基于准神经网络框架的高等工程教育质量模糊评价算法

Ya-Xin Zhou, Shiyuan Han, Jin Zhou, Kang Yao
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引用次数: 0

摘要

高等工程教育质量评价对工程人才的培养具有重要的指导意义和反馈作用。结合基于结果的教育(OBE)的教育核心概念和教育过程数据,从构建的准神经网络(QNN)框架出发,提出了一种面向工程教育的模糊质量评价算法。具体而言,考虑到工程教育全过程中各基本组成部分之间的逻辑关系,首先设计了一个四层QNN框架,将OBE的教育理念进行合理的底层实现,包括培养目标层、毕业需求能力层、毕业需求子能力层和课程层。然后,利用所提出的QNN框架下的教育过程数据,设计了描述工程教育目标能力成就尺度的模糊综合评价算法。最后,针对计算机科学相关四门课程的研究能力,基于过程教育数据集的实验证明了所提出的框架和算法的优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Quality Evaluation Algorithm for Higher Engineering Education Quality via Quasi-neural-network Framework
Quality evaluation for higher engineering education has important guiding significance and feedback role on cultivating engineering talents. Combining with the educational core concept of outcomes-based education (OBE) and the educational process data, a fuzzy quality evaluation algorithm is developed for engineering education deriving from a constructed Quasi-Neural-Network (QNN) framework. More specifically, considering the logical relationships among basic components in the whole process of engineering education, a four-layers QNN framework is designed first to underly and implement the educational concept of OBE reasonably, which includes the training objectives layer, requirement capability for graduation layer, requirement sub-capability for graduation layer, and course layer. After that, by employing the educational process data under the proposed QNN framework, a fuzzy comprehensive evaluation algorithm is designed to describe the achievement scale of target capability for engineering education. Finally, focusing on the research capability for computer science with related four courses, the experiments based on the process educational data sets show the superiority and efficiency of the proposed framework and algorithm.
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