Abstract
School dropout is a serious problem in distance learning, where early detection is crucial for effective intervention and student perseverance. Predicting student dropout using available educational data is a widely researched topic in learning analytics. Our partner's distance learning platform highlights the importance of integrating diverse data sources, including socio-demographic data, behavioral data, and sentiment analysis, to accurately predict dropout risks. In this paper, we introduce a novel model that combines sentiment analysis of student comments using the Bidirectional Encoder Representations from Transformers (BERT) model with socio-demographic and behavioral data analyzed through Extreme Gradient Boosting (XGBoost). We fine-tuned BERT on student comments to capture nuanced sentiments, which were then merged with key features selected using feature importance techniques in XGBoost. Our model was tested on unseen data from the next academic year, achieving an accuracy of 84\%, compared to 82\% for the baseline model. Additionally, the model demonstrated superior performance in other metrics, such as precision and F1-score. The proposed method could be a vital tool in developing personalized strategies to reduce dropout rates and encourage student perseverance
Abstract (translated)
在校外学习环境中，学生辍学是一个严重的问题，早期检测对于有效的干预和鼓励学生的坚持至关重要。使用现有的教育数据预测学生辍学是学习分析领域广泛研究的主题之一。我们的合作伙伴的校外学习平台强调了整合多样化的数据源的重要性，包括社会人口统计数据、行为数据以及情感分析，以便准确地预测辍学风险。 在这篇论文中，我们介绍了一种新颖的方法，该方法结合使用基于转换器（Transformer）模型中的双向编码表示 (BERT) 对学生评论进行情感分析，并通过极端梯度提升 (XGBoost) 分析社会人口统计和行为数据。我们将 BERT 在学生评论上进行了微调以捕捉细微的情感变化，然后将这些结果与通过 XGBoost 的特征重要性技术选取的关键特征合并在一起。 我们的模型在来自下一个学年的未见过的数据集上进行了测试，并达到了84%的准确率，相比之下，基准模型仅达到82%。此外，在其他指标如精确度和F1分数方面，该模型也表现出更优越的表现。 提出的方法可以成为开发个性化策略以降低辍学率并鼓励学生坚持学习的重要工具。
URL
https://arxiv.org/abs/2507.10421
