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.