Nguyen Thi Cam Huong, Sarlan Aliza, Arshad Noreen Izza
Universiti Teknologi Petronas, Department of Computer and Information Sciences, Seri Iskandar, Malaysia.
Software Engineering Department, FPT University, Ho Chi Minh, Vietnam.
PeerJ Comput Sci. 2024 Nov 29;10:e2572. doi: 10.7717/peerj-cs.2572. eCollection 2024.
Student dropout rates are one of the major concerns of educational institutions because they affect the success and efficacy of them. In order to help students continue their learning and achieve a better future, there is a need to identify the risk of student dropout. However, it is challenging to accurately identify the student dropout risk in the preliminary stages considering the complexities associated with it. This research develops an efficient prediction model using machine learning (ML) and deep learning (DL) techniques for identifying student dropouts in both small and big educational datasets.
A hybrid prediction model DeepS3VM is designed by integrating a Semi-supervised support vector machine (S3VM) model with a recurrent neural network (RNN) to capture sequential patterns in student dropout prediction. In addition, a personalized recommendation system (PRS) is developed to recommend personalized learning paths for students who are at risk of dropping out. The potential of the DeepS3VM is evaluated with respect to various evaluation metrics and the results are compared with various existing models such as Random Forest (RF), decision tree (DT), XGBoost, artificial neural network (ANN) and convolutional neural network (CNN).
The DeepS3VM model demonstrates outstanding accuracy at 92.54%, surpassing other current models. This confirms the model's effectiveness in precisely identifying the risk of student dropout. The dataset used for this analysis was obtained from the student management system of a private university in Vietnam and generated from an initial 243 records to a total of one hundred thousand records.
学生辍学率是教育机构主要关注的问题之一，因为它们会影响教育机构的成功与效能。为了帮助学生继续学习并拥有更美好的未来，有必要识别学生辍学的风险。然而，考虑到与之相关的复杂性，在初始阶段准确识别学生辍学风险具有挑战性。本研究利用机器学习（ML）和深度学习（DL）技术开发了一种高效的预测模型，用于在小型和大型教育数据集中识别学生辍学情况。
通过将半监督支持向量机（S3VM）模型与循环神经网络（RNN）集成，设计了一种混合预测模型DeepS3VM，以捕捉学生辍学预测中的序列模式。此外，还开发了一个个性化推荐系统（PRS），为有辍学风险的学生推荐个性化学习路径。针对各种评估指标对DeepS3VM的潜力进行了评估，并将结果与随机森林（RF）、决策树（DT）、XGBoost、人工神经网络（ANN）和卷积神经网络（CNN）等各种现有模型进行了比较。
DeepS3VM模型的准确率高达92.54%，表现出色，超过了其他现有模型。这证实了该模型在准确识别学生辍学风险方面的有效性。本分析所用数据集来自越南一所私立大学的学生管理系统，最初有243条记录，最终生成了总计10万条记录。
