论文标题
Edtech的个性化建议：来自随机对照试验的证据
Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial
论文作者
论文摘要
我们研究个性化内容建议对儿童教育应用的使用的影响。在一项随机对照试验中，我们表明，个性化建议的引入将应用程序的个性化部分的内容增加约60％。我们进一步表明，与基线系统相比，人类内容编辑在给定年级的所有学生中选择故事的总体应用程序增加了14％。个性化内容中的个体收益的幅度随着有关学生的可用数据量而增加，并且具有对利基内容的偏好：偏爱的内容互动的较长历史的较长历史，他们喜欢利基含量的内容比不常见的新用户更喜欢流行内容。为了促进从更简单的系统中转向个性化推荐系统，我们描述了如何做出重要的设计决策，例如使用离线指标比较替代模型并选择合适的目标受众。
We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60%. We further show that the overall app usage increases by 14%, compared to the baseline system where human content editors select stories for all students at a given grade level. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the move to personalized recommendation systems from a simpler system, we describe how we make important design decisions, such as comparing alternative models using offline metrics and choosing the right target audience.
