为解决传统课堂教学评价主观性强、反馈滞后、维度单一的问题,并满足新工科教育个性化和能力导向的需求,构建一个全过程协同驱动的多模态智能课堂教学评价模型。该模型通过分布式传感网络采集视觉、听觉、操作、行为等数据,经预处理与特征提取后,依托动态分层和自适应融合框架实现了多源数据的协同分析。模型在Java 编程技术课程中开展实践,结果显示多模态融合模型识别准确率与教师人工评价具有高度一致性,可实现教学理论到实践的全维度评价,为新工科编程类课程智能评价提供新思路。

To address the issues of strong subjectivity, delayed feedback, and single dimension in traditional classroom teaching evaluation, and to meet the personalized and capability-oriented needs of new engineering education, this paper constructs a multi-modal intelligent classroom teaching evaluation model driven by whole-process collaboration. This model collects data from visual, auditory, operational, and behavioral aspects through a distributed sensing network. After preprocessing and feature extraction, it achieves collaborative analysis of multi-source data relying on a dynamic hierarchical and adaptive fusion framework. The model is practiced in the Java Programming Technology course, and the results show that the recognition accuracy of the multi-modal fusion model is highly consistent with teachers' manual evaluation. It can achieve full-dimensional evaluation from teaching theory to practice, providing new ideas for intelligent evaluation of programming courses in new engineering education.