As chief product person of the Babbel language corporation, he talks about informal learning at scale and will offer insights. 750 people all working on 1 app, fully funded by individuals willing to pay small amounts of money to learn languages. Mostly Euro-centric coming from the organic growth of the organisation.
5000 courses => 64000 lessons (unique language pairs), focus on communicative confidence, light-hearted, diverse topics. Well over 1 million subscribers (of which I am one - Spanish).
Digital = scale and reach
Team of 10 people can start the magic of the web.
How can we ensure Quality?
Learner centric, otherwise what is the value of the application?
Using a learner journey to unite efforts, to enable connections between learners. Conceptual flows of individuals that is used as the mantra to move the app forward.
See picture, where they also embed some spaced learning.
They work with patterns that are turned into fake persona's, which are designed and modeled (design thinking approach). Enabling developers and strategist to understand the different demographics. These personas are linked to learner journeys. Which enables to keep a focus on the learners.
Learning from the learners
What do they do? analytics, A/B tests, behavioral segmentation (showing what you did, signposting to what you did and worked...), interviews, intercept surveys, wishboard, market surveys, UX research (ask permission to video tape parts of the learner journey and ideas), customer service, market research. Not one is representative, but hoping that with enough different angles they are hoping to get closer to the actual learning in all it's complexity.
Dev at scale
20 different teams of people, a lot of independence, but only one product. So how likely it is that the releases are synchronizable as soon as they are launched by teams? Tripping over each other, contradictions, ...it quickly becomes chaos. So it is self-driven and autonomous, but potentially disastrous for the learners. Marketing and money was basis for scaling: stickers in planes and on poles in big cities, get people to pay a bit of money.
How do you trade off freedom versus working together
Teams organised around User Journey: Experience Groups (XGs) are clusters of teams across Product & Engineering, uniting tho enhance cross-functional collaboration around product ideas and speed up the development cycle: impressions, engagement, learning, learning media, platform and infrastructure (really interesting this!).
Product department
Product is made up of many specialist teams. some teams are embedded within multi-function or engineering teams: didactics, product design, product management and QA, data engineering and analytics, quality and release management.
Towards "learning experience design"
Mixed multidisciplinary approach, but in larger companies most of the time they are not often set up as bridged teams in a multidisciplinary, cross-functionalness.
Babbel meetups in Berlin every 2 - 3 months, welcome to come and have a look.
LXD basics
digital learning is not content distribution, we are only a small slice of our learner's day, we never really know what is going on. Learning Experience Design, all about the multidisciplinary nature.
Learner engagement
It only works for them if they use it. What is the science of pulling learners back in?
Weekly active paying users: returners. One of the key drivers = 7 day return to learning (it is this that most of the dev teams use to validate short term impact of new features and refinements). If the people who try a new release, do they come back within 7 days to use this newly released option. This simplifies discussions on what is important.
Obsessive focus on interpreting events: Tableau, Amplitude (big fat data stream).
Mixing art and science to understand the engagement ladder (to help our learenrs focus - hooked (N Eyal) triggers motivation (Fogg), Nudge (Thaler, Flow state, spaced repetition, babbel qualitative and quantitative data....).
Gamification: treat with care, some very useful tools, often used for trivial impact.
AI to make Babbel more human
AI is a very broad umbrella term for a wide range of very specific disciplines. Babbel uses 'narrow AI' to focus on very specific problems/opportunities. NLP, CL, ASR...
Making interfaces more human (hybrid human-AI). Using NLP to give the automated feedback more human (eg "I understand what you meant").
Making guidance more useful: content recommendations, based on other, related topics and level. Still very much in beta. Optimising for speed, and identifying opportunities.
Rose Luckin's golden triangle is used.
Tutorbot corpus (Kate McCurdy, Dragan Gasevic...)
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