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Anthropic certificates · AI fluency for communities (4/4)
Completed courses: Teaching the AI Fluency Framework; AI Fluency for educators; AI Fluency for nonprofits; AI Fluency for students. The four course completion certificates are attached.
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The travel photos curling into an arc around "Planning it?" make the question visual, nice. Since the client rejected the first full video, what did they push back on most, pacing or story?
AI-Powered Student Success & Early Intervention Platform
Helping universities identify students who may be at risk of dropping out through machine learning and continuous student monitoring.
I built this web application for a client who needed a better way to monitor university students, identify potential dropout risks, and help lecturers intervene early.
Rather than building a system that only makes predictions, I developed a complete platform with separate dashboards for students and lecturers, powered by two integrated machine learning models.
What I built
1. Dual Machine Learning Models
Entry Prediction Model: Evaluates incoming students using their initial enrolment information to identify potential dropout risks.
Academic Progress Model: Uses students' academic results after their first two semesters to reassess their predicted risk as they progress.
2. Student Dashboard
Students can access their dashboard and submit weekly self-reports, giving lecturers additional insight into their academic progress and experiences.
3. Lecturer Dashboard
Lecturers can:
View and filter students by predicted risk level.
Access AI-generated predictions, including categories such as "Graduate" and "Needs Support".
Monitor weekly student self-reports.
Identify students who may require additional attention.
The Technology & Implementation
Python
Machine Learning
Data Processing
Model Training and Integration
Web Application Development
Database Integration
The Outcome
The client received an integrated platform that combines machine learning predictions with regular student feedback, helping lecturers monitor student progress and identify potential challenges earlier.
My contribution: Machine learning model development, integration of both models into the application, and development of the student and lecturer dashboards.
Interested in building an AI-powered application, integrating machine learning into your existing platform, or turning an idea into a functional product?
Let's work together.