A Practical Study on the Integration of Learner Profiling and Teachers' Digital Literacy in Foreign Language Classrooms Driven by Big Data

Abstract

The development of big data technology provides new opportunities for the development of education and gives new connotations and missions to precision teaching, and the integration of learner portraits and teachers' digital literacy practices in foreign language classrooms is now studied using college English classroom teaching as an example. Based on the learning data in the foreign language classroom, the study constructs individual portraits of learners in the foreign language classroom in terms of learner styles, learner behaviors and learner outcomes, and generates group portraits by using the K-means algorithm based on Canopy and maximum-minimum distance to output the learner portraits in the foreign language classroom. The results of the learning portraits are then embedded into the teaching process, realizing the accurate teaching practice that is organically integrated with teachers' digital literacy. The foreign language classroom presents four types of learners: "potential learners", "excellent learners", "borderline learners" and "striving learners", with their respective proportions being 46.00%, 19.80%, 13.40% and 20.80%. Teaching practice shows that the method not only improves students' learning ability and interest in learning, but also improves students' innovation and thinking ability.