Researching for better instructional methods using AB experiments in MOOCs: results and challenges
Researching for better instructional methods using AB experiments in MOOCs: results and challenges
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在 MOOC 中使用 AB 实验研究更好的教学方法：结果和挑战
DOI：
10.1186/s41039-016-0034-4
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发表时间：
2016
影响因子：
3.2
通讯作者：
David E. Pritchard
中科院分区：
文献类型：
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作者：
Zhongzhou Chen;Christopher Chudzicki;D. Palumbo;Giora Alexandron;Youn;Qian Zhou;David E. Pritchard
我们在一个大规模的开放式在线课程中进行了两个AB实验（治疗与对照）。第一个实验评估刻意练习活动（DPAs）的发展解决问题的专业知识，衡量传统的物理问题。我们发现，一个更具互动性的拖放格式的DPA产生更快的学习比多项选择格式，但DPA不提高性能解决传统的物理问题比正常的家庭作业练习。实验二表明，不同的视频拍摄设置可以提高教师的流畅性，从而提高学生的参与度，但对学习效果没有显着影响。这两个案例展示了MOOC AB实验作为开放式研究工具的潜力，但也揭示了其局限性。我们讨论了三个最重要的挑战：广泛的学生分布，“开卷”性质的评估，以及大量和各种各样的数据。我们建议可能的方法来科普这些。
We conducted two AB experiments (treatment vs. control) in a massive open online course. The first experiment evaluates deliberate practice activities (DPAs) for developing problem solving expertise as measured by traditional physics problems. We find that a more interactive drag-and-drop format of DPA generates quicker learning than a multiple choice format but DPAs do not improve performance on solving traditional physics problems more than normal homework practice. The second experiment shows that a different video shooting setting can improve the fluency of the instructor which in turn improves the engagement of the students although it has no significant impact on the learning outcomes. These two cases demonstrate the potential of MOOC AB experiments as an open-ended research tool but also reveal limitations. We discuss the three most important challenges: wide student distribution, “open-book” nature of assessments, and large quantity and variety of data. We suggest possible methods to cope with those.
机器学习和数据挖掘百科全书
DOI：
10.1007/978-1-4899-7502-7_900-1
发表时间：
2016
期刊：
--
影响因子：
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作者：
Flach P
通讯作者：
Flach P