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.

基于数字技术的学习内容个性化给个体和社会带来了巨大的好处，然而如何实现这种个性化仍然是一个未解之谜。为了解决这个问题，我们在一个大型数字自学平台上进行了一项随机对照试验。我们基于两个卷积神经网络开发了一个算法，根据学习路径将任务分配给4365位学习者，将学习者随机分为三组：两个处理组（一个群组自适应处理组和一个个人自适应处理组）和一个对照组。我们分析了三组在平台上所提供的努力和表现之间的差异。我们的空缺结果揭示了与学习路径个性化相关的多个挑战。

Can machine learning solve the challenge of adaptive learning and the
  individualization of learning paths? A field experiment in an online learning
  platform

机器学习能否解决自适应学习和个性化学习路径的挑战？在线学习平台的实地实验