Microlearning: Experience of teaching at a Chinese university

Purpose. Responding to the demands of an increasingly digitized and knowledge-driven era, microlearning has become a crucial educational methodology, facilitating adaptable and accessible learning experiences. The purpose of this article is to share the experience of implementing different technologies to design and deliver microlearning content to the students at Shangqiu Normal University (China) to demonstrate how microlearning can be used effectively in the classroom at tertiary level. Methodology. The study employed a mixed-methods approach, combining a theoretical analysis (analysis, synthesis, comparison) of literature on microlearning and pedagogical practices with empirical data gathered through direct observation and structured discussions to understand the implementation and impact of microlearning on Chinese university students. Results. In response to the temporal constraints of a ‘Teaching Methods of English’ course at Shangqiu Normal University, microlearning was implemented, utilizing short, interactive video lectures and modularized traditional materials. Interactive elements and assessments were integrated to activate prior knowledge, proving effective for Chinese students, who were motivated by incentives to actively participate. WeChat facilitated ubiquitous access to micro-lectures, and gamification enhanced engagement. To mitigate knowledge fragmentation, microlearning was combined with traditional methods. Technical support ensured seamless implementation. Future developments in AI and gamification promise to further enhance the efficacy of microlearning. Conclusions. This article demonstrates the effective implementation of microlearning at a Chinese university, utilizing diverse technologies to enhance student engagement and knowledge retention. Recommendations include integrating micro-lectures, leveraging mobile platforms, and incorporating gamification, while addressing cultural reticence and ensuring technical support. Future research should explore longitudinal impacts and AI integration.