Beyond Supervision: Human / Machine Distributed Learning in Learning Sciences Research

Machine Learning (ML) is at the core of a new set of methodologies that are currently taking the world by storm and that have a great potential to advance research in the learning sciences. However, research has mostly focused on applying top-down methodologies effectively aiming at replacing humans. However, this hinges on the assumption of scale effects and transferability of trained ML models across populations – assumptions that may not hold in learning sciences research. We discuss the potentials and pitfalls of supervised and unsupervised ML for the learning sciences and argue that the greatest benefits from the use of ML lies in supporting humans so that researchers can tap into new data sources and enhance the validity of their inferences.