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Towards a quantitative framework for detecting transfer of learning

Authors
Mr. Matthew Galdo
The Ohio State University ~ Psychology
Dr. Brandon Turner
The Ohio State University ~ Psychology
Prof. Mark Steyvers
University of California, Irvine ~ University of California, Irvine
Abstract

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.

Tags

Keywords

learning curves
transfer
practice laws
measurement

Topics

Learning
Discussion
New

Thanks for the interesting talk. How is the transfer across less related tasks? Is this still above chance? I.e. a general transfer ability?

Prof. Gerit Pfuhl 0 comments