Measuring what Matters in Learning
Measuring what Matters in Learning

Completion rates are easy to measure. Impact is not. 

Measuring what Matters in Learning

At Qquench, we measure learning by asking a harder question:

What actually changes after learning?

This page explains how we think about learning impact — and how we help organizations evaluate learning in ways that are meaningful, realistic, and responsible. 

WHY TRADITIONAL METRICS FALL SHORT 

Why Completion Is Not Impact 

Most learning programs are evaluated using: 

Completion rates

Quiz scores

Attendance 

These metrics answer one question: 

Did learners finish the course?

They do not answer: 

Did learners apply what they learned 

Did decisions improve

Did confidence increase

Did behaviour change over time

We believe learning measurement must go beyond proof of participation. 

HOW QQUENCH DEFINES LEARNING IMPACT 

Our Definition of Learning Impact

Learning impact shows up in three places:

1. Behaviour

what people do differently 

2. Decisions

how people choose under pressure

3. Performance

how consistently learning shows up in work

Not all learning programs need to measure all three — but every program should measure at least one meaningfully

LEVEL-ALIGNED MEASUREMENT

Different Learning Levels Require Different Metrics

We align measurement to the level and intent of learning, not a one-size-fits-all model. 

Foundational Learning 
Measured through: 

  • Concept clarity 
  • Confidence in terminology 
  • Shared understanding 

Applied Learning 
Measured
through: 

  • Procedural accuracy 
  • Task confidence 
  • Reduction in support dependency 

Judgment and Decision-Making Learning 
Measured through: 

  • Concept clarity 
  • Confidence in terminology 
  • Shared understanding 

Sustained Practice and Mastery Measured through: 

  • Consistency over time 
  • Reflection quality 
  • Manager or peer feedback 

This avoids false precision and unrealistic ROI claims

SCENARIO-BASED MEASUREMENT

Measuring Decisions, Not Just Recall

Scenario-based learning allows us to observe: 

How learners respond to complexity

Where hesitation occurs 

Which trade-offs are misunderstood 

How judgment evolves over retries 

These signals are often more valuable than test scores — especially in leadership, ethics, safety, and compliance contexts.

QUALITATIVE SIGNALS WE VALUE 

Not Everything That Matters Is Numeric

We intentionally include qualitative indicators such as:

Learner reflections 

Self-reported confidence shifts 

Manager observations 

Field feedback 

Especially in:

NGO and development contexts 

Ethics and
policy
learning 

Leadership and culture
programs 

Qualitative data adds meaning where numbers alone fall short.

MEASUREMENT AT SCALE

Measuring Impact Across Regions and Contexts

Global learning programs introduce complexity: 

Cultural interpretation

Language
nuance 

Infrastructure variation 

Different performance realities 

We design measurement frameworks that: 

Respect local context

Avoid over-standardization 

Focus on comparable patterns, not identical data

This is critical for global enterprises and UN-linked programs. 

Many global learning programs succeed centrally but fail to translate into local, real-world behavior without context-aware measurement.

WHITE-LABEL AND CONFIDENTIAL ENGAGEMENTS 

Measuring Impact in Confidential and White-Label Work 

In white-label and confidential engagements: 

Client-facing metrics may belong to the primary brand

Attribution may be restricted 

Data visibility may be limited 

Measuring what Matters in Learning

In these cases, we:

Align with partner measurement frameworks

Provide internal quality indicators 

Support aggregated and anonymized reporting 

Impact is still measured — just responsibly and discreetly. 

WHAT CLIENTS CAN EXPECT

What We Do and Do Not Claim 

We do: 

Design learning with measurement in mind

Align metrics to learning intent 

Support meaningful evaluation 

Avoid vanity metrics 

We do not: 

Guarantee business outcomes we do not control

Over-promise ROI attribution 

Reduce learning to dashboards

Trust matters more than exaggerated claims. 

HOW THIS CONNECTS TO OUR SYSTEM 

Impact Is Designed, Not Added

Learning impact is shaped by:

Learning DNA 

Design philosophy 

Architecture and methodology 

Capability choices 

Responsible use of AI 

Measurement is not an afterthought — it is part of the system. 

Explore Learning Architecture and Methodology 
View eLearning Case Studies 

If Learning Outcomes Matter to You 

Measuring what Matters in Learning

If you are looking for learning that: 

Can be trusted 

Can be evaluated responsibly 

Respects complexity 

Then we are aligned.

Explore a Learning Effectiveness Audit