用AI融合文本、行为与社会数据，精准预测远程学习辍学风险
Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion
- 引入RAG+提示工程+跨模态融合，提升情感分析与风险识别能力
- 在4423名学生数据上达89%准确率，误报率降低21%
- 可生成具体干预建议，适合教育机构与智能辅导系统使用
远程学习中的学生辍学问题仍具重大社会与经济影响。传统机器学习模型依赖结构化社会人口与行为数据，却难以捕捉非结构化互动中的情绪与情境因素。本文提出一种创新AI框架，通过三大协同技术实现突破：基于领域知识库的RAG增强情感分析，优化提示工程识别学业压力信号（如“孤立感”、“工作量焦虑”），以及跨模态注意力机制动态融合文本、行为与社会人口信息。该框架在包含4423名学生的纵向数据集上，实现89%准确率与0.88 F1分数，较传统模型提升7%，误报减少21%。系统还可生成可解释的干预策略（如为孤立者推荐导师计划），推动预测分析向教学实践转化，为全球教育系统提供可扩展的辍学风险缓解方案。
原文摘要 · Abstract (English)
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
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