量化儿童视频的学术质量：基于机器阅读理解
Quantifying the Academic Quality of Children's Videos using Machine Comprehension
AI总结：
本研究利用阅读理解模型和中学教科书问题数据集，量化YouTube Kids顶级频道的学术质量，通过分析8万多个视频，首次全面评估其教育价值。
摘要：
YouTube Kids (YTK) 是数百万儿童每天使用的最受欢迎的儿童应用程序之一。然而，多项研究已指出该平台视频存在的问题，如娱乐性和商业性内容过多。YouTube 最近提出了高质量指南，其中包括“促进学习”，并提议将其用于频道排名。然而，学习的概念是多方面的，且难以在在线视频的背景下定义和衡量。本研究聚焦于学校所教授内容方面的学习，并提出了一种衡量儿童视频学术质量的方法。利用一个包含儿童视频问答的新数据集，我们首先证明阅读理解 (RC) 模型能够估计学术学习效果。然后，利用一个包含不同主题的中学教科书问题的大型数据集，我们将顶级频道的学术质量量化为 RC 模型能够正确回答的儿童教科书问题的数量。通过分析排名前 100 的频道上发布的超过 80,000 个视频，我们首次对 YTK 上频道的学术质量进行了全面分析。
英文摘要：
YouTube Kids (YTK) is one of the most popular kids' applications used by millions of kids daily. However, various studies have highlighted concerns about the videos on the platform, like the over-presence of entertaining and commercial content. YouTube recently proposed high-quality guidelines that include `promoting learning' and proposed to use it in ranking channels. However, the concept of learning is multi-faceted, and it can be difficult to define and measure in the context of online videos. This research focuses on learning in terms of what's taught in schools and proposes a way to measure the academic quality of children's videos. Using a new dataset of questions and answers from children's videos, we first show that a Reading Comprehension (RC) model can estimate academic learning. Then, using a large dataset of middle school textbook questions on diverse topics, we quantify the academic quality of top channels as the number of children's textbook questions that an RC model can correctly answer. By analyzing over 80,000 videos posted on the top 100 channels, we present the first thorough analysis of the academic quality of channels on YTK.
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