研究课堂教学质量准确评价问题.课堂教学质量评价是一个多层次、多目标优问题,系统非线性较强.传统线性评估方法不能反映课堂教学质量非线性变化关系,教学质量评价误差大.为提高课堂教学质量评价准确,提出一种基于云模型的课堂教学质量数据挖掘方法.采用支持向量机对课堂教学质量与评价指标间的非线性关系进行逼近,采用遗传算法进行支持向量机参数优化,并采用云模型对遗传算法进行改进,提高全局搜索能力,防止获得局部最优支持向量机参数.仿真结果表明,算法提高了课堂教学质量评价精度,能够有效发现课堂教学质量问题.%Quality assessment of classroom teaching is a multi - level, multi - objective optimization, and the traditional linear evaluation method can not reflect the nonlinear relation between the quality of classroom teaching. To improve assessment accuracy, the paper proposed a cloud model of classroom teaching quality based on data mining method. Support vector machine was used to approach the non - linear relationship between classroom teaching quality and evaluation indexes, a genetic algorithm was used for parameter optimization of support vector machine, and the cloud model of genetic algorithm was used to improve the global search ability to prevent accessing to local optimal parameters of support vector machine. The simulation results show that this algorithm can improve instruction quality evaluation accuracy and effectively find out the problem in classroom teaching quality.
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