Smart Classroom Monitoring System Using IoT and Embedded Machine Learning

Abstract

In recent years, education sector specially the classrooms are adapting modern techniques for better mental and alert growth the upcoming youth. This paper presents an IoT-based Smart Classroom Monitoring System that evaluates student engagement using multiple environmental and behavioural sensors. Such a system will help the respective educational institutions monitor student alertness in real-time and optimize the learning environment. There are several factors like sound, temperature, light that effects the classrooms focus, this work computes the focus score of the classroom ranging from 0-100 in Realtime, that represent overall classrooms alertness. ESP-32 was used as the microcontroller, and various sensors were integrated, viz. Sound Sensor (KY-038), Light Intensity Sensor (LDR), Temperature and Humidity Sensor (DHT-22), PIR motion Sensor, Air Quality Sensor (MQ-135), and Ultrasonic Distance Sensor (HC-SR04). Sensor data is continuously processed using a sliding-window technique to compute a Focus Score of the classroom. The results is displayed on an I2C LCD and indicated through a tri-colour LED system. Data is also transmitted to the ThingSpeak cloud platform for storage and remote monitoring. This provides a low-cost, scalable solution to expand classroom monitoring capabilities and provides data to support intelligent, data-driven classroom environments.