Sharma Sunil Kumar
Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Sci Rep. 2024 Dec 28;14(1):30830. doi: 10.1038/s41598-024-81181-9.
Academic institutions face increasing challenges in predicting student enrollment and managing retention. A comprehensive strategy is required to track student progress, predict future course demand, and prevent student churn across various disciplines. Institutions need an effective method to predict student enrollment while addressing potential churn. The existing approaches are often inadequate in handling both numerical and textual data, limiting the ability to provide personalized retention strategies. We propose an innovative framework that combines deep learning with recommender systems for student enrollment prediction and churn prevention. The framework integrates advanced preprocessing techniques for both numeric and textual data. Feature extraction is performed with statistical measures for numeric data, and advanced text techniques like GloVe embeddings, Latent Dirichlet Allocation (LDA) for topic modeling, and SentiWordNet for sentiment analysis. A weighted feature fusion approach combines these features, and the optimal features are selected using the Pythagorean fuzzy AHP with a Hybrid Optimization approach, specifically the Instructional Emperor Pigeon Optimization (IEPO). The DeepEnrollNet model, a hybrid CNN-GRU-Attention QCNN architecture, is used for enrollment prediction, while Deep Q-Networks (DQN) are applied to generate actionable retention recommendations. This comprehensive methodology improves predictive accuracy for student enrolment and provides tailored strategies to enhance retention by addressing both text and numeric data in a unified framework. The DeepEnrollNet has the minimum MSE of 0.218978, MSRE of 0.216445, a NMSE of 0.232453, RMSE of 0.23213, and MAPE of 0.218754.
学术机构在预测学生入学人数和管理学生留存率方面面临着越来越大的挑战。需要一个全面的策略来跟踪学生的进展情况，预测未来的课程需求，并防止各个学科的学生流失。机构需要一种有效的方法来预测学生入学人数，同时应对潜在的学生流失问题。现有的方法在处理数值和文本数据方面往往存在不足，限制了提供个性化留存策略的能力。我们提出了一个创新框架，将深度学习与推荐系统相结合，用于学生入学预测和流失预防。该框架集成了针对数值和文本数据的先进预处理技术。对数值数据采用统计方法进行特征提取，对文本数据采用诸如GloVe嵌入、潜在狄利克雷分配（LDA）进行主题建模以及SentiWordNet进行情感分析等先进技术。一种加权特征融合方法将这些特征结合起来，并使用毕达哥拉斯模糊层次分析法与混合优化方法，特别是教学帝鸽优化（IEPO）来选择最优特征。DeepEnrollNet模型是一种混合的CNN - GRU - 注意力QCNN架构，用于入学预测，而深度Q网络（DQN）则用于生成可操作的留存建议。这种全面的方法提高了学生入学预测的准确性，并通过在统一框架中处理文本和数值数据提供了量身定制的策略来提高留存率。DeepEnrollNet的最小均方误差为0.218978，均方相对误差为0.216445，归一化均方误差为0.232453，均方根误差为0.23213，平均绝对百分比误差为0.218754。
