Meaningful IO presents an empiracally testable model of second language learning. The theory draws together several strands of empiracally collected data and puts them under a “social” lense. The motivation for Meaningful IO is to provide a theoretical basis for the design and implementation of highly efficient and enjoyable digital technologies for language learning.
An official statement of the hypothesis can be found here.
Discussion of the many, many contributing factors to language learning performance will also be posted on the project blog.
The Meaningful IO Experiment
See here for more details.
Meaningful IO and Transcrobes
Transcrobes takes a different approach to most of the existing tools available for language learning. Most products, tools and language acquisition research reduce learners to basic “learning machines” - highly abstract, average “learner bots”, that don’t correspond to any actual learners in the real world. Transcrobes considers that real world lives, constraints, motivations and identities are not something to be minimised or ignored, they are something to be leveraged to supercharge learning for all learners, not just those whose lives are closer to those “learning machines” in the minds of the tool makers and curriculum designers.
The goal of Transcrobes is to create a central core that allows many different “learning-optimised” activities to be connected. Different learners will benefit from particular activities more or less depending on where they are in their learning journeys and due to external, short term factors (think weather, hunger, etc.), and it is usually far, far more efficient and fun for learners if we adapt the tools and learning activities to learners’ needs, rather than force learners to adapt to the tools and the teaching plan, like what happens in most environments learners are forced to learn in. Powerful, networked, personal digital devices mean that it now becomes possible to accommodate a far wider range of needs in any given situation, with the right software on these devices.
Now we can optimise for learners, instead of optimising for teachers.