2024/07/03 by Tim Klausmann, Klausmann, Tim, Marius Köppel +5
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Online Learning and Analytics
paper · pdf · doi:10.48550/arxiv.2407.03118
openalex publication_date 2024/07/03 · openalex created_date 2024/07/06 · openalex updated_date 2026/07/28
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.