Knewton CEO Jose Ferriera has an interesting and revealing blog post up about “the coming adaptive world.” In part, it is a response to a report on adaptive learning by Education Growth Advisors. Jose writes, “Despite our constant protestations to the contrary, observers often confuse Knewton with the many adaptive learning app makers who are now popping up. Or they confuse app makers with platforms. Or they think we’re all competitors.” It’s a bit of a red herring, since the report does distinguish between platform and publisher business models. That said, the meaning of the distinction between these two categories isn’t drawn terribly clearly, and it’s fair for Knewton to try to clarify its market positioning. But in doing so, Jose reveals what appears to be a shift in their thinking about the market for a platform like theirs which tells us something important about the ed tech market in general.
Knewton has always been a platform play. They don’t design educational products. They provide an analytics engine that can be used to make educational products. So they are business-to-business. They sell to other education companies. The value proposition they offer is that they have invested in data science talent and infrastructure that is more powerful and sophisticated than most education companies can manage. It’s a bit like Amazon saying, “Hey, you’re never going to have even a tiny fraction of the experience that we have running unbelievably massive systems that can never go down. Why don’t you just leave that part of things to us by using Amazon Web Services and focus your attention on building the parts of your product that are specific to you?” This is a reasonable pitch for a company like Knewton to make, in my view. The issue that I have had with the company’s public marketing is that there has been a little too much “WHEEEEEEE!” in it:
I think there is a certain ethical responsibility to demystify these technologies in order to help educators and students alike understand when and how they can be helpful. I also think that demystification makes good business sense from Knewton’s perspective. The company simply isn’t going to get good results (and therefore repeat engagements) by hooking up random customer content to their analytics engine. They need content that has been designed for analytics in some real sense in order to produce meaningful insights. They need customers to come to them having some idea of what capabilities they want to design into the product from the beginning.
And that’s where Jose’s post gets interesting. He writes,
Sure — it’s straightforward enough to wire up a simple, self-contained adaptive app, based on a pre-determined, limited decision-tree. But how much better would that app be if it contained an effectively unlimited amount of back-end content? If all of its assessment items had been algorithmically “normed” so that they resulted in exact concept proficiency data for each student? Or if the app pre-acted to the learning modalities of each student? Or if it “started hot” so that from Day 1 of a student taking a new course, all her prior concept proficiencies and learning styles had been preloaded?
Knewton makes possible all these things and more. Today, Knewton functionality includes pinpoint student proficiency measurement, content efficacy measurement (yes, we can tell you how effective your content is), student engagement optimization, atomic-concept adaptive learning, and concept-level analytics. Next year we’re adding “adaptive tutoring,” which combines the wisdom of crowds with Knewton’s network to find the perfect people online right now to give you real-time help.
Hmm. Assessment items being “algorithmically ‘normed’ so that they resulted in exact concept proficiency data for each student?” “Pinpoint student proficiency measurement?” Gee, that sounds suspiciously like Item Response Theory. And if you can find your way past Knewton’s marketing to their tech blog, you will find out that, in fact, Item Response Theory is exactly what Knewton uses for this. Still missing is a straightforward explanation of what ITR can and cannot do well as well as the kind of content design investment that Knewton’s customers would have to make to take advantage of this capability. It’s not as simple as sprinkling a little machine learning fairy dust on your content. Customers that come to Knewton without that understanding of the investment they will need to make are going to end up spending a lot more time and money than they anticipated. But the larger point is that framing specific capabilities that Knewton customers can think about in advance is a start toward positioning themselves as a real infrastructure platform company. Likewise, “adaptive tutoring,” which appears to be a whizzy name for expertise recommendation, is a specific function that app designers can think about when they are building out new services, whether it is math tutoring or college counseling or career counseling. This positioning begins to enable app developers to think about what they can do with learning analytics services. Jose writes, “Until recently, only large learning companies and university systems could use the Knewton platform. But now our enterprise API is flexible enough for a much wider audience. We’re happy to partner with anybody — even so-called ‘competitors.’ We can’t quite say “yes” to everyone who wants to work with us yet, but our capacity is growing by leaps and bounds every day.”
And there is the pivot. Up until now, Knewton has been focusing on the big publishers—particularly Pearson, with whom it has a big partnership deal. One reason for that certainly could be that their APIs were not ready for smaller players before now. But I suspect another driver is the huge growth in ed tech startups in general and companies claiming to have some sort of adaptive learning products in particular. Arguably, a market exists today where one didn’t exist a couple of years ago. I say “arguably” because it remains to be seen whether this onslaught of small companies is just the result of an investment bubble or a sustainable trend. Most of these companies are never destined for IPO, and it’s not clear what the long-term appetite is for acquisition in this sort of volume or, lacking that appetite, how many of these companies are geared up to be small but self-sustaining businesses for the rest of their natural lives. (The fact that so many of them are looking for venture money is not a good sign.) In any event, an analytics infrastructure like Knewton absolutely could make many of these small companies potentially interesting and sustainable on significantly less startup cash by providing them with infrastructure, in the same way that AWS makes it easier and cheaper for all sorts of internet startups to form. But in order to become that sort of trusted backbone, they have to stop talking like magicians and start talking like infrastructure partners.