Capturing how teachers move around the classroom

A teacher’s position in the classroom and proximity to students can have a significant effect on student motivation, engagement, behaviour, and self-efficacy* – all of which influence learning. However, teachers rarely reflect on how they position themselves in the classroom, and what effect that can have on students. Classroom designers and school leaders, in turn, are unlikely to know how classrooms can maximise student engagement and minimise the neglect of students because of where they sit in the classroom.

“We have been working to create visual interfaces that assist teachers in reflecting on spatial aspects of classroom activities.”

Emerging technology uses sensors and computer vision to capture digital data traces of the spatial behaviours of teachers and learners. This presents an opportunity to support teaching and learning, an area that I am exploring with my team at the Centre for Learning Analytics at Monash (CoLAM), in Melbourne (Australia).

We have been working to create visual interfaces that assist teachers in reflecting on spatial aspects of classroom activities. We developed an advanced system, called Moodoo, to analyse the data. Moodoo automatically generates metrics for characterising teaching approaches using positioning data, in the form of x and y coordinates on a floorplan, captured across various learning spaces. This allows us, for example, to compare the time teachers spend with students during hands-on learning relative to a lecture format. We can also visualise and quantify how different teachers deliver the same type of instruction in the same classroom space. One teacher might be highly focused on one area of the classroom, while another might be quite unfocused, moving throughout the room. The orange traces in the images below show how these two different teachers move around the same space.

Combining Moodoo and wearable trackers with learning outcome data can help us identify the most effective ways for teachers to position themselves for certain learning tasks and classrooms. The information we collect can serve as a foundation for programmes to train novice teachers or for professional development. For example, we identified how educators co-teaching in the same science classroom position themselves. That information is now being used to train novice teachers, teaching them to avoid excessive movement about the classroom so that students’ work is not disrupted too often. We also collected data to alert teachers to students who are not interacting with their peers, a problem which might otherwise go unnoticed.

“We believe that a human-centred approach is critical in the design of any educational technology.”

However, introducing even more sensing capabilities into classrooms obviously raises ethical concerns. Data captured from digital learning platforms have already been used to evaluate and even to fire schoolteachers. As sensing technologies become increasingly pervasive and inexpensive, we may soon see them embedded into regular school activities as a way of tracking students and teachers.

For this reason, we are collaborating with teachers on the processing of their data and on creating visual interfaces. We believe that a human-centred approach is critical in the design of any educational technology. Data-intensive support tools must be designed in cooperation with teachers, principals, students, parents, and other educational stakeholders to ensure that they are effectively and ethically integrated into the learning ecosystem. The proper use of these emerging sensing technologies is a major breakthrough for training new teachers, assisting in the architectural design of learning spaces, and identifying spatial behaviours of teachers and students that can improve learning.