高等职业教育质量智能评价：BP神经网络模型的构建与验证

• 作者： 张裕晨 ¹ 丁小 ¹ 邱均平 ^1，2
• 作者单位：
  1.  杭州电子科技大学中国科教评价研究院

  2.  杭州电子科技大学浙江高等教育研究院
• 通讯作者： 邱均平 
• 发布时间：2025-03-25

摘要: 【目的/意义］利用BP神经网络这一机器学习方法构建高等职业教育质量评价模型，旨在科学、客观、全面、智能地评价高等职业教育质量，为我国高等职业教育的建设和发展提供有力支持。［方法/过程］首先，基于BP神经网络，结合高等职业教育质量的内涵和特征，以利益相关者的角度从人才培养、院校基本办学条件、国际化发展、服务贡献和政策落实5个维度构建了一个包含14个二级指标和39个具体观测点的高等职业教育质量评价指标体系；其次，利用BP神经网络模型对随机抽取的100所“双高”院校的相关数据进行训练和测试，以验证BP神经网络模型在高等职业教育质量评价中的实际应用效果。【结果/结论］从评价方法的科学性、评价结果的合理性、评价体系的可用性三个角度对研究结果进行分析，发现BP神经网络模型在模拟和评估高等职业教育质量方面表现出较高的准确性和效率，为高等职业教育质量的评价提供了智能化的新思路新方法，为高职院校治理以及质量提升提供依据与支持，助推我国现代高等职业教育的高质量发展。

Abstract: [Purpose/Significance] Employing the Back Propagation (BP) neural network, a machine learning technique, to develop an evaluation model for higher vocational education quality seeks to offer a scientific, objective, comprehensive, and intelligent assessment. This endeavor aims to provide robust support for advancing and enhancing higher vocational education in China. [Method/Process] Initially, guided by the principles of the BP neural network and considering the unique features of higher vocational education quality, an evaluation index system was formulated from the stakeholders’ perspective. It encompasses five dimensions: student cultivation, basic conditions of institutions, international development, service contribution, and policy implementation. Comprising 14 secondary indicators and 39 specific observation points, this system was then utilized to train and test a BP neural network model with data from a random selection of 100 institutions, validating its practical application in evaluating higher vocational education quality.  [Results/Conclusions] An analysis of the research findings from three perspectives—the scientific nature of the evaluation method, the usability of the evaluation results, and the usability of the evaluation system—reveals that the BP neural network model demonstrates high accuracy and efficiency in simulating and assessing the quality of higher vocational education. It introduces a new, intelligent approach to evaluating the quality of higher vocational education, providing a basis and support for improving the educational quality of higher vocational institutions and promoting the high-quality development of modern higher vocational education in China.

• 来自： 丁小
• 分类： 教育学 >> 职业技术教育学
• 稿件状态： 已投稿期刊
• 引用： PSSXiv:202503.02906 (或此版本 PSSXiv:202503.02906V1)
  DOI:10.12451/202503.02906
  CSTR:32012.36.PSSXiv.202503.02906
• 推荐引用方式： 张裕晨,丁小,邱均平.高等职业教育质量智能评价：BP神经网络模型的构建与验证.哲学社会科学预印本平台:https://zsyyb.cn/abs/202503.02906.[PSSXiv:202503.02906V1] (点此复制)