Enhancing knowledge transformation in digital education: an analysis of the SECI model’s application in course design and execution

Abstract

Abstract This study investigates the application of the SECI model of knowledge dimensions in the design and execution of educational courses in “Innovation and Digitization Management” and “Data-Based Decision Making” microdegrees, developed within the rapidly evolving educational landscape of 2023 and 2024. The research incorporates a diverse sample of learners across multiple demographics to enhance generalizability. Leveraging natural language processing (NLP) and text analysis, the study explores patterns in course content that correlate with positive learner feedback and effective knowledge transformation. The methodology, combining both quantitative and qualitative approaches, includes data preprocessing, tokenization, vectorization, and clustering to compare and contrast course elements with learner feedback systematically. Preliminary findings indicate that the integration of the SECI model, emphasizing real-time content sharing and example-based learning, significantly enhances the transfer of knowledge from tacit to explicit forms. This research aims to identify a replicable cadence in content preparation that optimizes learning outcomes, applicable to a broad range of educational contexts including data-related courses, innovation, digital transformation or knowledge-management topics.

Keywords

• SECI model
• Knowledge transformation
• Digital education
• Course design
• Natural language processing
• Tacit knowledge