We live in an increasingly personalized world. Music subscription services leverage smart algorithms to figure out which new releases you’ll be most interested in, social media sites suggest accounts we never knew existed but end up enjoying, and even our web searches are personalized, leveraging our geography and lifetime behavior to surface better results for every search.
But technology can only personalize but so much. That’s because, in a vast majority of cases, what personalization algorithms actually do is just compare you to other users. Take something like your Netflix queue for example. If you watch a lot of British procedurals, Netflix is likely going to recommend the next season of Father Brown. But it’s also going to compare your behavior to users who, well, behave like you. That like the things you do. So if other fans of Father Brown are watching a documentary on beetles, it increases the likelihood that you too will receive that recommendation, even if you yourself might have a strange and inexplicable fear of beetles.
Now, before you dismiss this as a silly example, let’s follow it through. Netflix’s algorithm doesn’t really know you. It orients you in a constellation of other users, compares, contrasts, and suggests things based on the one behavior it can track: what you watch on Netflix. It doesn’t know about your beetle fear. It doesn’t know your favorite band, the food you like, the last good book you’ve read, or that tonight, you’re thinking about watching a horror movie for the first time in five years because your co-worker told you it was fun.
The point is, these algorithms are just an approximation of personalization. Your best friend or your partner? They know you deeply. They understand you. They know about that beetle fear, what your comfort food is, that you reread your favorite book every January. They actually know you. Netflix just knows an approximation of your taste in movies.
There’s a real benefit to personalization algorithms, of course. They work and they undeniably work better than just surfacing the most popular titles to every title. We all have our tastes and our quirks and while an algorithm can’t describe those, it can point us in the right direction and take us to an outcome–in this case, media suggestions–that are more tailored to our individual personalities than any generic list could be.
Let’s extrapolate this idea a bit to a topic we here at Lingo Live know a lot more about: learning and development. Language apps that improve English vocabulary are all well and good, but they aren’t ideal for non-native speakers looking to navigate nuanced interactions in the workplace. Apps might focus on, say, food or car parts or sports terminology when that’s not what a language learner needs now.
Similarly, programs tailored to specific work-related skills are more personal, but not truly personal. After all, simply taking a course online is a wildly disparate experience for one person than another. We all learn differently because we all are different. An online course or degree in “business English” or a skill certification in “running a presentation” might be better than a catch-all language learning app, but it’s not personal. Even if that program evolves based on your behavior, it’s just still just an algorithm situating you in a universe of similar users. It’s like the Netlix personalization model–better but not best.
True personalization in the learning space can really only come from one thing: another person with whom you have an authentic connection.
We all know this, inherently. Just think about the best doctor or the best teacher you’ve ever had. It wasn’t just that they knew their stuff, it was the connection you had with them. Other people might not have agreed, but to you, to you personally, they were the best.
It’s the same with language learning. Individual coaches who have connections with individual learners deliver better results. Coaches that understand the precise issues that learners have can work on solving these exact issues, not the issues of “users like you.” And as the relationship develops and the learner is more comfortable with themselves and the dynamic, the outcomes improve as well. Personal relationships–again, a truly personal learning situation–have higher baselines generally, but they improve over time much better than apps or online degrees ever could.
This is why we believe so strongly in our approach. We’ve seen it work time and time again for real learners facing real issues in their real workplaces. And the biggest successes we’ve seen invariably come from a deep, personal relationship with their coach. That relationship evolves and accrues all the characteristics other personal, one-to-one relationships have. A coach who knows a learner is more effective because they know both the learner and the curriculum.
When you’re thinking about bringing a learning solution into your organization, keep this in mind. What is it you actually want out of your solution? What outcome? Most companies want their employees to grow and prosper, not just a certification that says they passed a generic course. Smart organizations treat employees as individuals. They know this engineer needs to leave early to pick up his kids from basketball practice, while this saleswoman thrives in chaotic environments, while the QA guy really needs his tea before you talk to him in the morning. They know that these people have different strengths and weaknesses. They react to those and allow space for those quirks and traits. They know that when people are at peace with themselves at work, they excel and innovate instead of just producing with their head down. Employees who are heard and seen and, yes, dealt with as individuals with individual wants and desires simply perform better.
So when you’re looking into a learning solution, ask yourself: does it treat your employees as the individuals they are or like data points in an algorithm?