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Supporting the art and science of Montessori with technology

Have you ever wished you could clone yourself, so that one “you” could carry out your work while the other “you” could just focus on carefully observing and learning? Having heard a version of this from enough Montessori teachers, we started to wonder if we could use technology to give teachers this superpower. This is one of the questions we are exploring today as part of our commitment to making Wildflower schools a platform for innovation and learning what we can share with the world.

Observation is at the heart of the Montessori approach that defines Wildflower schools. Teachers observe students as they go about their work, take careful notes, maintain meticulous records about student engagement and progress, and regularly reflect on all of the above in order to build their understanding of each student’s unique development path and plan their actions in the classroom. We think this whole suite of activities is going to become even more important as we seek to serve a more diverse group of students with a broader range of starting points and learning trajectories.

However, we face a fundamental limitation, which is that a teacher can only devote this kind of careful attention to one student (or, at most, a small group of children) at a time. A typical Montessori classroom might have as many as 25-35 children, all making independent choices and engaging in different kinds of work at any moment. Some teachers respond to this challenge by introducing testing or other sorts of student-facing educational technology into the classroom, but this can undermine the Montessori approach and steal time from more valuable activities. We began to wonder…what if there is a third way?

Our idea, pioneered by Nazmus Saquib and others at the MIT Media Lab and being piloted since January in three classrooms, has two interlocking components:

First, the prototype uses wireless sensors to gather fine-grained information about activity in the classroom as the students naturally go about their work. We can track a student’s engagement with Montessori materials, their movement about the classroom, their interactions with each other and their time with teachers. The sensors are silent, unobtrusive and largely invisible, embedded in the shoes of students, the materials that they work with and zones of the classroom.

Second, we are building a growing stack of analytical and machine-learning layers to help teachers process and make meaning of the resulting information: translating raw sensor data into educationally meaningful events and aggregating those events into records over time. The technology helps teachers analyze trends and patterns to give them insights and guide their practice. It leverages those patterns to make predictions and recommendations about the future development path of students.

The idea is not to replace teacher observation and judgment (which technology could never do, in any case), but rather to support teachers with the kinds of insights that technology can produce so they can focus their energy on the work that experienced Montessori educators are uniquely capable of. If the technology can consistently capture basic facts like which materials the students are engaging with, which students are working with which other students, etc., it might allow teachers to refocus their observational energies on things like whether the student is showing signs of joy or frustration, the particular places where a student is getting stuck, etc.

We are encouraged by what we’ve learned so far. In preliminary testing, we’ve been able to accurately capture interactions between students, materials, teachers and areas of the classroom. Teachers have responded positively to our early efforts at helping them make meaning of the data. However, the real test of these ideas is whether we can make them useful in everyday classroom practice, whether teachers are able to do things with the technology that they could not do without it and whether experiences and outcomes for students improve.

That is the journey we are embarking upon with the pilot we kicked off in January. We are deploying the technology in three classrooms for an extended period of time, and we will be working closely with teachers to understand whether and how they make use of the information, to evolve the tools we are giving them to make meaning of the data, and to identify where we need new kinds of information and new sensors to form a complete picture of what’s going on in the classroom. As we learn, we’ll continue to share our progress in this space. Stay tuned.


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