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Studying teacher selection of resources in an ultra-large scale interactive system: Does metadata guide the way?

机译：在超大规模互动系统中研究教师的资源选择：元数据是否可以引导方式？

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Ultra-large-scale interactive systems on the Internet have begun to change how teachers prepare for instruction, particularly in regards to resource selection. Consequently, it is important to look at how teachers are currently selecting resources beyond content or keyword search. We conducted a two-part observational study of an existing popular system called TeachersPayTeachers hypothesizing that "evaluative metadata' (i.e. comments, ratings, and popularity measures) would drive selection of resources. The first part examined patterns in tens of thousands of sales overall, and the second part focused on patterns of sales in one focal topic that could be expert coded. We find that there are significant gaps in available metadata, that some aspects of metadata are closely associated with sales, and that metadata are weak correlates of expert-determined quality. We conclude by making suggestions for additional research and suggesting how ultra-large scale-interactive systems such as TeachersPayTeachers could be used to improve teacher education.
机译：互联网上的超大规模互动系统已经开始改变教师为教学做准备的方式，特别是在资源选择方面。因此，重要的是要了解教师当前如何选择除内容或关键字搜索之外的资源。我们对现有的流行系统（称为TeachersPayTeachers）进行了两部分的观察性研究，假设“评估性元数据”（即评论，评分和受欢迎程度）将推动资源的选择。第一部分研究了成千上万次销售的模式，第二部分着重于一个可以由专家编码的焦点主题中的销售模式。我们发现，可用元数据存在巨大差距，元数据的某些方面与销售密切相关，而元数据与专家-最后，我们提出了进一步研究的建议，并提出了如何使用超大型互动式系统（如TeachersPayTeachers）来改善教师教育的建议。

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