Intelligent optimization algorithm-driven informational teaching model for English reading and writing in universities.

This article focuses on improving college students' English reading and writing abilities through an enhanced teaching-learning optimization algorithm. It introduces the M-TLBO (multi-learning teaching learning-based optimization) algorithm, which addresses limitations of the original TLBO (teaching learning-based optimization) algorithm—such as singular teaching ability and premature convergence to local optima—by incorporating multiple teachers, adaptive teaching factors, reverse learning, and stochastic variation strategies. Comparative analysis shows that M-TLBO outperforms other TLBO variants in convergence speed, accuracy, and stability. Empirical evaluation of the English literacy information-based teaching model using M-TLBO demonstrated significant improvements in students' writing performance across five dimensions, with post-test scores increasing by an average of 5.05 points and statistically significant differences observed. The study notes that further large-scale experimental validation is needed to confirm these findings.