Development of Educational Resources and Personalised Learning Support Strategies for Biotechnology English Language Education in Multimedia Interactive Mode

Abstract

The research aims to solve the dual challenges of static resources and lack of personalised support in biotechnology English education by realising dynamic adaptation of educational resources and precise regulation of cognitive processes through multimodal biometrics. The research designs a multi-layer architecture integrating eye tracking, haptic feedback and speech stress analysis, and develops a dynamic resource generation algorithm based on federated learning and a reinforcement learning-driven policy engine. Experimental data show that in a controlled experiment at MIT's Department of Bioengineering, the system resulted in a 37.2% increase in gene editing technology terminology mastery (p<0.001), a protein engineering English writing sentence diversity index (MTLD) of 73.4 (51.2 for the control group), a reduction in operational errors to 7.2% (23.8% for the control group), and a 56% increase in cognitive recovery efficiency. The system deployment resulted in a 37% reduction in average annual classroom demand at MIT and a 2.3-year payback cycle for equipment investment. It provides a replicable path for the digital transformation of education, promotes the deep integration of biometrics in the education-research-industry chain, and forms an interdisciplinary solution with academic rigour and engineering practicality.