Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing student experience in a beginner computer programming MOOC. A dataset of more than 25,000 online posts made by participants during the course was analysed and compared to student feedback. The results were further analysed by grouping participants according to their prior knowledge of the subject: beginner, experienced, and unknown. In this study, the average sentiment expressed through online posts reflected the feedback statements. Beginners, the target group for the MOOC, were more positive about the course than experienced participants, largely due to the extra assistance they received. Many experienced participants had expected to learn about topics that were beyond the scope of the MOOC. The results suggest that MOOC designers should consider using sentiment analysis to evaluate student feedback and inform MOOC design.

中文翻译：

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使用情感分析和目标群体评估MOOC中的学生反馈

许多试图评估MOOC参与者体验的课程设计者会发现，很难跟踪和分析学生的在线行为和互动，因为可能有成千上万的学习者参加了有时仅持续数周的课程。这项研究探索了自动情绪分析在评估初学者计算机编程MOOC中的学生体验中的用途。分析了参与者在课程中发表的超过25,000个在线帖子的数据集，并将其与学生反馈进行了比较。通过根据参与者对本主题的先验知识（初学者，有经验的和未知的）将参与者分组来进一步分析结果。在这项研究中，通过在线帖子表达的平均情绪反映了反馈陈述。初学者，MOOC的目标群体，与经验丰富的参与者相比，该课程更加积极，这主要是因为他们获得了额外的帮助。许多有经验的参与者都希望了解超出MOOC范围的主题。结果表明，MOOC设计者应考虑使用情绪分析来评估学生的反馈并为MOOC设计提供信息。