Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

大规模开放在线课程（MOOC）已成为全球学习者的流行工具。但是，缺乏情感互动和支持是学习者放弃学习并最终导致学习成绩差的重要原因。这项研究通过分析13个MOOC的40万多个论坛帖子，运用成就情感的综合框架来揭示其对学生学习的整体影响。首先建立了六个机器学习模型来自动识别成就情感，包括K最近邻居，逻辑回归，朴素贝叶斯，决策树，随机森林和支持向量机。结果显示，Random Forest的Kappa值为0.83，ROC_AUC值为0.97，表现最佳。然后，采用“逐步建立”策略的多层次建模可以量化成就情绪对学生学习成绩的影响。结果表明，不同的成就情感对学生的学习有不同的影响。这些发现使MOOC平台和讲师可以自动或手动向学生提供相关的情感反馈，从而改善他们在MOOC中的学习。