Optimization of English Grammar Teaching Effect by Using Machine Learning Algorithm

Abstract

This study investigates the optimization of English grammar teaching effectiveness through the integration of machine learning algorithms into instructional methodologies. A mixed-methods approach was employed, encompassing quantitative analysis of student performance data and qualitative evaluation of educator perceptions. Participants were randomly assigned to either an experimental group, receiving instruction through a machine learning-powered adaptive platform, or a control group receiving traditional grammar instruction. Statistical analysis revealed a significant improvement in post-test scores among participants in the experimental group, indicating the efficacy of the adaptive platform in enhancing grammar proficiency levels. The machine learning algorithm employed in the platform demonstrated high accuracy and recall rates in identifying and addressing grammar errors in learner-generated texts. Ethical considerations surrounding data privacy and algorithmic bias were addressed through transparent reporting and adherence to ethical guidelines. The findings underscore the transformative potential of technology-enhanced pedagogical approaches in language education and highlight the importance of responsible implementation. Continued research and innovation in this area are essential to harness the full potential of machine learning in optimizing English grammar teaching effectiveness.