We Should Be Teaching Our Students Like Yoda Taught Luke

Yoda and Luke Skywalker in The Empire Strikes Back.  Lucasfilm Ltd./Everett Collection

In George Lucas’ classic The Empire Strikes Back, Yoda patiently tutors, challenges and imparts his wisdom to Luke Skywalker deep in the Dagobah swamps, sometimes while perched on the shoulder of his young Jedi-in-training. That scene depicts the Platonic ideal of personalized learning.

It’s an approach to teaching predicated on keen understanding of an individual student’s strengths, weaknesses and goals. On creating a learning path customized to a student’s unique needs. On competency-based milestones (Luke must levitate stones before he can try levitating his X-Wing fighter). And on flexible learning environments, where a student’s particular needs dictate everything from the way space is used to how time is allocated.

Personalized learning isn’t the latest fad in education. In fact, it’s an approach that great teachers have long used to ward off drudgery, to challenge students to do their best and to infuse their classrooms with energy and a sense of possibility. Thousands of years after Socrates engaged and challenged his student Plato with dialogues that inspired him to write The Republic, Charles Babbage encouraged Ada Lovelace to use her mathematical talents to create what is now considered the first computer program.

The next challenge is to use what we already know: that tutoring and learning for mastery can make a dramatic difference for individual students, to help discover how to deliver that kind of rich, personalized learning experience to millions of students.What’s different today is the pace at which the momentum for personalized learning is building, thanks to a combination of cutting-edge technology, a growing body of research and a wider appreciation for the power of this approach to teaching and learning. Taken together, these catalysts have helped grow personalized learning from a sporadic practice to an insurgent philosophy.

There are tantalizing clues in places like MS 88, a public middle school in Brooklyn, New York, where three years ago, an organization called New Classrooms started implementing a math program called Teach to One. Using custom software, Teach to One draws on assessment data and information about MS 88’s resources to help teachers suggest a daily, personalized lesson for each student.

Every morning, MS 88 students consult TV screens to see their personalized learning plan for the day. Teachers have designed their classrooms so that students can learn in any one of eight settings, including teacher-led environments, peer-based lessons and even independent study.

Teach to One is in 17 schools serving 6,500 students in nine school districts. It’s now moving from a pilot phase to version 1.0 of the model, and the early results are intriguing: last year, students on average made 1.5 years of progress in math or 47 percent better than the national average. Even more impressive, the students who started with the lowest scores in math made the biggest strides.

How to Teach Like Yoda

My team at The Gates Foundation believes that personalized learning is key to reaching our goal of getting every single high school student ready for college. After working on it with many partners around the country, we’ve come up with a list of four best practices for personalized learning.

The first is a unique and up-to-date learning profile of every student’s strengths, weaknesses, motivations and goals. At Summit Public Schools in California, for example, custom software documents each student’s content and skills mastery, interests inside and outside the classroom and future aspirations. Students and teachers use this information to create a Personalized Learning Plan and to set weekly goals. Students, their parents and their teachers can access the online profile at any time.

Second, personalized learning students follow a path through the curriculum based on their unique profiles. At Acton Academy, a private elementary school in Texas, students work with their teachers to plot a personal “journey” based on their needs. Students choose the types of technology they use, can adopt new tools as they find them, and are encouraged to cast a wide net to learn what they need to, whether on the internet, from each other, or even by picking up the phone and calling an expert.

The third practice of personalized learning is competency-based progression. In Teach to One classrooms students master the skills on their personal learning path through online or live instruction, task sessions involving real-world applications of the material, and project-based sessions. Every day, students take an assessment on the day’s content that determines whether they can move on.

The fourth practice of personalized learning is flexible learning environments. In these learning environments designed and managed by teachers, teachers have the flexibility to tailor instruction to each student’s individual needs, skills and interests, determining the most appropriate structures, tools and methods to best help each student. For example, the classroom walls at Nolan Middle School in Michigan have been removed and the desks replaced with modular furniture. Students choose which subjects to work on during schedules divided into large blocks of work time. Two teachers monitor groups of up to 50 students; two others use real-time data to identify and work with students who need extra help.

Schools of the Future

The schools and teachers really innovating with personalized learning are pioneers helping discover what works, what doesn’t, and how to make everything work better. And they are showing us what schools of the future might look like. In an ongoing study of 23 schools adhering to at least one of the four key practices, we are seeing encouraging interim results.

There are some limitations to the study: the sample size is small; we have only two years of data from high-achieving charter schools (not yet a broad cross-section of schools); and it’s hard to isolate personalized learning as the driver of the students’ performance. In spite of those caveats, the takeaway finding is clear. Based on student achievement data, students made gains in reading and math that are significantly above average, when compared to similar students and schools where personalized learning is not happening.

The study will continue into next year, and there will be more studies as we continue to build the body of evidence, but it looks like working to make sure that students and teachers have the tools and resources to create a personalized education does indeed lead to more and better learning. Now, we have to go faster and further so we reach more students, sooner, with models that we know work.