Article Gold Open Access 2025

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

International Journal of Web-Based Learning and Teaching Technologies
Journal · Vol. 20 · Issue 1
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. © 2025 IGI Global. All rights reserved.

Keywords

Author Keywords

Algorithm Applied Economics Computer Course Practice Intelligence Practical Teaching

Index Keywords

E-learning Learning systems Teaching Curricula Personnel training Decision making Education computing Students Problem solving Machine learning economics Learning algorithms intelligence High quality Educational institutions Practical training Practical skill Applied economic Computational algorithm Course practices Practical teachings Web based
Author Affiliations
Sichuan Technology and Business University, Sichuan, China
Funding & Acknowledgements
No funding information
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