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Towards Feasible Instructor Intervention in MOOC Discussion Forums Indicate Submission Type: Research-in-Progress

机译:对MooC讨论论坛的可行的教练干预表明提交类型:研究进展

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Massive Open Online Courses allow numerous people from around the world to have access to knowledge that they otherwise have not. However, high student-to-instructor ratio in MOOCs restricts instructors' ability to facilitate student learning by intervening in discussions forums, as they do in face-to-face classrooms. Instructors need automated guidance on when and how to intervene in discussion forums. Using a typology of pedagogical interventions derived from prior research, we annotate a large corpus of discussion forum contents to enable supervised machine learning to automatically identify interventions that promote student learning. Such machine learning models may allow building of dashboards to automatically prompt instructors on when and how to intervene in discussion forums. In the longer term, it may be possible to automate these interventions relieving instructors of this effort. Such automated approaches are essential for allowing good pedagogical practices to scale in the context of MOOC discussion forums.
机译:大规模开放的在线课程允许来自世界各地的众多人可以获得他们否则没有的知识。然而,Moocs的高学生对教练率限制了教师通过干预讨论论坛的促进学生学习的能力,因为他们在面对面的教室里。教师需要在何时以及如何在讨论论坛中进行自动化指导。使用从现有研究中得出的教学干预的类型学,我们注释了一个大型讨论论坛内容的语料库,使监督机器学习自动识别推广学生学习的干预措施。这种机器学习模型可能允许在何时以及如何在讨论论坛中进行何时以及如何促进仪表板以自动提示教师。在长期内,可能有可能自动化这些干预措施缓解这项努力的教练。这种自动化方法对于允许在MooC讨论论坛的背景下施加良好的教学实践至关重要。

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