Real Challenges and Optimization Strategies for Teacher-Student Verbal Interaction in University Classrooms from the Perspective of Deep Learning

Abstract

This study investigates the real challenges and optimization strategies associated with teacher-student verbal interaction in university classrooms from the perspective of deep learning. A questionnaire survey was administered to both faculty members and students across multiple higher education institutions, yielding 652 valid teacher responses and 707 valid student responses. The dual-perspective design enables an objective and comprehensive examination of actual classroom verbal interaction patterns. The findings reveal that although the majority of teachers demonstrate a solid understanding of deep learning theory, a notable disconnect persists between their classroom dialogue practices and intended educational objectives. Students generally express a preference for critical thinking-oriented classroom interactions; however, they encounter significant barriers to deep engagement stemming from a combination of subjective and objective factors. Both groups exhibit objective biases in their perceptions of classroom interaction quality. Common challenges identified include the dominance of surface-level question-and-answer exchanges, a lack of tiered instructional design, and insufficient institutional supporting mechanisms. Building on the quantitative questionnaire data, the study proposes improvement strategies across three dimensions: classroom discourse construction, teacher-student collaborative development, and institutional support systems. These strategies are designed to balance theoretical rigor with the practical realities of university teaching. The recommendations form a layered, streamlined, and step-by-step implementation plan, providing empirical evidence and actionable guidance for enhancing the quality of undergraduate classroom instruction in the context of deep learning.