A Smart Algorithm based Teaching Model for Optimizing Language Education Using PSO-DE Intelligence
Fei Li
International Journal of Computational Linguistics Research, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/ijclr/2025/16/4/137-145
Link: https://www.dline.info/jcl/fulltext/v16n4/jclv16n4_1.pdf
Abstract:
This paper proposes a smart algorithm based teaching model for language education, leveraging a hybrid
intelligent algorithm that combines Particle Swarm Optimization (PSO) and Differential Evolution (DE). The
model aims to enhance teaching effectiveness by optimizing classroom content and adapting to studentsâ€™
diverse learning needs. The study identifies 18 effective teaching behaviors through expert consultation and
applies the hybrid algorithm to analyze and improve linguistics instruction. Experimental results show the

hybrid PSO-DE algorithm outperforms traditional methods like genetic algorithms and ant colony optimiza-
tion in convergence speed and solution accuracy. The research highlights that expert teachersâ€™ strategies

follow a pyramid shaped effectiveness structure, emphasizing the importance of tailored, data driven in-
struction. Findings suggest that integrating adaptive algorithms can significantly boost learning efficiency,

student satisfaction, and overall educational quality in language teaching. The authors advocate for broader
adoption of such intelligent systems to support teacher development and modernize linguistics education in
the digital era.