基于大数据的创新创业课程教学效果评价模型分析

Diao Aijun
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引用次数: 2

摘要

为了提高创新创业课程的教学效果,通过对创新创业课程教学效果的分析,实现了对创新创业课程教学效果的定量评价,提出了一种基于大数据分析的创新创业课程教学效果评价方法。分析创新创业课程教学中存在的问题及对策。构建创新创业课程教学效果的大数据统计平均分析模型,结合样本回归分析法对创新创业课程教学效果进行高效数据分析,构建创新创业课程教学效果评价的决策目标函数。采用收敛规则评价的方法对创新创业课程教学效果进行定量回归分析,并结合描述性统计分析结果进行大数据特征提取和相关性描述。通过平均互信息聚类方法,将创新创业课程教学效果的统计特征应用到模式识别和特征选择中,实现基于大数据分析的创新创业课程教学效果评价。仿真结果表明,效果评估置信度高,评估结果准确可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis on the Evaluation Model of the Teaching Effect of Innovation and Entrepreneurship Course Based on Big Data
In order to improve that effect of the teaching of the innovation and start-up course, through the analysis of the effect of the teaching of the innovative start-up course, the quantitative evaluation of the effect on the teaching of the innovative start-up course is realized, and a method for evaluating the effect of the innovation and start-up course teaching based on the big data analysis is put forward. To analyze the problems and countermeasures of the teaching of the innovation and start-up course. a large-data statistical average analysis model of the effect of building an innovative entrepreneurship course teaching is constructed, a sample regression analysis method is combined to carry out the high-effect data analysis on the teaching of the innovative start-up course, and the decision-making objective function of the effect evaluation on the teaching of the innovative start-up course is constructed, The quantitative regression analysis of the effect of the innovative start-up course teaching is carried out by adopting the method of convergence rule evaluation, and the large-data feature extraction and the relevance description are carried out in combination with the descriptive statistical analysis result. The statistical feature of the effect of the innovative start-up course teaching is applied to the pattern recognition and feature selection by means of the average mutual information clustering method, and the effect evaluation of the innovation and start-up course teaching based on the big data analysis is realized. The simulation results show that the confidence level of the effect evaluation is high, and the evaluation result is accurate and reliable.
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