Purpose

The prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.

Design/methodology/approach

Machine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.

Findings

About 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.

Practical implications

This predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.

Originality/value

The study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

中文翻译：

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使用机器学习预测学生流失风险

目的

学生流失的预测对于促进保留机制至关重要。本研究旨在专注于实施一种方法来预测物理治疗项目高年级学生的流失。

设计/方法/方法

机器学习是一种可以识别模式并生成预测模型的计算机工具。使用定量研究方法，访问了包含 336 名大学生的高年级课程数据库。参与者的数据是从金融学术管理系统和智利自治大学的一个平台收集的。选择了 5 个定量变量和 11 个定性变量，与大学生流失相关。使用该数据库，基于监督机器学习测试了 23 个分类器。

发现

约 23.58% 的男性和 17.39% 的女性在减员学生组中。分类器的平均准确度随着用于训练的变量数量的增加而增加。使用“Subspace KNN”算法（86.3%）获得了最佳准确度水平。分类器“RUSboosted trees”产生的假阴性数量最少，所用算法的灵敏度更高（78%），特异性为 86%。

实际影响

这种预测方法可识别大学课程中的减员学生，并可用于提高学生在高年级的保留率。

原创性/价值

该研究开发了一种新的高年级课程学生流失预测模型，适用于流失学生数量较少的不平衡数据库。