Abstract Students’ success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and increasing operational costs, academic institutions are paying more attention to sustaining students’ enrollment in their programs without compromising rigor and quality of education. With the scientific advancements in Big Data Analytics and Machine Learning, universities are increasingly relying on data to predict students’ performance. Many initiatives and research projects addressed the use of students’ behavioral and academic data to classify students and predict their future performance using advanced statistics and Machine Learning. To allow for early intervention, this paper proposes the use of Automated Machine Learning to enhance the accuracy of predicting student performance using data available prior to the start of the academic program.

中文翻译：

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增强对学生成功的预测：自动化机器学习方法

摘要 近年来，学生的成功已成为大多数高等教育机构的主要战略目标。随着预算的削减和运营成本的增加，学术机构越来越重视在不影响教育的严谨性和质量的情况下维持学生的入学率。随着大数据分析和机器学习的科学进步，大学越来越依赖数据来预测学生的表现。许多倡议和研究项目解决了使用学生的行为和学术数据对学生进行分类并使用高级统计和机器学习预测他们未来的表现。为了尽早干预，