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

TL;DR

This study investigates whether machine learning algorithms can effectively personalize learning paths in an online platform through a large-scale field experiment, revealing significant challenges in implementation.

Contribution

The paper introduces a neural network-based algorithm for adaptive task assignment and evaluates its impact through a randomized controlled trial with thousands of learners.

Findings

No significant difference in effort among groups

No significant difference in learner performance

Highlights challenges in implementing personalized learning paths

Abstract

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.