Research on Experimental Teaching of Retailing Course based on Learning Situation Analysis and Digital Literacy Theory

Abstract

In the context of the digital economy and the development of new quality productive forces, there is an urgent need for Retailing courses to transform from traditional theoretical instruction to fostering data-driven decision-making capabilities. This paper aims to explore the reform of experimental teaching in Retailing courses through learning situation analysis, integrating theories of digital literacy and autonomous learning. Utilizing primary data from pre-course surveys, and combining the "NSSE-China" (National Survey of Student Engagement) model with the EU DigComp framework, this study conducts an in-depth analysis of students' foundational cognition, tool proficiency, and learning expectations. The findings reveal that while students possess a certain awareness of "digital survival," there is a significant "skill-willingness mismatch" regarding data processing technologies, statistical analysis theories, and content creation literacy. Based on these insights, this paper proposes an AI-empowered, case-based, and progressive blended learning model for Retail, aiming to provide a reference for the digital transformation of business education.