practices, offering innovative tools that deepen students’ understanding of economic issues and improve problem-solving capabilities, particularly in data analysis, model building, and evidence-based decision-making.