Accessing the Montessori Laboratory: Data-Driven Pedagogy

by Liz Monsen, Head Montessori Teacher at LePort Schools

Maria Montessori is famous for recasting the role of the teacher. Rather than an authority figure dictating content to a passive class of students, Montessori pioneered the concept of the teacher as a guide: a facilitator coaching students to become active, mindful agents directing their own learning and growth.

Montessori’s conception of teacher-as-guide is well-known, and many educators today embrace some variant of this idea. Less well-known is Montessori’s other paradigmatic concept: the teacher as a scientist. To be effective, Montessori writes, a teacher must observe — she must work with a scientific, research-oriented approach, and strive for objectivity in her understanding of each particular child under her care:

“The vision of the teacher should be precise like that of the scientist…she has an exact task to perform, and it is necessary that she should put herself into immediate relation with the truth by means of rigorous observation…” [1]

Observation is the central responsibility of the Montessori teacher. As it was explained to me in my own training, if a teacher does not properly observe the children, then her classroom cannot function as an effective Montessori environment [2]. It is through observation and experimentation that the teacher becomes a scientist and puts herself in the service of children.

This transformation — from teacher to scientist — takes place through deliberate preparation. The teacher must learn to view the classroom as an ongoing pedagogical experiment. In this way, each classroom environment works as a laboratory. Considering the classroom a laboratory means that the children are the subjects of the study. Montessori explained that to observe them, teachers must forget themselves and remain motionless and silent. They must see only the children, watching them closely to see what each child does spontaneously, and observing how they engage with chosen work. The teacher must not interfere, but rather create a detailed record of her observations [3]. In this way, Montessori teachers work as scientists, learning from their observations, tracking childhood development, and applying that knowledge to the subtle guidance they offer.

In Montessori schools around the world, teachers actively observe their students and take precise notes, which they then use to continually refine their classroom and curriculum. Unfortunately, this profound work doesn’t translate into the kinds of advances it should make possible. Observations are typically stored in notebooks, often in handwritten form. (In Montessori’s time, during the early 20th century, such methods of recording and storing information were the scientific standard. Since then, the scientific standard has changed radically, but Montessori teachers’ practices have not). The result is that notes often remain stored in classrooms and offices, isolated within each school and within the mind of the teacher who recorded them. Each piece of information must be manually searched for whenever the teacher senses a need for it, and when found, only exists in isolation. A rich database of logs, reports, records, lesson charts, and observation notes becomes largely inaccessible over time. This medium is difficult to maintain, introduces inefficiencies, and ultimately distances the teacher from the information.

There is a possible solution to this record-keeping dilemma: software. As a network of Montessori schools that has a software development team imbedded within it, LePort is in the unique position to improve the collection and use of these observations. With an increasing number of schools, and a population reaching from infancy to adolescence, LePort has the means to consider this kind of data in aggregate. By creating a unified and extensible data model — one that enables the collection and analysis of teacher observations — we can gain a new and useful perspective into our classrooms and students, and put that knowledge in the service of the children we teach.

For instance, we have the opportunity to redefine assessment. Through continuous information gathering, we could gain a more precise picture of student development. Teachers could see summary views of how their classrooms and students progress over time. These powerful tools could offer new ways for teachers to self-assess how they might best support the needs of their students. Scholar Alfie Kohn writes,

“The most impressive classrooms and curricula are designed to help the teacher know as much as possible about how students are making sense of things. When kids are engaged in meaningful, active learning — for example, designing extended, interdisciplinary projects — teachers who watch and listen as those projects are being planned and carried out have access to, and actively interpret, a continuous stream of information about what each student is able to do and where he or she requires help.” [4]

Kohn goes on to explain that for such teachers, testing is redundant. Teachers that have a continuous stream of information are more well-informed than teachers that rely on tests. However, the teacher’s knowledge is not easily accessible to others. It can’t, for instance, be expressed fully in a number or grade.

Imagine if the teacher’s knowledge could be objectively available through data visualization to teachers, parents, and supervisors. It would then be possible to look at trends over time and view each individual student alone, or in relation to peers across schools, cities, and states. We could identify anomalies in both positive and negative directions, and have more insight into the causes of those anomalies. We wouldn’t have to depend on test results that interrupt student learning and undercut their motivation. We would have the opportunity to redefine assessment.

Big data is transforming how many industries make decisions and track progress. In making a commitment to exploring technology-based tools for teachers, LePort has the opportunity to use ongoing passive data collection as an assessment platform. Big data and machine-learning technology could be integrated into a Montessori curricular framework to create new, more informative models of childhood development. In this way, the age-old wisdom of Maria Montessori could become the basis of a new wave of pedagogical innovation.

This type of technologically-informed scientific approach holds significant promise for the Montessori community. It could allow the Montessori approach to be scaled, while still maintaining the deepest commitment to the unique needs of each individual child. Combining information across schools will simultaneously preserve the individuality of each classroom, while leveraging the resources of the larger network. Passive data collection will allow us to draw generalizations about our methods and outcomes without subjecting children to artificial, demoralizing batteries of assessments. Most importantly, we will be able to rise to Maria Montessori’s revolutionary and transformative standard of teacher-as-scientist — and have, as a by-product of our work in the classroom, a powerful tool of human progress.

[1] Maria Montessori, Advanced Montessori Method Vol. 1 trans. Florence Simmonds (Amsterdam: Montessori-Pierson Publishing Company, 2007), 107–108.

[2] Words remembered from course 36 at Montessori Northwest spoken by Primary Director of Training Ginni Sackett.

[3] Maria Montessori, “Suggestions and Remarks upon Observing Children” From London Training Course 1921, Lecture 3. Reprinted in AMI Communications 2 (2008): 17–19.

[4] Alfie Kohn, “Why the Best Teachers Don’t Give Tests” Alfie Kohn Blog, October 30, 2014,

Like what you read? Give LePort Labs a round of applause.

From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.