Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.
翻译:Quizlet 是美国最流行的在线学习工具 Quizlet 是美国最流行的在线学习工具 Quizlet 是美国最流行的在线学习工具, 高中学生中超过2/3的高中学生使用2/3以上, 大学生使用1/2/2的大学学生使用。 超过95%的 Quizlet 用户报告成绩因此提高, 该平台已成为数百万间教室使用的脱法工具 。 在本文中, 我们探索了向学生推荐合适的学习内容的任务, 因为他们先前的利益, 以及他们的同龄人正在学习什么。 我们提议了一种新颖的方法, 即: 神经教育建议粗教育建议引擎(NENEEEEE), 利用学生行为而不是评级来建议教育内容。 我们发现, 这个方法可以更好地捕取更符合学习成绩的 。 NEREEEEEEE 建了一个经常性的神经网络, 其中包括协作和内容基于内容的推荐方法, 并且考虑到任何特定学生的速度、 硕士和经验来建议适当的任务。 我们通过共同学习用户的嵌入和内容嵌入方法来培训NER。 我们用NER 和内容嵌入方法, 尝试如何预测内容嵌入最后的时间戳戳戳戳戳戳戳印内容 。 我们还在最后的模型中, 我们用一个建立一个信任的模型算算算算算算算算算器 。