Online education platforms have experienced explosive growth over the past decade, generating massive volumes of user-generated content in the form of reviews, ratings, and behavioral logs. These heterogeneous signals provide unprecedented opportunities for understanding learner satisfaction, which is a critical determinant of course retention, engagement, and long-term learning outcomes. However, accurately predicting satisfaction remains challenging due to the short length, noise, contextual dependency, and multi-dimensional nature of online reviews. In this paper, we propose a unified \textbf{Large Language Model (LLM)-based multi-modal framework} for predicting both platform-level and course-level learner satisfaction. The proposed framework integrates three complementary information sources: (1) short-text topic distributions that capture latent thematic structures, (2) contextualized sentiment representations learned from pretrained Transformer-based language models, and (3) behavioral interaction features derived from learner activity logs. These heterogeneous representations are fused within a hybrid regression architecture to produce accurate satisfaction predictions. We conduct extensive experiments on large-scale MOOC review datasets collected from multiple public platforms. The experimental results demonstrate that the proposed LLM-based multi-modal framework consistently outperforms traditional text-only models, shallow sentiment baselines, and single-modality regression approaches. Comprehensive ablation studies further validate the necessity of jointly modeling topic semantics, deep sentiment representations, and behavioral analytics. Our findings highlight the critical role of large-scale contextual language representations in advancing learning analytics and provide actionable insights for platform design, course improvement, and personalized recommendation.

翻译：过去十年间，在线教育平台经历了爆发式增长，产生了海量用户生成内容，涵盖评论文本、评分数据及行为日志等形式。这些异质信号为理解学习者满意度——这一决定课程留存率、参与度及长期学习成效的关键因素——提供了前所未有的机遇。然而，由于在线评论文本具有短文本性、噪声干扰、语境依赖及多维度特征，准确预测满意度仍面临严峻挑战。本文提出一种统一的**基于大语言模型的**多模态框架，用于同时预测平台级与课程级学习者满意度。该框架整合三类互补信息源：(1) 捕捉潜在主题结构的短文本主题分布；(2) 基于预训练Transformer语言模型习得的语境化情感表征；(3) 从学习者行为日志中提取的交互特征。这些异质表征在混合回归架构中融合，以生成精准的满意度预测。我们在多个公开平台收集的大规模MOOC评价数据集上开展广泛实验，结果表明所提出的基于大语言模型的多模态框架在性能上持续优于传统纯文本模型、浅层情感基线及单模态回归方法。全面的消融研究进一步验证了联合建模主题语义、深层情感表征与行为分析的必要性。研究结论凸显了大规模语境化语言表征在学习分析领域的关键作用，并为平台设计、课程优化及个性化推荐提供了可实践洞见。