Contextual Text Analytics for Knowledge Extraction in Biomedical Device Design Education

Contextual Text Analytics for Knowledge Extraction in Biomedical Device Design Education addresses the growing need to efficiently process vast amounts of unstructured textual data generated during education and research. Traditional manual methods struggle to extract meaningful information from complex biomedical design documents, student feedback, and literature, causing delays in curriculum improvement and innovation. To tackle this, the paper proposes a novel framework termed Contextual Biomedical Text Analytics (CBTA) that integrates natural language processing (NLP) techniques with domain-specific ontologies to enhance contextawareness in text processing. CBTA leverages semantic embedding, named entity recognition, and topic modeling to extract relevant knowledge, trends, and competency indicators from diverse textual datasets. Experimental validation on biomedical device design course materials demonstrates CBTA's enhanced ability to identify key concepts, design challenges, and learning gaps, outperforming baseline text mining techniques by 18% in precision and 15% in recall. The results indicate that CBTA facilitates actionable insights for educators, enabling targeted curriculum updates and personalized learning paths. In conclusion, CBTA presents an intelligent, scalable solution for advancing biomedical device design education through data-driven knowledge extraction, supporting both academic excellence and industry readiness.

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