This study provides a comprehensive review of the phenomenon of students dropping out from tertiary education. Student withdrawal is the result of a long decision‐making process and complex interaction between several determinants. We first provide an overview of definitions, theoretical models and perspectives of dropping out. Referring to previous theoretical and empirical evidence from a wide range of disciplines, we then focus on a detailed discussion of determinants affecting the decisions of students to drop out. There are three main reasons for students to leave the higher education system without a degree. These are 1) the national education system, e.g., the country’s financing policy, 2) the higher education institutions, e.g., the type of institution or teaching quality, and 3) the students themselves, with this last aspect subdivided into a) pre‐study determinants, such as the secondary school type, and b) study‐related aspects, such as working while studying. Based on these findings, we discuss the implications for further research, especially the application of modern data mining techniques on comprehensive data sets covering a wide range of relevant determinants which may lead to new insights into the dropping out process. The results will provide helpful tools for universities wishing to implement early warning systems and to support students at risk, at an early stage of their study.

中文翻译：

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辍学：文献综述

这项研究对学生退学的现象进行了全面的回顾。学生退学是一个漫长的决策过程以及几个决定因素之间复杂的相互作用的结果。我们首先提供定义，理论模型和辍学观点的概述。参考以前来自广泛学科的理论和经验证据，然后我们将重点讨论对影响学生退学决定的决定因素的详细讨论。学生离开大学而没有学位的主要原因有三个。它们是：1）国家教育体系，例如国家的资助政策； 2）高等教育机构，例如机构的类型或教学质量，以及3）学生本人，最后一个方面又细分为：a）研究前决定因素，例如中学类型； b）与学习有关的方面，例如在学习中工作。基于这些发现，我们讨论了进一步研究的意义，特别是现代数据挖掘技术在涵盖广泛相关决定因素的综合数据集上的应用，这可能会导致对辍学过程的新见解。研究结果将为希望在早期学习中实施预警系统并为处于危险中的学生提供支持的大学提供有用的工具。特别是将现代数据挖掘技术应用于涵盖广泛相关决定因素的综合数据集上，这可能会导致对辍学过程的新见解。研究结果将为希望在早期学习中实施预警系统并为处于危险中的学生提供支持的大学提供有用的工具。尤其是将现代数据挖掘技术应用于涵盖大量相关决定因素的综合数据集上，这可能会导致对辍学过程的新见解。研究结果将为希望在早期学习中实施预警系统并为处于危险中的学生提供支持的大学提供有用的工具。