Using the cloud & AI to help lecturers keep track of students attendance.
Project Overview
IoT Smart Attendance is a system concept that uses smart IP cameras, cloud analytics, and a custom mobile app to automate university attendance logging and engagement tracking. It identifies whether students attended, how long they stayed, and key patterns such as punctuality.
The Problem
Universities still rely heavily on:
Digital attendance registers
Inaccurate or inconsistent reporting
Limited visibility into student welfare and engagement
There is an opportunity to automate data capture while assisting lecturers in spotting at-risk students early.
Goals & Impact
Detect attendance and engagement.
Improve student support intervention.
Support fire safety & welfare monitoring.
Reduce admin burden on lecturers.
Real-time reporting through a mobile dashboard.
Research & Competitive Analysis
I examined existing attendance products used in the workplace such as ExakTime, ClockShark, and HikVision biometric systems — evaluating cost, scalability, UX and levels of automation.
Key insights:
Current systems are expensive for education
Interfaces can be complex or outdated
Connectivity issues can disrupt reliance on mobile check-ins
Privacy + biometric compliance require careful design
Technical Concept
Hardware
Smart IP camera w/ face recognition
Mobile devices for app access
Design Process
Wireframes → Prototypes → Iterations
I produced a full UX prototype in Figma that evolved through structured lecturer feedback from multiple staff members.
Each round of feedback informed usability updates:
Lecturer Feedback Highlights → Design Improvements
Feedback → Update Implemented
Show image instead of student ID number → Student images added
Add traffic light visuals for attendance health → Traffic-light % indicators added
Add authorised absence category → 3-tier attendance breakdown added
Provide session duration/punctuality → Added thresholds + punctuality legend
Identify attendance patterns → Visual attendance trend graph added
Add contact actions like “Call Student” → Call + emergency staff buttons added
Ability to manually adjust attendance errors → Tick-box system added
Outcome & Reflection
This project strengthened my ability to combine user-centred design with emerging IoT technology in a realistic university context. My decisions were supported by research into existing attendance tools and guided by continuous lecturer feedback, ensuring each feature was directly useful and intuitive in daily teaching scenarios.
I also gained important awareness of ethical and legal considerations around biometric data, including consent, security, and algorithmic fairness — shaping the system toward responsible implementation. Technically, this project grew my understanding of how smart cameras, cloud services, and mobile apps can work together as a connected ecosystem.
Overall, the result is a concept that not only automates attendance, but also helps universities improve student support and identify issues earlier.
Future Iterations
A student-facing app to give learners visibility into their attendance and engagement
Predictive analytics to help staff recognise early drop-off or welfare concerns
Stronger integration with existing university systems to reduce admin work