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A Support Vector Regression-Based Prediction of Students' School Performance

机译:基于支持向量回归的学生学习成绩预测

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The relationship between a person¡¦s personality and performance has long been studied by psychologists. Research suggests that a person¡¦s performance and behavior are related to personality characteristics and background data to a certain degree. In this paper, the Big Five personality model is adopted for measuring profiles of students, whose undergraduate performance and behavior are then analyzed. A machine learning approach, support vector regression (SVR), is employed to find correlations from the given sample data. The performance and behavior of a person are predicted from the obtained regression values. Personality, biological, performance, and behavior data of 120 undergraduates in Taiwan were collected through questionnaires. Ninety valid data samples are used for training in SVR and the others are used for evaluating the regression predictions. Most of the predicted performance yielded near 80% accuracy. It is shown that there are correlations between a person¡¦s performance and personality characteristics. SVR is shown to be a suitable method for exploring personality correlations.
机译:长期以来,心理学家一直在研究一个人的性格与表现之间的关系。研究表明,一个人的表现和行为在一定程度上与人格特征和背景数据有关。本文采用大五人格模型对学生的档案进行测量,然后分析其本科生的表现和行为。使用机器学习方法,即支持向量回归(SVR),从给定的样本数据中找到相关性。根据获得的回归值预测一个人的表现和行为。通过问卷调查收集了台湾120名大学生的人格,生物学,表现和行为数据。九十个有效数据样本用于SVR训练,其他样本用于评估回归预测。大部分预测性能产生了接近80%的准确度。结果表明,一个人的绩效与人格特征之间存在相关性。 SVR被证明是探索人格相关性的合适方法。

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