Making Classroom Learning Work for More Students

Making Classroom Learning Work for More Students

By John Mullin

The CEO of Enlearn argues that “generative,” adaptive learning, or a digital curriculum that can adjust in real-time to the needs of individual students and teachers, is the future of education in America. Will his company’s product help keep us competitive?

With one out of five students not finishing high school, our current models for school-based learning still aren’t working for too many of our youth. Enormous amounts of effort and money have been expended trying to address this—to leave no child behind—but the challenge persists. And our collective failure to solve this problem steals opportunity from millions of students every year.

One area receiving a great deal of focus and investment that could potentially address part of this challenge is adaptive learning—digitized curriculum and courseware that can adjust or adapt what comes next for each student based on their degree of mastery with previous work. Unfortunately, efforts to successfully personalize learning through adaptive curricula will fail many students for several key reasons:

  • They are designed for individual students working in isolation on a computer or device, but that’s not how kids spend their time in schools. Classrooms are dynamic (and sometimes disruptive) environments with many, many variables that affect each student’s learning every day. To truly optimize learning, you need to optimize the entire classroom ecosystem.
  • The exercises and problems within any one adaptive curriculum are limited to those chosen by a particular group of authors, editors, and publishers—their best attempt at a one-size-fits-all subset for each content area. But this is still a small subset of all the problems that could be presented, which limits the learning paths that can be traveled, and, therefore, the students for whom a given adaptive curriculum will be effective.
  • They focus only on student mastery. Did you get the problem right? If so, you move on; if not, you repeat or go back. But this gated approach ignores the deep-engagement measures and growth mindset needed for students to actually want to continue—to foster sustained learning and progress.
  • Current adaptive methods ignore the critical role of the teacher, who happens to be the single greatest determinant of student success inside of schools. If you want to help struggling students succeed, then adaptivity needs to be designed to enhance teaching, not bypass it.


We can do better—by harnessing the power of real-time classroom data to not just adapt, but to actually create, curricula unique to each student and each specific classroom. We can adapt, not just for one student working in isolation on a computer, but also for the entire classroom working with their teacher, together, in real time.

This breakthrough technology—which we call generative adaptation—doesn’t just re-organize content like a playlist for each student, it generates new content to fill in the gaps in a curriculum—it makes each curriculum virtually infinite. And then it continuously identifies and refines personalized pathways through that courseware—pathways that optimize both engagement and mastery for each learner.

Because this approach is capable of adapting in real time for every student in the classroom, and filling in any content gaps for each student, we can now deliver one-size-fits-one—a personalized curriculum that is continuously created for and adapted to the individual.


Why does generating new content matter? Why is it better than simply enabling existing content to be re-organized per student?

To our knowledge, no single text or courseware has been able to cover the myriad learning paths and progressions best suited for every unique student, regardless of how the content is sequenced or adapted. If such texts existed, we wouldn’t have math wars, or battles between whole language and phonics, etc. Those battles occur precisely because the experts creating the curricula disagree on the subset and sequence of content to be delivered to students—and they don’t have the means to include all possible learning pathways and progressions, so they make choices based on their pedagogical beliefs/preferences. It’s their best effort at one-size-fits-all.

By definition, this choice leaves out potential learning pathways and progressions, which means that some subset of students will be forced through a sub-optimal learning path for their specific needs.

For decades, we’ve accepted this as the best we could possibly do. We don’t have to accept it any longer. Through technology, we can now deliver the best path for each student—again one-size-fits-one.

Here’s an interesting illustration of the potential: Recently, our partners at the Center for Game Science at the University of Washington found through their algebra challenges that nearly 95 percent of students participating were able to reach concept mastery of linear equations using the generative adaptive version of the content. But some students needed as much as six times the practice to reach those mastery levels. Furthermore, the additional practice material that any two students required was different—it was specific to each student’s unique learning pathway and progression. This means providing six times the content, tailored specifically to the moment-by-moment learning challenges of each student. A fixed text is incapable of generating any new content, let alone six times the content.

By providing the generative adaptive version, 95 percent of students were able to achieve mastery, versus about 30 percent in the non-generative version. If we have the ability to more than double the number of students achieving mastery in a given concept, we not only have the opportunity to dramatically change learning outcomes, but the responsibility to do so as well.


A static or fixed text is no friend of the teacher. Formative assessment has been widely shown to be one of the most effective teaching strategies for increasing student learning, yet the information available to teachers from traditional texts is limited to results on worksheets or homework or quizzes—there is little or no real-time data provided to help the teacher understand whether or not students are learning in the moment.

With the generative, adaptive technology model I’ve discussed, teachers now have access to continuous, real-time formative data. Not after grading the assignments for the day, or after the unit test, but as a window into ongoing student learning—how is each student doing right now? What could or should I do to have the greatest impact on their learning right now?

In a recent trial, our self-adaptive platform’s real-time data enabled teachers to assist individual students three times more frequently than occurred in traditional paper-based classrooms. The continuous formative feedback also enabled the teachers to target their assistance to the students who needed it the most at that moment, versus a more random delivery of assistance in the traditional classrooms.

But none of this can happen with a static or finite old-fashioned text—whether it’s adaptive or not—because this restricts the number of potential learning pathways for a given concept or subject, and it’s limited to the content and progressions agreed upon by a particular group of authors, editors, and publishers. Again, simply re-ordering static or fixed content doesn’t address the needs of all learners.

So, to cover all potential pathways, and ensure that every student gets his or her specific learning needs met, content must be generated and adapted in the moment. Over time, this self-adaptive technology platform will improve with each additional learning experience captured. And, as the platform continuously and automatically adapts based on real-time data, it accelerates the potential rate and degree of engagement and mastery in every learning opportunity.

The bottom line here is simple: We now have the ability to finally blow apart the one-size-fits-all model of content and learning, and to replace it with a truly personalized learning experience for each student, classroom, and teacher. If we want to help an entire generation of young people learn in the 21st century, we need to make schoolwork for the kids who are being failed by the current model. Generative adaptation has the potential to help solve this—to create learning pathways created for, and specialized to, each learner.


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