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首页> 外文会议>Mexican international conference on artificial intelligence >Learning Models for Student Performance Prediction
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Learning Models for Student Performance Prediction

机译：学生表现预测的学习模型

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Predicting student performance supports educational decision-making by allowing directives and teachers to detect students in special situations (e.g. students at risk of failing a course or dropping out of school) and manage these in a timely manner. The problem we address consists of grade prediction for the courses of a given academic period. We propose to learn a predictive model for each course. Two cases can be distinguished: historical grades are unavailable for prediction (first semester) and historical grades are available. For the first case, features that include selection test scores, socioeconomic information, and middle school the student comes from are proposed. For the second case, features that include past grades from similar courses are proposed. To test our approach, we gathered data from a Mexican public high school (three generations, 2,000 students, four semesters, and 24 courses). Our results indicate that features such as numerical ability, family, motivation, and social sciences are relevant for prediction without historical grades, while grades from the immediate previous semester are relevant for prediction with historical grades. Additionally, support vector machines and linear regression are suitable techniques for tackling grade prediction.
机译：预测学生表现可以通过指导和老师发现处于特殊情况下的学生（例如，有可能无法参加课程或辍学的学生）并及时进行管理来支持教育决策。我们解决的问题包括给定学期课程的成绩预测。我们建议为每门课程学习一个预测模型。可以区分两种情况：历史成绩不可用于预测（第一学期），历史成绩可用。对于第一种情况，提出了一些功能，包括选拔考试成绩，社会经济信息和学生来自的中学。对于第二种情况，提出了包括类似课程的过去成绩在内的功能。为了测试我们的方法，我们从墨西哥一所公立高中（三代人，2,000名学生，四个学期和24门课程）收集了数据。我们的结果表明，数字能力，家庭，动机和社会科学等特征与没有历史成绩的预测相关，而前一学期的成绩与历史成绩的预测相关。另外，支持向量机和线性回归是用于处理等级预测的合适技术。

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