Constructing a Data-driven In-depth Interaction Model for English Language Teaching and Learning in Large Classes Targeting Whole Person Development

Abstract

The inherent limitations of large-class English instruction frequently lead to a decrement in instructional efficacy, particularly when contrasted with smaller class environments. This study introduces a novel pedagogical framework, termed the Data-Driven In-Depth Interaction Model for English Language Teaching and Learning (ELTL), tailored for large-class settings and aimed at promoting comprehensive personal development in the era of big data. The model, grounded in data collection, analysis, and mining, was implemented in a classroom teaching practice, yielding significantly superior outcomes for the experimental group in comparison to the control group. The advent of the big data era necessitates a pedagogical paradigm shift from one-dimensional to three-dimensional instruction, a redefinition of the teacher's role from a traditional lecturer to an integrative facilitator, and a marked enhancement in teacher data literacy, evolving from passive data acquisition to proactive data-driven decision-making.