基于集成学习技术的新冠肺炎影响的聚类学习结果预测模型

Wongpanya Sararat, Pratya Nuankaew
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引用次数: 0

摘要

新冠肺炎的影响导致了学习模式的突然改变。因此,本研究在Rajabhat Maha Sarakham大学研究了受新冠肺炎影响的在线学习模式对学习成绩的影响。这项研究有三个目标。第一个目标是研究Rajabhat Maha Sarakham大学受新冠肺炎影响的学习结果集群。第二个目标是使用机器学习和数据挖掘技术开发预测模型,用于对受新冠肺炎影响的学习结果进行聚类。第三个目标是评估Rajabhat Maha Sarakham大学受新冠肺炎影响的集群学习结果的预测模型。数据收集包括来自Rajabhat Maha Sarakham大学信息技术学院2020-2021学年两门课程的139名学生。研究工具包括学生教育信息、机器学习模型开发和基于数据挖掘的模型性能测试。研究结果揭示了使用教育数据挖掘技术发展学生关系的优势,这可以有效地管理在线模式下的高质量教学。研究中开发的模型具有较高的准确性。因此,机器学习技术的应用显然支持和促进了学习者素质的发展。
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Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques
The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.
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