Instant Messaging for Language Learning.
Using instant messaging mechanics to disrupt traditional language learning practices.
We’ve all said it, “I want to learn a foreign language”, and yet how many of us actually get further than a few interactive online courses or audio lessons before our attention wanders?
In a world full of mobile devices and applications all vying for our attention, clearing out an hour here and an hour there for distraction free learning can be a very difficult task, and that’s before we’ve even started to learn. If you’re anything like me, you need to be in the right mindset to focus on a language course for 30–40 minutes. If not, then it all goes in one ear and out the other.
So instead of fighting to free up a large chunk of time in a busy schedule, when your mind may be elsewhere, why not break that chunk of time up into more bite-sized manageable pieces. After all, we do this every day when communicating with friends, family, and co-workers via instant messaging platforms, such as iMessage, Hangouts, WhatsApp, and Slack, to name but a few.
We don’t allocate ourselves 10 or 20 minute slots to write letters to each other every day, we would never find the time. Instead, we focus fully on reading and replying to instant messages in short bursts, and in the process, maintain multiple conversations over extended periods of time.
Our conversations are divided into manageable chunks, scattered along perpetual timelines. These timelines, when added up, can be immeasurable in length. Without even thinking about it we are investing vast amounts of time communicating by splitting the process up into many small chunks. Just imagine what we could achieve by applying that same process to language learning.
Building a network of quality and trust
One of the most important aspects of such a network built for language learning, is the level of quality. In order to create a reliable and valuable network of contributors requires a system to measure user reputation and credibility. Like eBay’s approach of assigning feedback on a user’s quality of service, a similar process would be required to rate each user’s contributions, as both student and teacher.
Here is an example of a theoretical user. The profile card displays his name, his native language, and the language he wishes to chat in. The yellow heart and adjacent number represent the user’s credibility. With a total of over 1500 points, the user has a good reputation, and will likely make a good partner to share conversations with.
Reputation would be an integral part of such a free and open learning environment. In order for users to find high quality members of the community to learn from, they would need a way to surface the best suitable conversation partners from a potentially vast network of users.
A Resource for both student and teacher
Not only can we connect with native speakers of foreign languages for our own learning, but we can also provide that same support to others who wish to speak the languages we ourselves are proficient in.
One to One
Let’s say, for example, that I’m a native English speaker who wishes to learn Japanese. I may choose to connect with a native Japanese speaker who in turn wishes to learn English. Together, we can take part in one-to-one conversations, across one or more separate threads. Some entirely in English, some entirely in Japanese, and other in a mix of both.
In this example, both parties benefit from each other’s contributions, taking on the role of both student and teacher, making it a worthwhile endeavour for both sides.
One to Many, Many to One
As a teacher…
If I only wish to interact as a teacher and aid others in their language learning, then I could choose to take part in several exclusively English conversations, with users of varying nationalities.
As a student…
Alternatively, I may choose to engage solely as a student. Taking part in several different foreign language conversations concurrently, but not contributing any time to conversations in my native language for the benefit of others.
Such a choice would not be particularly beneficial to the network as a whole, and in turn would restrict my ability to gain credibility on the network over time, subsequently reducing my appeal as a conversation partner. These diminishing returns would therefor incentivise me to build a better reputation on the network, in order to engage in higher quality language conversations for my own benefit.
Many to Many
Last but not least, if I know more than one language and wish to improve my skills across a variety of languages, then I could contribute and participate in several conversations across multiple languages concurrently.
This trade-off would have the potential to create a rich network of language learners. A network far greater in value than the mere sum of its parts.
Perpetual vs Ephemeral content
One question that would require a great deal of thought would be that of data storage and data protection. Language learning is a by-product of the conversations that take place on the network, so would we necessarily want to store conversation data for future reference? After all, the process of language learning is in the practice of conversation, not in the referencing of previous activities.
Here today, gone tomorrow
On one hand, the concept of ephemeral messages — conversations that erase themselves after a short period of time — would solve a great deal of issues with regards to privacy and storage across a network of audio conversations. Storing this data would require vast amounts of space, not to mention the additional support, maintenance, and security, such a system would require.
An archive of lessons, searchable in an instant
On the other hand, in certain circumstances, there might be a case for a searchable archive of past conversations. Old messages could be stored out of sight, while still being searchable in an instant. Allowing the user to refer back to valuable conversation history with the tap of a finger.
…or maybe a bit of both
In everyday life, unless purposely recorded, a majority of our conversations live only in our memories, and not stored on our computers and mobile devices. Verbal communication is ephemeral by nature, but there are still times when we wish to record moments for future reference.
So maybe the answer to the storage question is a bit of both. Message histories are ephemeral by default, but with the ability to save valuable or noteworthy exchanges for future reference. Either within the network at a cost to the developer, or my personal preference, downloadable in an open format, such as collection of mp3 files that can be stored and played back by at the user’s digression.
Wider use cases
I have chosen language learning as a use case for instant messaging mechanics within education, but that’s not to say the process couldn’t be applied across a wider variety of teaching and learning disciplines. If you know of any other disciplines that might benefit from the instant messaging model, then please share your thoughts as a note on this paragraph. Or better still, if you have experience using this technique within a learning environment, feel free to write a response to this article below.
About the author: Alex Masters is a Learning Technologist based in the Disruptive Media Learning Lab at Coventry University, England.
About the Lab: The DMLL is a semi-autonomous cross-University experimental unit whose remit is specifically to drive innovation of teaching, learning and practice forward (in the ‘Google model’: to break and remake existing ways of doing higher education) so that the University can re-model its own practices. For more information, please visit dmll.org.uk.
