Hannaford Li, Cheng Xiaoyue, Kunes-Connell Mary
College of Nursing, Creighton University, 2500 California Plaza, Omaha 68178, NE, USA.
Department of Mathematics, University of Nebraska at Omaha, 6001 Dodge Street, Omaha 68182, NE, USA.
Nurse Educ Today. 2021 Apr;99:104784. doi: 10.1016/j.nedt.2021.104784. Epub 2021 Jan 26.
Despite powerful efforts to maximize nursing school enrollment, schools and colleges of nursing are faced with high rates of attrition and low rates of completion. Early identification of at-risk students and the factors associated with graduation outcomes are the main foci for the studies that have addressed attrition and completion rates in nursing programs. Machine learning has been shown to perform better in prediction tasks than traditional statistical methods.
The purpose of this study was to identify adequate models that predict, early in a students career, if an undergraduate nursing student will graduate within six college years. In addition, factors related to successful graduation were to be identified using several of the algorithms.
Predictions were made at five time points: the beginning of the first, second, third, fourth years, and the end of the sixth year. Fourteen scenarios were built for each machine learning algorithm through the combinations of different variable sections and time points.
College of Nursing in a private university in an urban Midwest city, USA.
Seven hundred and seventy-three full time, first time, and degree-seeking students who enrolled from 2004 through 2012 in a traditional 4-year baccalaureate nursing program.
Eight popular machine learning algorithms were chosen for model construction and comparison. In addition, a stacked ensemble method was introduced in the study to boost the accuracy and reduce the variance of prediction.
Using one year of college academic performance, the graduation outcome can be correctly predicted for over 80% of the students. The prediction accuracy can reach 90% after the second college year and 99% after the third year. Among all the variables, cumulative grade points average (GPA) and nursing course GPA are the most influential factors for predicting graduation.
This study provides a potential mode of data-based tracking system for nursing students during their entire baccalaureate program. This tracking system can serve a large number of students automatically to provide customized evaluation on the dropout risk students and enhance the ability of a school or college to more strategically design school-based prevention and interventional services.
尽管为使护理专业招生人数最大化做出了巨大努力，但护理学院仍面临着高流失率和低毕业率的问题。早期识别有风险的学生以及与毕业结果相关的因素是研究护理专业项目流失率和毕业率的主要重点。机器学习已被证明在预测任务中比传统统计方法表现更好。
本研究的目的是确定合适的模型，以便在学生职业生涯早期预测本科护理学生是否会在六个大学年内毕业。此外，使用几种算法来确定与成功毕业相关的因素。
在五个时间点进行预测：第一年、第二年、第三年、第四年开始时以及第六年结束时。通过不同变量部分和时间点的组合，为每种机器学习算法构建了14种情况。
美国中西部城市一所私立大学的护理学院。
773名全日制、首次入学且攻读学位的学生，他们于2004年至2012年参加了传统的4年制本科护理项目。
选择了八种流行的机器学习算法进行模型构建和比较。此外，本研究引入了一种堆叠集成方法来提高预测的准确性并降低预测的方差。
利用一年的大学学业成绩，可以正确预测超过80%的学生的毕业结果。在大学第二年之后预测准确率可达90%，第三年之后可达99%。在所有变量中，累积平均绩点（GPA）和护理课程GPA是预测毕业的最有影响的因素。
本研究为护理学生在整个本科项目期间提供了一种基于数据的跟踪系统的潜在模式。这种跟踪系统可以自动为大量学生服务，为有辍学风险的学生提供定制评估，并增强学校或学院更具战略性地设计校内预防和干预服务的能力。
