Technology & Tools

Our evaluation methodology is powered by a suite of technological tools designed to capture, process, and visualize the flow of multiple forms of capital throughout an action-learning journey. These tools work together to create a seamless evaluation experience that balances ease of use with analytical depth.

Telegram Bot Spec & Conversation Flows

At the heart of our data collection system is a Telegram bot designed to capture reflections, observations, and attributions in accessible, low-friction ways:

Bot Capabilities

• Voice Note Processing: Records, transcribes, and analyzes voice reflections

• Text Input Analysis: Processes written reflections with natural language processing

• Tagging System: Supports both explicit and AI-inferred tagging

• Scheduled Prompts: Delivers contextual reflection prompts at key moments

• Peer Attribution: Enables acknowledgment of value received from others

Conversation Flow Examples

Morning Intention Prompt:

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Good morning! What forms of capital do you hope to generate or receive today? [Voice note option] [Text input option] [Quick reply buttons for common capitals]

Evening Reflection Prompt:

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As you reflect on today: 1. What was the most valuable exchange you experienced? 2. Who contributed meaningfully to your learning? 3. What surprised you about how value flowed today? [Voice note option] [Text input option] [Capital flow tagging suggestions]

Peer Attribution Flow:

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You mentioned @username contributed to your learning. Would you like to acknowledge this contribution? [Yes/No] → If Yes: What form of capital did they share with you? [Social] [Human] [Natural] [Cultural] [Other] How significant was this contribution? (1-5)

Data Repositories & Pipelines

The data collected through the Telegram bot and other sources flows through a structured pipeline designed for both security and accessibility:

Primary Repository Structure

• Raw Data Storage: Secure database for all unprocessed inputs

• Processed Data Layer: Tagged and classified data ready for analysis

• Visualization Data: Pre-processed datasets optimized for dashboard displays

• Historical Archive: Long-term storage of evaluation data for longitudinal analysis

Data Pipeline Flow

1. Collection: Inputs gathered from Telegram, surveys, programmatic sources

2. Processing: NLP analysis, tagging, classification, and validation

3. Storage: Structured database organization with appropriate access controls

4. Analysis: Pattern recognition, network mapping, and insight generation

5. Visualization: Transformation into interactive visual formats

6. Feedback: Insights delivered back to participants and facilitators

Technical Implementation Options

• Lightweight Option: Airtable + Zapier + Google Sheets

• Medium Option: MongoDB + Python processing + Tableau

• Full-Featured Option: PostgreSQL + custom API + D3.js visualizations

Dashboards & Visualization Guidelines

Our visualization approach makes complex capital flows accessible and actionable:

Dashboard Components

• Network View: Interactive graph showing relationships between participants and capital flows

• Timeline View: Chronological display of capital flows throughout the journey

• Heatmap View: Intensity visualization of different capital types across aspects

• Individual View: Personal capital contribution and reception patterns

• System View: Macro-level patterns across the five systemic spheres

Visualization Principles

1. Clarity First: Prioritize clear communication over visual complexity

2. Interactive Depth: Allow users to explore data at multiple levels of detail

3. Contextual Comparison: Show data in relation to baselines and sustainability norms

4. Narrative Integration: Connect quantitative patterns to qualitative stories

5. Accessible Design: Ensure visualizations are understandable by non-technical users

Technical Implementation

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Data → Processing Pipeline → Visualization Layer → Interactive Dashboard

The dashboard updates in near real-time as new data flows in, providing a living picture of the evaluation landscape throughout the action-learning journey.

By integrating these technological tools into a cohesive system, we create an evaluation infrastructure that makes visible the often-invisible flows of value that drive regenerative development, while keeping the human experience at the center of the process.