Abstract

An important problem in higher education is to find the most suitable admission procedure that can distinguish between students with high academic potential and future dropouts. Admissions usually rely on pre-enrolment achievement measures; therefore, it is crucial that these selection criteria have high predictive validity on academic achievement. In this study, we use sophisticated statistical learning methods, such as receiver operating characteristic curve analysis, logistic and Tobit regression to analyse the predictive validity of the Hungarian university entrance score on final university performance, in particular on degree completion and qualification. We place particular emphasis on drawing statistically grounded conclusions. The analysis is built on data of 21,547 undergraduate students from the Budapest University of Technology and Economics. We find that the current Hungarian centralised entrance score is a valid predictor, however, its predictive validity varies significantly across disciplines. We find that high school grades have strong predictive validity, and general knowledge is more important than program-specific knowledge. We also find that the academic performance of females is underpredicted and that of the males is overpredicted by the university entrance score.

摘要

高等教育中的一个重要问题是找到最合适的录取程序，以区分具有高学术潜力的学生和将来辍学的学生。入学通常依靠入学前成绩衡量；因此，至关重要的是这些选择标准对学习成绩具有较高的预测效度。在这项研究中，我们使用了复杂的统计学习方法，例如接收者工作特征曲线分析，逻辑分析和Tobit回归分析匈牙利大学入学分数对最终大学表现的预测效度，特别是在学位完成和学历方面。我们特别强调得出基于统计的结论。分析基于21的数据，布达佩斯科技经济大学的547名本科生。我们发现当前的匈牙利集中入学分数是有效的预测因子，但是，其预测有效性在各个学科之间差异很大。我们发现高中成绩具有很强的预测效度，而一般知识比特定于课程的知识更重要。我们还发现，大学入学分数低估了女性的学业表现，而高估了男性的学业表现。