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Supervised Machine Learning Model-Based Approach for Performance Prediction of Students

机译:基于机器学习模型的学生性能预测方法

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Predicting students’ performance is one of the crucial issue for learning contexts, since it helps to develop alternative recommendation systems for academically weak students. Thus, several methods and practices have been applied for educational improvement. However, most of the existing methods do not determine the performance of the students. In this study, we have studied the execution of six machine learning models (Decision tree, Random Forest, Support Vector Machine, Logistic Regression, Ada Boost, Stochastic Gradient Descent) to analyze and evaluate the students’ achievements. The performance is evaluated in term of accuracy, precision, sensitivity and f-measure. Among the selected models, the results validate that the efficiency of Stochastic Gradient Descent is better in training small dataset. In addition, it also produces the higher accuracy as compared with other models. This contribution aims to develop the best model which may derive the conclusion on students' academic achievement.
机译:预测学生和rsquo;性能是学习背景的关键问题之一,因为它有助于为学习弱者制定替代推荐系统。因此,已申请了若干方法和实践用于教育改进。但是,大多数现有方法都不确定学生的表现。在这项研究中,我们研究了六种机器学习模型(决策树,随机森林,支持向量机,Logistic回归,ADA Boost,随机梯度下降)的执行,以分析和评估学生’成就。在精度,精度,灵敏度和F测量的术语中评估性能。在所选模型中,结果验证了随机梯度下降的效率更好地训练小型数据集。此外,与其他模型相比,它还产生更高的精度。这一贡献旨在开发最佳模型,可能导致学生的结论'学术成就。

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