Automated Classroom Attendance System (ACAS)
Document Type
Project
Advisors
Dr. Jonathan Engelsma; jonathan.engelsma@gvsu.edu
Embargo Period
5-7-2019
Abstract
Classroom attendance recording and tracking can be a very time-consuming process. If done regularly it can equate to hours of lost productivity over the course of a year. Physically taking attendance can also introduce errors which can cause inaccuracies in reporting. The goal of this project was to bring more automation into the attendance taking process. The Automated Classroom Attendance System (ACAS) is a cloud-based solution built using Amazon Web Services (AWS) for automatically recording and tracking classroom attendance. AWS was chosen based on their wide range of services that allows for scalability, reliability and ease of administration. ACAS uses Amazon’s facial recognition technology called Rekognition to identify students and records them as either present or absent according to which classes they are enrolled in. Rekognition is a deep learning facial recognition application programming interface (API) which has a high success rate of providing accurate results. ACAS also has an easy to use web application that assists teachers with creating students, adding classes and retrieving attendance history. The only component of ACAS that is not running in AWS are the classroom webcams which are responsible for uploading images to AWS for facial recognition processing. By implementing ACAS in the classroom, teachers can save time by not having to take attendance and it will also help reduce inaccuracies in attendance reporting.
ScholarWorks Citation
Fenton, Adam, "Automated Classroom Attendance System (ACAS)" (2019). Technical Library. 340.
https://scholarworks.gvsu.edu/cistechlib/340
