A Data-Driven Framework for Metaverse-Based Chinese Learning System Design Based on Knowledge Flow and Open Innovation

Abstract

The emergence and fast growth of metaverse technologies have brought new possibilities to the field of immersive and interactive learning system architectures. Nonetheless, the current research is mainly concerned with the technological affordances and learning outcomes, and less emphasis is placed on the systematic organization of learning spaces in the metaverse. A solution to the gap is provided by this study which develops a data-driven framework of metaverse learning space design depending on knowledge flow and open innovation. It is a multi-stage methodology that combines practitioner knowledge, expert validation, and large scale data generated by users. In the first place, more than 11,000 records were gathered through Python-based web scraping of online platforms to find out patterns of learning activities. Secondly, five Chinese language teachers took part in structured discussions to interpret and refine these patterns into pedagogically meaningful categories. Lastly, expert validation was performed to confirm conceptual consistency. The knowledge management lifecycle, which was expanded to cover knowledge storage, was taken as an analytical framework. The findings indicate that knowledge acquisition (0.345) and practice (0.244) are prevalent in modern learning system architectures, whereas knowledge storage (0.170) is an important factor, and knowledge creation (0.060) is not well represented. These results led to a functional architecture of metaverse learning system architectures being created, which included the lecture, activity, materials storage, and assessment functions. Both theory and practice are enriched by this study because it (1) expands the knowledge management lifecycle, (2) introduces a quantitative modeling approach to the concept of knowledge flow, and (3) provides a systematic way of converting knowledge processes into learning space design. The suggested framework will provide a scalable solution to creating adaptive and knowledge-based metaverse learning system architectures.