基于大数据模糊k均值聚类的英语教学能力评价算法

Q2 Social Sciences
Lili Qin, Weixuan Zhong, Hugh C. Davis
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引用次数: 0

摘要

针对传统英语教学能力评价算法中大数据信息分类不准确的问题,本文提出了一种基于大数据模糊k均值聚类的英语教学能力评价算法。首先,建立了约束参数指标分析模型。其次,采用定量递归分析对大数据信息模型的能力进行评价,实现能力约束特征信息的熵特征提取;最后,结合大数据信息融合和K-means聚类算法,实现英语教学能力指标参数的聚类和整合,制定相应的教学资源分配方案,对英语教学能力进行评估。实验结果表明,用该方法评价英语教学能力具有良好的信息融合分析能力,提高了教学能力评价的准确性和教学资源应用的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering
In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly, quantitative recursive analysis is used to evaluate the capabilities of big data information models and achieve entropy feature extraction of capability constrained feature information. Finally, by integrating big data information fusion and K-means clustering algorithm, the article achieves clustering and integration of indicator parameters for English teaching ability, prepares corresponding teaching resource allocation plans, and evaluates English teaching ability. The experimental results show that using this method to evaluate English teaching ability has good information fusion analysis ability and improves the accuracy of teaching ability evaluation and the efficiency of teaching resource application.
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CiteScore
2.40
自引率
0.00%
发文量
68
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