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The Realization of Intelligent Algorithm of Knowledge Point Association Analysis in English Diagnostic Practice System

机译:英语诊断实践系统知识点关联分析智能算法的实现

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This paper first conducts knowledge point association analysis on a large amount of data collected in practical applications. Data mining includes data collection, data preprocessing, actual mining, and result analysis, establishes knowledge point association rules table, and develops college English diagnostic practice system. Then, starting from the existing paper composition mode of the system, the knowledge point association rule table is introduced, and the knowledge point association relationship mining model is constructed using the association rule algorithm to explore the potential influence relationship between different knowledge points that affect the improvement of learning quality. Finally, the data collected when the system is used is preprocessed, and the three dimensions of learning status evaluation, question-type association analysis, and college English score prediction are, respectively, modeled. Finally, after combining these submodels, a relatively complete and reliable diagnosis is obtained by evaluation model and related verification.
机译:本文首先对实际应用中收集的大量数据进行知识点关联分析。数据挖掘包括数据收集,数据预处理,实际挖掘和结果分析,建立知识点关联规则表,并开发大学英语诊断实践系统。然后,从系统的现有纸张组成模式开始,介绍了知识点关联规则表,并且使用关联规则算法构造了知识点关联关系挖掘模型来探讨影响的不同知识点之间的潜在影响关系。改善学习质量。最后,在使用系统时收集的数据是预处理的,并且分别建模了学习状态评估,问题类型关联分析和大学英语分数预测的三个维度。最后,在组合这些子模型之后,通过评估模型和相关验证获得相对完整和可靠的诊断。

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