The Programme Governance Dashboard is a local-first, lightweight analytics tool designed to help academic institutions visualize structural risks, curriculum coherence, and governance compliance across their degree programmes. Built for speed and privacy, it interprets standard educational definitions (programmes, modules, outcomes, and assessments) to immediately surface actionable mapping defects before they impact faculty productivity and student experiences.
Phase: Phase 1 — Curriculum Spine
Role: Aggregates curriculum health, alignment, and behavioral risk signals to provide institutional oversight across multiple programmes.
Upstream Inputs: Validated structural data from the Mapping Engine and behavioral risk signals from the Simulation Tool.
Downstream Outputs: Provides institutional RAG health summaries used for quality assurance reporting and strategic curriculum review.
Does NOT:
- Perform detailed workload simulation or pathway personalisation.
- Authorize or record individual human-AI decision outcomes.
📘 New to the system? Please refer to the comprehensive User Manual for detailed step-by-step instructions on navigating the dashboard, interpreting AI oversight signals, and tracking curriculum snapshots.
👉 http://cloudpedagogy-programme-governance-dashboard.s3-website.eu-west-2.amazonaws.com/
This screenshot shows the fully rendered Programme Governance Dashboard using the demo dataset included in this repository.
The demo allows users to explore system-level curriculum structure and governance visibility, including:
- Relationships between programmes, modules, outcomes, and assessments
- Identification of duplication, imbalance, and structural gaps
- Governance metadata and alignment indicators
- Snapshot comparison of curriculum states (e.g. before/after redesign)
- Local-first, privacy-preserving interaction (no backend or data transmission)
The demonstration uses entirely synthetic data and represents a fictional multi-programme curriculum environment designed to illustrate governance-aware analysis and system-level insight.
- Programme Health Summary: Authoritative baseline view (landing page) summarizing institutional oversight across curriculum alignment, assessment coverage, and risk distribution.
- RAG Risk Classification System: Automated "Traffic Light" grading for modules and programmes:
- Red: Unassessed outcomes or zero-active-skill modules detected.
- Amber: Low assessment density (less than 1.0 assessment per outcome).
- Green: Full structural coverage and alignment verified.
- Deterministic Analytics Engine: Automated logic that detects highly shared modules, unassessed outcomes, and over-concentrated curriculum traps natively.
- Programme Comparison Matrix: Instantly stack up to 3 distinct programmes side-by-side to contrast structural metrics and governance completion.
- Assessment Load Clustering: Identifies scheduling clash risks (e.g., excessive student assessment volume) aggregated tightly within specific weekly intervals.
- Interactive Drill-Down Inspection: Accessible overlay panels that reveal the exact native raw data or missing metadata, ensuring the audit logic is completely transparent.
- Historical Snapshot Trends: Dedicated scenario tracking that lets you permanently save a structural version of your curriculum and visibly compare live variance (Deltas +/-) when testing hypothetical module revisions.
- Defensive Error Interception: Resilient, non-blocking integrity checks that intercept and flag orphaned data relationships (like missing module references) without crashing the interface.
- Academic Leadership: Instantly assess the degree of "shared module" reliance across intersecting degrees, accurately measuring true operational/teaching efficiency.
- Quality Assurance (QA) Teams: Audit missing or misaligned Learning Outcomes before official review cycles submit inaccurate matrices to external accreditors/regulators.
- Programme Redesign Committees: Experimentally load hypothetical module mappings and capture state "Snapshots" to determine if a curriculum restructure truly improves assessment distribution compared against foundational baselines.
- Local-First Processing: The dashboard maps data actively against the machine's localized cache variable (
localStorage). - Zero Backend Required: Utterly decoupled from external databases or API servers, making it deeply secure for sensitive, unapproved, or proprietary curriculum definitions.
- Static Deployability: Entirely serverless architecture. The React application strictly compiles to static assets meaning it can be securely hosted anywhere with zero maintenance.
Ensure you have Node.js installed, then execute the following steps within your terminal:
- Clone or download the repository to your local directory.
- Install standard dependencies:
npm install
- Run the live local development server:
npm run dev
- Perform strict type checking and build compiling testing:
npm run build
Because the dashboard relies on a zero backend structure natively supported by Vite, you can statically host it universally.
- Run the target compilation build:
npm run build
- The output directory (
/dist) will contain the finalized optimized HTML, JavaScript, and CSS nodes. - Simply drag or upload the
/distfolder directly to your provider of choice (e.g., GitHub Pages, Netlify, Vercel, AWS S3, or practically any standard internal campus webhost bucket).
The localized data engine mathematically normalizes four mapping vectors:
- Programmes: Container hierarchies aggregating overarching descriptions and timestamped metadata states.
- Modules: The fundamental teaching payload blocks processing precise credit weights. These map rotationally to Programmes allowing heavy structural scaling analytics.
- Outcomes & Assessments: Granular pedagogical interactions. Outcomes attach sequentially to Modules, while Assessments map to Outcomes, resolving pure alignment density algorithms.
- Governance Metadata: Scalar checklist indices embedded directly against Modules verifying explicit documentation execution natively tracking administrative hurdles.
IMPORTANT NOTE ON INTERPRETATION These metrics are interpretive structural indicators designed strictly to surface quantitative mapping gaps algorithmically. They are NOT compliance scores (i.e. 'Pass/Fail'). A 50% curriculum integration mark does not inherently mean a critically defective curriculum, but serves as a mechanical flag. They DO NOT replace professional faculty judgement, qualitative audits, or deeply contextual institutional decision-making.
- JSON Payload Engine: Allowing explicit
.jsonfile exports and imports directly into the UI, breaking the dependence on strictlocalStoragebounding across singular devices. - Printable Executive Summary: A one-click PDF abstraction generator stripping away UI buttons and compiling your Overview metrics and Analytics Insight flags into a clean letterhead format suitable for QA board review meetings.
This repository contains exploratory, framework-aligned tools developed for reflection, learning, and discussion.
These tools are provided as-is and are not production systems, audits, or compliance instruments. Outputs are indicative only and should be interpreted in context using professional judgement.
All applications are designed to run locally in the browser. No user data is collected, stored, or transmitted.
All example data and structures are synthetic and do not represent any real institution, programme, or curriculum.
This repository contains open-source software released under the MIT License.
CloudPedagogy frameworks and related materials are licensed separately and are not embedded or enforced within this software.
CloudPedagogy develops open, governance-credible resources for building confident, responsible AI capability across education, research, and public service.
- Website: https://www.cloudpedagogy.com/
- Framework: https://github.com/cloudpedagogy/cloudpedagogy-ai-capability-framework
This tool supports both AI capability development and lightweight governance.
- Capability is developed through structured interaction with real workflows
- Governance is supported through optional fields that make assumptions, risks, and decisions visible
All governance inputs are optional and designed to support — not constrain — professional judgement.