Abstract:
Personal identification is an important aspect in recognizing the identity of a particular individual. A person’s identity is validated through the traditional or biometric methods; biometric technology associated with Internet-of-things (IoT) provides a framework for integrated computing devices capable of managing efficiently the students’ attendance.
This project aims to implement an IoT based system that monitor students from home to schools, during class hours and from school to home and notify parents and school administrators about the irregularity observed to their respective children.
The system is equipped of a finger print sensor to register and verify students and staff attendance, a PIR sensor to detect the presence of human to wake-up the device, a real time clock to synchronize each generated report with the local time. A web application is developed to allow students real-time monitoring for parents and school administrators and the system will be able to generate a daily, monthly and annually report to education decision makers.
Classification machine learning with decision-tree algorithm is used to analyse data and generate a model to evaluate the impact of monitoring attendance on preventing students to dropout. The generated model with accuracy of 91.4% shows that keeping students’ attendance at high percentage would reduce significantly the dropout rate in secondary schools.
