Towards a quantitative framework for detecting transfer of learning

Transfer of learning refers to how learning in one context influences performance in a different context. Because tasks are rarely performed in isolation, a well-versed theory of transfer is paramount to understanding learning. Yet, a thorough understanding of transfer has been frustratingly elusive, with some researchers arguing that meaningful transfer rarely occurs or attempts to detect transfer are futile. In spite of this pessimism, we explore a model-based account of transfer. Building on the laws of practice, we develop a scalable, quantitative framework to detect transfer (or lack thereof). We perform a simulation study to explore, under what conditions, can we detect transfer and the recoverability of the model. We then use our modeling framework to explore a large-scale gameplay dataset from Lumosity. Preliminary results suggest our model provides a reasonable account of the data and that the added complexity of transfer is justified.