利用机器学习技术进行虚拟学习的学生分析

Neha Singh, U. C. Jaiswal
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摘要

自新冠肺炎疫情发布以来,在线教育成为人们关注的焦点。教育绩效分析是虚拟教室和各种学术机构的中心话题。本研究使用LogitBoost、Logistic Regression、J48、OneR、Multilayer Perceptron和朴素贝叶斯(Naive Bayes)等多种机器学习分类器分析了学生在虚拟学习中的学习情况,以找到产生最佳结果的理想分类器。本研究基于召回率、精确度和f-measure来评估算法,以确定其有效性。因此,作者试图通过采用两种不同的测试模型:使用训练集和10交叉折叠模型,对本研究中的算法进行比较分析。研究结果表明,训练集模型优于10交叉折叠模型。研究结果表明,利用使用训练集模型的多层感知器分类器在预测虚拟学习中的学生学习方面表现得更好。
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Analysis of Student Study of Virtual Learning Using Machine Learning Techniques
Since COVID-19 was released, online education has taken center stage. Educational performance analysis is a central topic in virtual classrooms and across the spectrum of academic institutions. This research analyzed students' studies in virtual learning using many machine-learning classifiers, which include LogitBoost, Logistic Regression, J48, OneR, Multilayer Perceptron, and Naive Bayes, to find the ideal one that produces the best outcomes. This research evaluates algorithms based on recall, precision, and f-measure to determine their efficacy. Accordingly, the authors try to perform a comparative analysis of the algorithms in this research by employing two distinct test models: the use of training sets and the 10 cross-fold models. The research results demonstrate that the training set model outperforms the 10 cross-fold model. The findings demonstrate that the multilayer perceptron classifier utilizing the use training set model performs much better in terms of predicting student study in virtual learning.
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