The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualization can be implemented. To tackle this question we conduct a randomized controlled trial on a large digital self-learning platform. We develop an algorithm based on two convolutional neural networks that assigns tasks to $4,365$ learners according to their learning paths. Learners are randomized into three groups: two treatment groups -- a group-based adaptive treatment group and an individual adaptive treatment group -- and one control group. We analyze the difference between the three groups with respect to effort learners provide and their performance on the platform. Our null results shed light on the multiple challenges associated with the individualization of learning paths.

翻译：基于数字技术的学习内容个性化有望带来巨大的个人与社会效益。然而，如何实现这种个性化仍是一个悬而未决的问题。为探讨此问题，我们在一个大型数字化自主学习平台上开展了一项随机对照试验。我们开发了一种基于两个卷积神经网络的算法，该算法可根据学习路径为$4,365$名学习者分配学习任务。学习者被随机分为三组：两个处理组——基于群体的自适应处理组和个体自适应处理组——以及一个对照组。我们分析了三组学习者在平台上的努力程度与表现差异。我们的零结果揭示了学习路径个性化所面临的多重挑战。