Analysis and Optimization Strategy of OBE-CDIO Talent Training Quality Based on Data Mining

With the comprehensive arrival of the information age, big data technology has demonstrated enormous potential and value in various industries, driving profound changes in various fields. At the same time, the rapid development of technology has posed new challenges and higher requirements for the quality analysis of talent cultivation in the new era. To address these challenges, this article proposes an OBE-CDIO talent training quality analysis and optimization strategy based on data mining (DM). This strategy deeply integrates DM technology with Outcome Based Education (OBE) and CDIO (Conceptual Design Implementation Operate) educational concepts, aiming to comprehensively and deeply explore key information in the talent cultivation process through advanced data analysis methods, and provide scientific basis for improving the quality of talent cultivation. In the process of implementing the strategy, we fully utilized advanced technologies such as machine learning (ML) to conduct detailed analysis of various aspects of talent cultivation, in order to discover potential problems and room for improvement. The experimental results show that through in-depth mining and analysis of a large amount of data, we can not only accurately identify the key factors affecting the quality of talent cultivation, but also provide strong data support for teaching improvement and curriculum optimization.