This study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor’s degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features’ selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path dimensions. We proposed and tested three distinct course stage data models based on entrance date, end of the first and second curricular semesters. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. The previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predictive power. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success.

这项研究提出了一种数据挖掘方法，以预测一年级学生的学业成就。葡萄牙高等学校一年制学士学位的10个学年的数据集（N = 9652）已被分析。对特征的选择导致了包含68个特征的特征集，包括社会人口统计学，社会出身，以前的教育，特殊法规和教育路径维度。我们根据入学日期，第一和第二学期的结束，提出并测试了三个不同的课程阶段数据模型。支持向量机（SVM）模型获得了最佳的总体性能，并被选择进行基于数据的敏感性分析。先前的评估表现，研究差距和与年龄相关的特征在解释入学失败时起着重要作用。对于后续阶段，当前的评估性能功能将揭示其预测能力。建议的指南包括为风险概况提供研究支持小组并创建监测框架。从实际的角度来看，