An Educational Data Mining Model for Supervision of Network Learning Process

Jianhui Chen; Jing Zhao

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An Educational Data Mining Model for Supervision of Network Learning Process

机译：网络学习过程监督的教育数据挖掘模型

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To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.

机译：为了改善学校的教学计划，优化在线学习系统，并帮助学生获得更好的学习成果，建立了一个教育性的数据挖掘模型来监督电子学习过程。应用了数据挖掘技术，关联规则算法和聚类算法中的统计分析和可视化。系统分析了大学英语教学管理平台的教学数据。关于学生英语学习效果与网络学习习惯之间的关系得出了相关结论。结果表明，该方法可以有效地帮助教师判断学生的在线学习成绩，了解他们的在线学习状况，并改善他们的在线学习过程。因此，该模型可以提高学生在线学习的有效性。

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《International Journal of Emerging Technologies in Learning (iJET)》 |2018年第11期|共11页
作者
Jianhui Chen; Jing Zhao;
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正文语种
中图分类教育;
关键词
data miningstatistical analysis visualizationassociation rule algorithmclustering algorithm;

机译：数据挖掘统计分析可视化关联规则算法聚类算法;

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1. An Adaptive Educational Data Mining Technique for Mining Educational Data Models in Elearning Systems [J] . V. Murugananthan, B. L. ShivaKumar Indian Journal of Science and Technology . 2016,第3期

机译：用于学习系统中的教育数据模型的自适应教育数据挖掘技术
2. Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning [J] . Rebecca Deneckere, Charlotte Hug, Ghazaleh Khodabandelou, International journal of information system modeling and design . 2014,第4期

机译：有意过程挖掘：使用监督学习发现和建模过程背后的目标
3. A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks [J] . M R Narasinga Rao, Deepthi Gurram, Sai Mahathi Vadde, International Journal of Electrical and Computer Engineering . 2015,第5期

机译：基于神经网络的教育数据库观点挖掘预测模型
4. Enhancing educational data mining techniques on online educational resources with a semi-supervised learning approach [C] . Vincent Tam, Edmund Y. Lam, S. T. Fung, IEEE International Conference on Teaching, Assessment, and Learning for Engineering . 2015

机译：通过半监督学习方法增强在线教育资源上的教育数据挖掘技术
5. Data mining based learning algorithms for semi-supervised object identification and tracking. [D] . Dessauer, Michael P. 2011

机译：基于数据挖掘的学习算法，用于半监督对象识别和跟踪。
6. Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process [O] . Mohammad Farhadian, Seyed A. Rafat, Karim Hasanpur, 2018

机译：转录组学数据与监督机器学习模型相结合的跨物种荟萃分析可确定泌乳过程的共同基因特征
7. Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning [O] . Deneckère Rebecca, Hug Charlotte, Khodabandelou Ghazaleh, 2014

机译：故意过程挖掘：使用监督学习发现和建模过程背后的目标

An Educational Data Mining Model for Supervision of Network Learning Process

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