Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments

TL;DR

This paper introduces sequential randomized trials (SRTs), a novel experimental design for developing adaptive, personalized interventions in large-scale digital learning environments, addressing limitations of traditional A/B testing.

Contribution

The paper presents the SRT design, demonstrating its advantages over traditional experiments and providing practical guidance for implementing SRTs in digital learning contexts.

Findings

SRTs effectively inform personalized intervention sequencing.

In a large MOOC, culturally targeted reminders improved engagement.

SRTs enable continuous adaptation of interventions.

Abstract

Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through inclusive culturally targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments.