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首页> 外文会议>IEEE International Conference on Automation/Congress of the Chilean Association of Automatic Control >A causal modelling for desertion and graduation prediction using Bayesian networks: a Chilean case
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A causal modelling for desertion and graduation prediction using Bayesian networks: a Chilean case

机译：贝叶斯网络遗弃和毕业预测的因果建模：智利案

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Currently the high rates of university dropouts and low graduation are social problems that are very relevant in Chilean society. Predicting these events can allow institutions to take action to avoid them. The typical prediction models based on machine learning are capable of making reliable predictions, however they do not allow to understand the causality that originates both events, which could help to take better actions. This work proposes to find, analyze and weigh the causal relationships that allow predicting whether a student will drop out or will graduate according to the information available using a framework with Bayesian networks. The study is based on real data from the Universidad Católica de Temuco in Chile collected over three years. The results reveal variables and relevant relationships according the opinion of human experts, which suggest that the proposed model provides better capabilities to represent the causality of university dropout and graduation. From the results we believe that it is feasible to design better retention policies and timely degree at a university.
机译：目前大学辍学率高的高率和低毕业是在智利社会中非常相关的社会问题。预测这些事件可以允许机构采取行动以避免它们。基于机器学习的典型预测模型能够进行可靠的预测，但是它们不允许了解发起两个事件的因果关系，这可以有助于采取更好的行动。这项工作建议发现，分析和权衡允许预测学生是否会辍学的因果关系，或者根据使用与贝叶斯网络的框架提供的信息毕业。该研究基于智利大学Católicade Temuco的真实数据，三年多。结果揭示了人类专家意见的变量和相关关系，这表明拟议的模式提供了更好的能力来代表大学辍学和毕业的因果关系。从结果来看，我们认为在大学设计更好的保留政策和及时学位是可行的。

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