ABSTRACT

Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions’ effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results show that data available at matriculation are only weakly predictive of persistence, while prediction improves dramatically once data on university grades become available. Some predictors of persistence are under policy control. Financial aid predicts higher persistence, and being denied a first-choice major predicts lower persistence. Student success programmes are ineffective at some universities; they are more effective at others, but when effective they often fail to target the highest risk students. Universities should use data regularly and systematically to identify high-risk students, target them with interventions, and evaluate those interventions’ effectiveness.

摘要

拉丁美洲，尤其是智利，高等教育入学率大幅上升。然而毕业率和坚持率仍然很低。提高毕业和坚持的方法之一是使用数据和分析来识别有辍学风险的学生、有针对性的干预措施，并评估干预措施在提高学生成功方面的有效性。我们使用八所智利大学的数据来说明这种方法的潜力。结果表明，入学时获得的数据对持久性的预测能力很弱，而一旦获得大学成绩数据，预测就会显着改善。一些持久性预测因素受到政策控制。经济援助预示着更高的毅力，而被拒绝首选专业则预示着更低的毅力。一些大学的学生成功计划效果不佳；他们对其他人更有效，但当有效时，他们往往无法针对风险最高的学生。大学应定期、系统地使用数据来识别高风险学生，针对他们采取干预措施，并评估这些干预措施的有效性。