A Practical Study on Designing Problem Chains for Primary School Chinese Reading Classes from the Perspective of Deep Learning

Abstract

To address the prevalent challenges of "superficiality, fragmentation, and teacher dominance" in current elementary Chinese
reading instruction, this study is guided by the [1] core principles of the "Compulsory Education Chinese [2] Curriculum Standards (2022
Edition)". By integrating deep learning theory with Bloom's Taxonomy of Cognitive Objectives, and employing literature review, action
research, and case analysis, we systematically explore a three-dimensional linkage framework connecting "academic stages, literary genres, and cognitive development". Practical data shows that after implementation in six pilot classes across three primary schools in Xi' an,
student participation rates increased by 35%, text analysis scores improved by 22%, and core competency indicators demonstrated significant enhancement.