Moodle Analytics & Automation Upgrade
Budget: $250 – $750 USD
I’m ready to take our existing Moodle LMS to the next level by building a robust, Python-powered analytics and automation layer. The immediate goal is to surface real-time insights on student performance—specifically grades and test scores—directly within Moodle so instructors and administrators can intervene sooner and students can track their own progress instantaneously.
Here’s how I see the engagement:
• Solution architecture
– Design a scalable Python microservice or plugin that taps Moodle’s REST/web-service API to pull grades and test-score data on demand.
– Set up secure data pipelines (e.g., Celery, FastAPI, or similar) to ensure minimal latency and uncompromised privacy.
• Real-time analytics engine
– Implement live calculation of averages, trends, and outlier detection.
– Provide configurable alert thresholds (early-warning flags for struggling learners).
• Interactive dashboards
– Deliver web-based, auto-refreshing visualizations inside Moodle using libraries such as Plotly, Chart.js, or Dash.
– Enable drill-down from course level to individual student level in one click.
• Automation hooks
– Trigger personalized email or in-platform notifications when performance dips below set criteria.
– Schedule recurring summary reports for instructors and department heads.
• Deployment & handover
– Containerized setup (Docker/Kubernetes) for easy staging and production rollout.
– Unit tests, integration tests, and CI/CD scripts.
– Comprehensive technical documentation plus an administrator guide.
I’m looking for an end-to-end partner who can refine the concept, build the modules, test rigorously, and stay available for post-launch tweaks. If you have demonstrable experience with Moodle internals and Python-driven analytics, let’s discuss your approach and timeline.
Here’s how I see the engagement:
• Solution architecture
– Design a scalable Python microservice or plugin that taps Moodle’s REST/web-service API to pull grades and test-score data on demand.
– Set up secure data pipelines (e.g., Celery, FastAPI, or similar) to ensure minimal latency and uncompromised privacy.
• Real-time analytics engine
– Implement live calculation of averages, trends, and outlier detection.
– Provide configurable alert thresholds (early-warning flags for struggling learners).
• Interactive dashboards
– Deliver web-based, auto-refreshing visualizations inside Moodle using libraries such as Plotly, Chart.js, or Dash.
– Enable drill-down from course level to individual student level in one click.
• Automation hooks
– Trigger personalized email or in-platform notifications when performance dips below set criteria.
– Schedule recurring summary reports for instructors and department heads.
• Deployment & handover
– Containerized setup (Docker/Kubernetes) for easy staging and production rollout.
– Unit tests, integration tests, and CI/CD scripts.
– Comprehensive technical documentation plus an administrator guide.
I’m looking for an end-to-end partner who can refine the concept, build the modules, test rigorously, and stay available for post-launch tweaks. If you have demonstrable experience with Moodle internals and Python-driven analytics, let’s discuss your approach and timeline.