M. H. Ismail, T. R. Razak, Noorfaizalfarid Mohd Noor, Azlan Abdul Aziz
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引用次数: 0
摘要
无论学生的社会或经济背景如何,资助都能确保他们公平地接受高等教育。然而,由于资助来源有限,管理者肩负着以公平公正的方式确定学生是否有资格获得资助的重任。此外,由人工评估人员确定资格的过程也可以从机器学习辅助决策支持工具中获益。本研究调查了使用机器学习算法实现这一目标的可行性。本次比较研究选择了三种算法,以决策树作为基准。这些算法是根据 ZAWAF 提供的高度不平衡数据集进行训练的。训练过程采用了 k 倍交叉验证和分层抽样技术。结果发现,所有机器学习模型的表现都优于基准模型,其中 MLP-ANN 模型的准确度和精确度得分最高。这表明,机器学习模型有可能被整合为财政援助分配的决策支持。
Evaluating Machine Learning Algorithms for Predicting Financial Aid Eligibility: A Comparative Study of Random Forest, Gradient Boosting and Neural Network
Financial aid ensures equitable access to higher education, irrespective of students' social or economic backgrounds. However, as the financial aid source are limited, the administrators are burdened with the task of determining the student eligibility for financial aid in a fair and unbias manner. Additionally, the process of determining eligibility by human evaluators can benefit from machine learning assisted decision support tools. This study investigates the feasibility of using machine learning algorithms to achieve this goal. Three algorithms were selected for this comparative study with Decision Tree acts as a baseline. The algorithms are trained against a highly imbalanced dataset provided by ZAWAF. The training process incorporates k-fold cross-validation and employs stratified sampling techniques. It was found that all machine learning models outperformed the baseline, with the MLP-ANN model exhibiting the highest accuracy and precision scores. This demonstrates the potential for machine learning models to be integrated as decision support for the distribution of financial aid.