Using Machine Learning to Automate Classroom Observation for Low-Resource Environments

Classroom observations are a key component of professional development programs for teachers. While there are many classroom observation systems, these systems are costly to implement and may suffer from biased feedback and Hawthorne effect. Automation of classroom observation processes can potentially help obviate these challenges. This paper presents the design and implementation of an automated classroom observation system based on audio data collected during a class session using an App on the teacher's smart phone. The App automatically labels classroom activities into Stallings-type class observation categories like lecture, classwork, classroom management, practice, question/answer, and reading aloud. Based on the teacher's use of different teaching activities and student performance, the app can provide teachers with intelligent recommendations on how to best allocate class time to various activities. The App used machine learning techniques and was trained on classroom observation data collected from semi-rural primary schools in Pakistan. A variety of machine learning algorithms were evaluated, and using 10-fold cross-validation, the Random Forest algorithm yielded the best accuracy of about 69%. The results show that this approach is a viable and a much cheaper limited alternative to physical classroom observations especially in low-resource contexts of the developing world.