超越经典与当代模型：使用 RAG、提示工程和跨模态融合的远程学习学生辍学预测变革性 AI 框架
Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion
AI总结：
本文提出结合RAG、提示工程与跨模态注意力融合的变革性AI框架，利用非结构化学生互动数据预测远程学习辍学风险，准确率达89%并减少假阴性，同时生成可解释的教学干预策略。
摘要：
远程学习中的学生辍学仍然是一个严峻挑战，具有深远的社会和经济后果。虽然经典的机器学习模型利用结构化的社会人口统计和行为数据，但它们往往无法捕捉嵌入在非结构化学生互动中的微妙情感和情境因素。本文引入了一个变革性的 AI 框架，通过三项协同创新重新定义了辍学预测：用于特定领域情感分析的检索增强生成（RAG）、用于解码学业压力源的提示工程，以及用于动态对齐文本、行为和社会人口统计见解的跨模态注意力融合。通过将情感分析建立在精心策划的教学内容知识库基础上，我们经过 RAG 增强的 BERT 模型以前所未有的情境相关性来解释学生的评论，同时优化的提示分离出学业困扰的指标（例如，“孤立”、“工作量焦虑”）。随后，跨模态注意力层将这些见解与时间参与模式相融合，创建整体风险特征。在一个包含 4423 名学生的纵向数据集上进行评估，该框架达到了 89% 的准确率和 0.88 的 F1 分数，比传统模型高出 7%，并将假阴性率降低了 21%。除了预测之外，该系统还通过检索情境一致的战略（例如，针对孤立学习者的导师计划）来生成可解释的干预措施。这项工作弥合了预测性分析与可操作的教学法之间的差距，为缓解全球教育系统中的辍学风险提供了一个可扩展的解决方案。
英文摘要：
Student dropout in distance learning remains a critical challenge, with profound societal and economic consequences. While classical machine learning models leverage structured socio-demographic and behavioral data, they often fail to capture the nuanced emotional and contextual factors embedded in unstructured student interactions. This paper introduces a transformative AI framework that redefines dropout prediction through three synergistic innovations: Retrieval-Augmented Generation (RAG) for domain-specific sentiment analysis, prompt engineering to decode academic stressors,and cross-modal attention fusion to dynamically align textual, behavioral, and socio-demographic insights. By grounding sentiment analysis in a curated knowledge base of pedagogical content, our RAG-enhanced BERT model interprets student comments with unprecedented contextual relevance, while optimized prompts isolate indicators of academic distress (e.g., "isolation," "workload anxiety"). A cross-modal attention layer then fuses these insights with temporal engagement patterns, creating holistic risk pro-files. Evaluated on a longitudinal dataset of 4 423 students, the framework achieves 89% accuracy and an F1-score of 0.88, outperforming conventional models by 7% and reducing false negatives by 21%. Beyond prediction, the system generates interpretable interventions by retrieving contextually aligned strategies (e.g., mentorship programs for isolated learners). This work bridges the gap between predictive analytics and actionable pedagogy, offering a scalable solution to mitigate dropout risks in global education systems
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
