基于主成分分析和支持向量机的《数据结构》课程教学质量评价

Xu Xin-ai, Wang Li-na, Qi Chun-ying
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引用次数: 1

摘要

为了获得更高精度的“数据结构”教学质量评价结果,根据“数据结构”教学质量评价的特点。设计了基于主成分分析与支持向量机相结合的“数据结构”教学质量评价模型,并应用于具体实例。实验结果证实,所建立的模型在“数据结构”教学质量评价层面获得了高精度的评价结果,可为完善“数据结构”及相关课程的教学质量评价提供有价值的信息。
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Teaching Quality Evaluation of “Data Structure” Courses Based on Principal Component Analysis and Support Vector Machine
In order to obtain a higher-precision “data structure” teaching quality evaluation result, according to the characteristics of the “data structure” teaching quality evaluation. A “data structure” teaching quality evaluation model based on the combination of principal component analysis and support vector machine is designed and applied to specific examples. The experimental results confirm that the established model has obtained high-precision evaluation results in the “data structure” teaching quality evaluation level, which can provide valuable information for improving the teaching quality evaluation of “data structure” and related courses.
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