大数据驱动的计算机网络远程教学质量评价研究

Research on computer network distance teaching quality evaluationbased on big data driving

计算机实验教学效果评价研究一直是高校实验教学的一个重要研究内容,针对传统方法存在的计算机实验教学效果评价误差大等难题,为提高计算机实验教学效果评价的准确度,设计一种大数据分析技术的计算机实验教学效果评价方法。首先,分析当前计算机实验教学效果评价的研究进展,并采集实验教学效果的相关数据集,建立计算机实验教学效果评价指标;然后,引入层次分析建立层次结构的计算机实验教学效果评价指标体系,确定每一个指标权重,并进行一致性检验,从而实现计算机实验教学效果的评价;最后,选择具体计算机实验教学作为测试对象进行仿真实验。结果表明,相比于其他方法,所提方法的计算机实验教学效果评价精度得到了明显的提升,满足现代计算机实验教学的发展要求,具有更高的实际应用价值。

The evaluation of computer experimental teaching effect has always been an important research content of university experimental teaching.Since the traditional methods have large errors in the computer experimental teaching effect evaluation,a computer experimental teaching effect evaluation method based on big data analysis technology is designed to improve the evaluation accuracy.The research progress for the current computer experimental teaching effect evaluation is analyzed.In the method,the relevant data sets of experimental teaching effect are collected to establish the evaluation index of computer experimental teaching effect,and then,the AHP is introduced to establish the computer experimental teaching effect evaluation index system with the hierarchical structure,the weight of each index is determined,and the consistency test is implemented,so as to realize the evaluation of computer experimental teaching effect.Some specific cases of computer experimental teaching are taken as the test objects in the simulation experiments.The results show that,in comparison with that of the other methods,the evaluation accuracy of computer experimental teaching effect of the designed method has been significantly improved.Therefore,the method can meet the development requirements of modern computer experimental teaching and has a higher practical application value.