大数据在高职创业创新教育中的应用

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Information Technology and Web Engineering Pub Date : 2023-11-21 DOI:10.4018/ijitwe.333898

Long Chen, Jiang He

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引用次数: 0

摘要

传统网格没有考虑创新创业教育大数据的动态特性。基于自适应识别加权算法的网格化人工智能教学信息量化分析评价模型逐步应用于创新创业教育的日常操作系统中。本文研究了自适应识别加权算法在国内高职院校创新创业教育网格化分析中的应用，提出了基于自适应识别加权算法的网格化分析人工智能教学模型和高校创新创业教育智能在线分析方法。结果表明，基于网格分析网络教学和自适应识别加权算法的高校创新创业教育模式能够高效、智能地诊断学生的教学数据，实现高校大数据分析技术的创新。

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Application of Big Data in Entrepreneurship and Innovation Education for Higher Vocational Teaching

The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.

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来源期刊

International Journal of Information Technology and Web Engineering COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.60

自引率

0.00%

发文量

期刊介绍： Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.