What is Wildflower?

Wildflower Montessori is an open-source model for shopfront Montessori lab schools. These are single classroom schools, each having on the order of two teachers and twenty children, decreasing the costs of administrative overhead and increasing teachers’ control and autonomy.

They are also lab schools that work to push forward the Montessori philosophy through developing materials to teach modern fluencies and designing mechanisms for technology-enabled qualitative observation. They create a mixed-age environment that includes room for adults doing creative work and parents learning about the Montessori philosophy. And finally, they serve as change agents for the city itself, working to make the city friendlier to children and families rather than working to create contained campuses. Wildflower Montessori is an open-source model, and several schools have been developed based on this model and serve as demonstration schools for the philosophy.

Design Principles

An Authentic Montessori Environment: providing a peaceful, mixed-age, child-directed learning environment.

A Shopfront, Neighborhood-nested Design: committed to remaining small, integrated in the com- munity, and responsive to the needs of the children.

A Lab School: serving as a research setting dedicated to advancing the Montessori Method in the context of the modern world.

A Seamless Learning Community: blurring the boundaries of home-schooling and institutional schooling by placing high priority on parent education and giving parents an integral role in the class- room.

An Artist-in-residence: bringing richness to the learning environment by giving the children oppor- tunities to observe and interact with adults doing day-to-day creative work.

An Attention to Nature: emphasizing the nonseparation between nature and human nature through a unique living-classroom design and extensive time in nature.

A Role in Shaping the Neighborhood: working with the community to improve local parks, streets, and establishments to create an urban environment that is healthier for children.

A Spirit of Generosity: seeing school as a change agent for society, and reflecting a spirit of generosity internally by setting aside a substantial part of the tuition base towards financial aid, and externally by being an open an involved member of the surrounding community.

An Open-source Design: advancing an ecosystem of independent Wildflower schools that mutually support one another.

Learning Materials

Maria Montessori designed a large number of educational materials which are central to the Montessori Method and remain relevant today. In the modern world, as fluencies like computer science and design gain more importance than they did a century ago, we may imagine new materials that complement Montessori’s original materials. The following pages show some such materials.

Montessori materials are intended to be:

Beautiful

Attention to beauty is a hallmark of Montessori materials. They appeal to the child’s senses through the use of color, shape, and texture.

Simple

A Montessori material teaches one concept at a time in its simplest form, therefore isolating the challenge.

Self-Teaching

A built-in “control of error” frees the child from the external judgment of either praise or criticism.

Allowing for Repetition

The materials allow children to complete a task once or many times depending on their level of interest and stamina.

Sequential

The Montessori curriculum carefully orders and sequences materials. This allows children to scaffold new skills and concepts on previous experience and pre-existing knowledge.

Wildflower Social Utilities

Starting a school is a social process. There is the process of finding a space, the process of matching teachers with parents, the process of designing a logo, etc. These processes can be made easier with software. We have been in the process of building social software to help parents and teacher-leaders to make it easier to start a Wildflower school. We call these Wildflower Social Utilities. Here are a few examples.

Classroom Visualizations

Observation is a key component of the Montessori Method. Teachers are trained to be constant, conscious, and astute observers of the child. You will often see the teachers in a Montessori classroom taking copious notes.

We now have technologies that can allow us other methods of observation and analysis to complement the traditional ethnographic methods used by Montessori teachers. As an example, we put video cameras in the ceilings, which, along with image recognition software that we have written, determines what area of the classroom the child is in, and what material each child is working with. Because different classroom areas correspond to different curriculum areas in Montessori, and because each material teaches one thing, this data creates a clear picture of what the child is learning, and when. We may visualize this data to give a detailed picture of the child and the classroom to the teachers and parents. The following images are data visualizations that depict the learning activity within the classroom.



Wildflower Montessori School Visualization: Floor Plan

Materials and Sections, Individual Child View









Volume and Interaction, All Children over the Day







Volume and Interaction, One Child’s Tracks Weekly or Daily



Mapping Education

Learning and Poverty, New York City

This map visualizes the relationship between poverty and school performance in New York City. New York is divided into 32 school zones across 5 boroughs, and students can choose to attend any school in their designated zone. This map looks at the percentage of students that are on free or reduced lunch, and compares that to indicators such as teacher turnover, graduation rate, teacher experience, teacher pay, and test scores. For a child to be eligible for free or reduced lunch, they must come from a family whose income is at 85% of poverty level or below (equivalent to $43,568 or less for a family of 4 annually).

What we find is that schools that have high levels of poverty tend to have fewer certified teachers, fewer experienced teachers, more teacher turnover, lower test scores, and lower graduation rates. We do see that in these zones, there is a higher investment per student, but not high enough to address these issues.

Across the district, the teacher turnover rate is quite high. In many zones in the Bronx and Brooklyn, the teacher turnover rate is over 20% per year. And every zone has a teacher turnover rate of over 10%.

Also, across the district, many children are on free or reduced lunch. In Flatbush, Central Brooklyn, and Bed-Stuy, over 80% of children are on free or reduced lunch. In Bushwick, Ozone Park and Howard Beach, that number is over 90%. And even in neighborhoods like the West Village, the number of children on free or reduced lunch is over 50%. This is a remarkable number in a neighborhood where private school tuitions can exceed $50,000/year.

Finally, one cannot look at the underlying data without seeing effective racial segregation in our schools. This is a big enough topic that it warrants its own map, so we will give the topic a more appropriate treatment in a later map and didn’t include the data in this visualization. However, it is worth mentioning here that the numbers show a striking difference in racial demographic makeup of our urban public schools as compared to suburban public schools and urban private schools.

In other words, the story of our schools is one that cannot be told without telling the stories of class and race in our cities.





School Locations, Washington, D.C.

This map suggests optimized locations for new schools in the city of Washington, D.C. based on populations density data. The algorithm behind this visualization informs the process of school location on actual population needs. This in turn can help cities lessen the burden of daily commute, associate places of learning with neighborhoods and respond to real-time demographic changes.

Existing schools are marked in black while proposed schools are colored. By changing the number of required schools the map will generate different solutions.

The process of building a new school is complex and lengthy, and once built it is constantly challenged by the natural process of aging neighborhoods and shifts in demographic dynamics. We view this map as a tool for an urban reality where school-making is an agile process. In this reality, schools would be smaller, and they would be assembled when needed, and change use just as easily when neighborhoods change.

We used k-means clustering to assign students in Washington, D.C. to schools. The existing schools act a fixed means while the proposed schools are free to move around. This ensures that the proposed schools are located such that the total distance needed to travel by students is a local minimum.





Children, Cambridge

This map visualizes the number and distribution of children aged under 18 in the city of Cambridge, Massachusetts from 1970 - 2010.

The prominence of school-aged children in cities and neighborhoods is something that changes over the decades. This map helps show how the distribution of that population has changed over the past40 years in the hope that this data can be used to make smarter decisions about where to place schools and other resources for children. On a macro-level, the pronounced and continued reduction of the population of children in the city over the past 40 years makes us question whether we are creating our cities to be less friendly to children and families.

You can explore this map on three levels: city, year, and census tract. Each child as reported by the US Census is represented as a single dot on the map. Clicking through the years in the graph at the top of the page allows you to visualize this data across each decade from 1970 to 2010. For each year, you can hover over a census tract to see the total population, number, and percentage of children in that census tract. The color of the dots in each tract represents the percentage of the population in that tract under age 18.

The data displayed by this map was generated from 50 years of census data, which gives us a breakdown of the population of each census tract by age group.