Blog Post 4 of 6

 

Engine 2: Tracking and Validating Learning in Real Time

In today’s education and workforce development systems, proving what you’ve learned is a labyrinthine process. The status quo relies on outdated methods like standardized tests, which often fail to capture the diversity of human intelligence and potential. Worse yet, these systems are rigid, slow, and disconnected from the actual needs of learners and employers. Engine 2, the second engine in our system, transforms this outdated framework by introducing a dynamic, scalable, and transparent Proof of Learning Protocol which includes an Open Verifiable Credentials system.

Let’s explore how these innovations work and why they matter.

 

To recap, Engine 2 contains the following two parts:

  • Proof of Learning Protocol (2.1): A decentralized, auditable system that tracks and validates learning in real-time, ensuring accuracy and scalability.

  • Verifiable Credential System (2.2): Transforms validated learning into credentials that are recognized and trusted globally.

 
 

The Proof of Learning Protocol

How It Works
At the core of the Proof of Learning Protocol is the ability to track, validate, and reward learning in real time. Learners interact with educational nodes (granular concepts like “What is Agile project management?”) and complete activities that earn them proof of learning points. These points reflect their progress and mastery across three levels: Exploration, Proven, and Mastery.

Activities That Earn Points
Points are awarded for various activities, including but not limited to:

  • Watching a video or reading a PDF

  • Completing formative assessments or quizzes

  • Participating in peer-to-peer validation

  • Publishing projects or contributing content

  • Engaging in Q&A interactions with AI or peers

Each activity has a unique scoring algorithm that calculates the number of points earned, how they decay over time (very important), and their contribution to a learner’s overall progress. For example:

  • Viewing a video may award up to 50 points at the Explore level, but higher levels require more rigorous activities like publishing a project. 

  • Peer validation and project-based learning activities offer more durable points with lower decay rates, reflecting deeper engagement and retention.

Resource Efficacy Measure (REM):

The effectiveness of the resources learners engage with impacts the points they earn. Resources are scored based on:

  1. Real-Time Efficacy (RE): Metrics like engagement duration and content completion.

  2. Longitudinal Efficacy (LE): Long-term learning outcomes assessed through follow-up activities.

  3. User Feedback (UF): Ratings from learners.

Dynamic and Transparent Algorithms
A standout feature of the Proof of Learning Protocol is its adaptability. All scoring algorithms are open, auditable, and continuously optimized. Unlike traditional credentials, which are like black boxes that cannot be scrutinized by the public, and which are static and can’t be retroactively adjusted, this system allows for recalculations if new data comes to light—ensuring transparency, fairness and accuracy.

 

Levels and Decay Functions

The system’s points are dynamic, decaying over time to reflect the natural progression of knowledge retention. Here’s how it works:

  • Explore Points (0–100): Quick to earn but decay rapidly.

  • Proven Points (101–200): Require more effort and decay at a moderate rate.

  • Mastery Points (201+): Earned through advanced activities like teaching others or publishing projects. These points decay the slowest, ensuring that true expertise is recognized and retained longer.

The decay functions are adaptive, ensuring that points earned through deeper, more engaging activities hold greater long-term value.

 

Gamifying the Learning Experience

The Proof of Learning Protocol employs a Progressive Learning Difficulty Curve (PLDC) inspired by game design principles. Initial levels are easy to achieve, fostering motivation and engagement. As learners progress, the challenges become more complex, reflecting the deeper knowledge and skills required for advanced mastery.

This gamified structure ensures a balance between accessibility for beginners and rigor for advanced learners, creating an engaging and rewarding journey.

 
 
 

Inference-Based Points and Adaptive Learning

Inferred Points:
Learners also earn inferred points through interconnected nodes. For example, mastering “Coding a Smart Contract” might infer knowledge of “What is a Smart Contract,” awarding partial points to related nodes. This system reflects the interconnected nature of knowledge, reducing redundancy and encouraging holistic learning.

Adaptive Learning Algorithms:

The protocol integrates adaptive algorithms that adjust content complexity based on a learner’s performance. Struggling with advanced material? The system might recommend foundational nodes to reinforce your understanding. Excelling? It will push you toward higher-level challenges. This personalization ensures that every learner’s journey is optimized for growth.

 

Verifiable Credentials: Turning Points into Recognition

While points reflect progress, learners need tangible proof of their achievements. That’s where Engine part 2.2 comes in: transforming Proof of Learning points into Open Verifiable Credentials (OVCs). These credentials are:

Dynamic and Adaptive:
Unlike traditional certificates, OVCs evolve with the learner. For example:

  • Exploratory Credentials: Awarded for reaching 100 points at the Explore level.

  • Proficiency Credentials: Granted for earning 200 Proven points.

  • Mastery Credentials: Reflecting 300+ points, these signify expertise.

Credentials decay if knowledge isn’t maintained, ensuring they remain an accurate reflection of a learner’s current capabilities.

Secure and Transparent:

OVCs are stored on a blockchain, ensuring they are:

  • Tamper-proof: Immutable once issued.

  • Easily Verifiable: Accessible through a QR code or credential ID.

  • Privacy-Protected: Only relevant credential data is shared, keeping personal information secure.


Portable and Interoperable:

Learners can share their credentials across platforms like LinkedIn or job portals. They are compatible with global standards such as Mozilla Open Badges, xAPI, and IMS Global standards, making them universally recognized.


Real-World Impact

The combined power of the Proof of Learning Protocol and Open Verifiable Credentials creates a system that:

  • Empowers Learners: Offering real-time validation of skills and knowledge.

  • Supports Lifelong Learning: Encouraging learners to continuously engage and grow.

  • Enhances Trust: Providing employers and institutions with transparent, reliable credentials.

  • Reduces Barriers: Democratizing access to education and recognition, especially for underserved communities.

Imagine Alice the aspiring astronaut using her OVCs to prove her mastery of orbital mechanics to space agencies, or Lola the retrenched worker showcasing her automation credentials to land new job opportunities. This is the future of education—accessible, transparent, and learner-focused.

 


A New Standard for Education

Engine 2 redefines how we track, validate, and recognize learning. By combining real-time assessment, dynamic credentials, and adaptive algorithms, it creates a system that’s as flexible and diverse as the learners it serves. This isn’t just an upgrade to traditional education—it’s a complete reimagining.

In the next post, we’ll dive into Engine 3 and explore how learners can leverage their validated skills to unlock real-world opportunities. The journey doesn’t end with learning; it’s just the beginning.



Next Post in the series: