数据革命进入高等教育:查明智利学风险的学生
Paul T Von Hippel1, Alvaro Hofflinger2
1LBJ School of Public Affairs, University of Texas, Austin, Texas, USA.
概括
高等教育数据分析可以识别有风险的学生. 大学成绩显著改善了学预测,使得有针对性的干预措施能够提高学生的坚持和成功.
科学领域:
- 高等教育的分析.
- 学生的成功研究研究.
- 教育数据挖掘教育数据挖掘
背景情况:
- 拉丁美洲的高等教育面临着日益增长的入学率,但毕业率和持续率较低.
- 识别有风险的学生对于提高学生在大学中的成功至关重要.
- 数据分析为针对性干预和评估其有效性提供了一个潜在的解决方案.
研究的目的:
- 为了说明数据分析在识别有学风险的学生的潜力.
- 评估入学和大学成绩数据对学生持久性的预测能力.
- 评估学生成功计划及其准策略的有效性.
主要方法:
- 利用了来自智利八所大学的数据,包括入学信息和大学成绩.
- 采用预测建模来评估学生数据与坚持率之间的关系.
- 分析了财政援助,主要选择和学生成功计划对坚持的影响.
主要成果:
- 仅凭入学数据就显示了学生坚持不的预测能力较弱.
- 纳入大学成绩显著改善了持续性预测.
- 财政援助积极预测持久性,而拒绝首选专业预测持久性较低.
- 学生成功计划的有效性各不相同,并且经常未能针对高风险的学生.
结论:
- 大学应该系统地使用数据分析来识别和支持有风险的学生.
- 结合学术成绩的预测模型对于有效的干预至关重要.
- 干预措施必须针对高风险学生,并严格评估其有效性.
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