人工智能技术在教学质量评价中的研究与实践  

Research and Practice of Artificial Intelligence in Teaching Quality Evaluation

作　　者：陈元电[1] 陈静[1] 徐胜[1] Chen Yuandian;Chen Jing;Xu Sheng(School of Physics and Optoelectronic Engineering,Guangdong University of Technology,Guangzhou,510006,China)

机构地区：[1]广东工业大学物理与光电工程学院,广东广州510006

出　　处：《中国现代教育装备》2024年第9期20-22,26,共4页China Modern Educational Equipment

基　　金：2021年广东省本科高校教学质量与教学改革工程建设项目“解决复杂工程问题能力的培养—电子专业实践课程产教协同教学改革实践”(编号:粤教高[2021]9号文);2021年广东工业大学高等教育研究基金“旨在提高解决复杂工程问题能力的产教融合育人模式的研究与实践”(编号:GXLX20210212)。

摘　　要：通过分析高校数字化转型背景下教学质量评价系统的重要性,指出了当前教学质量评价方法的不足,并提出解决问题的思路。基于深度学习等人工智能技术,开展课堂专注度检测系统设计,实现对学生面部表情、头部姿态的信息抓取,并采用模糊综合评价算法进行量化,得到学生课堂专注度指标。实践证明,该系统能有效检测学生的课堂专注度水平,为教学质量评价提供客观、准确的个性化数据。By analyzing the importance of teaching quality evaluation systems in the context of digital transformation in universities,this paper points out the shortcomings of current teaching quality evaluation methods and proposes solutions to these problems.Using artificial intelligence technologies such as deep learning,a classroom focus detection system is designed to capture and detect two-dimensional information composed of students'facial expressions and head postures.Fuzzy comprehensive evaluation algorithms are used to quantify student classroom focus indicators.Practice has proven that the system can effectively detect students'level of classroom focus and provide objective,accurate,and personalized data for teaching quality evaluation.

关 键 词：教学质量评价 数字化转型 课堂专注度 人工智能 模糊综合评价 

分 类 号：G434[文化科学—教育学]