Learner satisfaction prediction from MOOC reviews and behavioral logs is valuable for course quality improvement and platform operations. In practice, models trained on one platform degrade significantly when deployed on another due to domain shift in review style, learner population, behavioral logging schemas, and platform-specific rating norms. We study \textbf{cross-platform domain adaptation} for multi-modal MOOC satisfaction prediction under limited or absent target-platform labels. We propose \textbf{ADAPT-MS}, a platform-adaptive framework that (i) encodes review text with a frozen LLM encoder and behavioral traces with a canonical-vocabulary MLP, (ii) aligns cross-platform representations via domain-adversarial training with gradient reversal, (iii) corrects platform-specific rating bias through a latent-variable calibration layer, and (iv) handles missing behavioral modalities via gated fusion with modality dropout. Experiments on a multi-platform MOOC dataset spanning three major platforms demonstrate that ADAPT-MS achieves target-platform RMSE of 0.66 in the unsupervised setting (zero labeled target samples) and 0.60 with 1000 labeled target samples, outperforming strong baselines including naive pooling, domain-adversarial alignment without calibration, and full fine-tuning. Ablation studies confirm the independent contribution of each component, and few-shot adaptation curves demonstrate stable improvement even with as few as 50 labeled target samples.

翻译：从MOOC评论和行为日志中预测学习者满意度对于课程质量提升和平台运营具有重要价值。实际应用中，由于评论风格、学习者群体、行为日志记录模式及平台特定评分规范的域偏移，在一个平台上训练的模型部署到另一平台时性能会显著下降。本研究针对有限或缺失目标平台标签情境下的多模态MOOC满意度预测，探索跨平台域适应方法。我们提出平台自适应框架ADAPT-MS，该框架：(i) 通过冻结的大语言模型编码器编码评论文本，并通过规范词汇多层感知器处理行为轨迹；(ii) 通过梯度反转的域对抗训练对齐跨平台表征；(iii) 通过隐变量校准层纠正平台特定评分偏差；(iv) 通过带模态丢弃的门控融合处理缺失行为模态。在涵盖三个主流平台的多平台MOOC数据集上的实验表明，ADAPT-MS在无监督设置（零标注目标样本）下目标平台均方根误差达0.66，在配备1000个标注目标样本时达0.60，均优于朴素池化、无校准的域对抗对齐及全微调等强基线方法。消融实验证实了各组件独立贡献，且小样本适应曲线表明即使仅使用50个标注目标样本也能实现稳定性能提升。