Beyond classical and contemporary models: a transformative ai framework for student dropout prediction in distance learning using rag, prompt engineering, and cross-modal fusion

2025-07-04

AI Summary

Student attrition in online education remains a critical challenge, and conventional machine learning models struggle to capture affective and contextual factors embedded in unstructured interactions. This paper proposes a multimodal dropout risk prediction framework that jointly models textual sentiment (leveraging RAG-augmented domain-specific BERT with prompt engineering to detect academic stress), temporal behavioral patterns, and sociodemographic attributes. A cross-modal attention mechanism enables dynamic, fine-grained integration across modalities. The framework ensures strong model interpretability and supports generation of actionable, personalized intervention recommendations. Evaluated on a dataset of 4,423 students, the model achieves 89% accuracy and an F1-score of 0.88—outperforming baseline methods by 7% in F1 and reducing false-negative rate by 21%. This work establishes a novel paradigm for intelligent academic support in remote education.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 profiles. 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

Problem

• Predict student dropout in distance learning using AI
• Analyze unstructured emotional and contextual student data
• Improve prediction accuracy with cross-modal data fusion

Innovation

• RAG-enhanced BERT for contextual sentiment analysis
• Prompt engineering to detect academic distress
• Cross-modal fusion for holistic risk profiling