Positioning and Path Exploration of Precision Teaching for English Practical Ability

Abstract

Abstract In the era of big data in education, the application of campus data mining has become a new challenge for the reform and innovation of education and teaching modes in colleges and universities. The study improves the assessment indexes for English practical ability and develops a learner portrait model that is geared towards English practical ability. After obtaining the relevant data of English majors in a university, data mining algorithms such as K-means and Apriori are applied to construct the learner portrait, outputting the behavioral and outcome characteristics of different groups of students. On this basis, we design precise intervention teaching for students with different profiles, analyze the effect analysis of precise teaching from the perspectives of learning behaviors and learning outcomes, and further summarize the generation path of precise teaching oriented to English practical ability. The sample students are divided into three categories: excellent learners (25%), striving learners (20%), and marginal learners (55%). Precision instruction enabled students to promote participation, interaction, and concentration in the classroom and resulted in an 8% to 13% increase in English learning outcomes. Practical English proficiency teaching that is precision-oriented can effectively improve the quality and efficiency of teaching and promote the improvement of students’ English proficiency.