Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform
This work addresses the challenge of implementing personalized learning in digital education, though it yields incremental insights due to null results.
The study tested whether machine learning algorithms could effectively individualize learning paths on an online platform, but found no significant differences in learner effort or performance between adaptive treatment groups and a control group.
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.