Learner Affect Through the Looking Glass: Characterization and Detection of Confusion in Online Courses
Learner Affect Through the Looking Glass: Characterization and Detection of Confusion in Online Courses
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透过镜子观察学习者的影响：在线课程中混乱的特征和检测
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Bhat
中科院分区：
文献类型：
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作者：
Ziheng Zeng;Snigdha Chaturvedi;S. Bhat
描述学生的情感和情绪状态的性质并检测它们在在线课程平台中具有根本的重要性。在本文中，我们研究这个问题，通过使用来自大型开放式在线课程的论坛帖子。我们发现，被艾德为编码混乱的帖子实际上是与他们的信息需求有关的不同学习者影响的表现-主要是寻求事实答案。我们定量地证明，使用内容相关的语言特征和社区相关的功能来自后作为可靠的检测器的混乱，同时广泛优于目前可用的算法的混乱检测。我们还指出，在这个领域的几个预测任务（例如，混淆和紧急检测）可以是相关的，并且针对一个任务训练的模型可以有效地用于对另一个任务进行预测，而不需要标记的示例。最后，我们强调了一个非常重要的问题，即使分类器适应看不见的课程。
Characterizing the nature of students’ affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needs– primarily seeking factual answers. We quantitatively demonstrate that the use of content-related linguistic features and community-related features derived from a post serve as reliable detectors of confusion while widely outperforming currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses.