Theory of Change in Monitoring and Evaluation: Turning Frameworks into Learning Systems

Author: Unmesh Sheth — Founder & CEO, Sopact
Last updated: October 12, 2025

Introduction: Why Theory of Change Matters in M&E

Monitoring and Evaluation has evolved from a compliance task to a core driver of accountability and learning. Funders, policymakers, and boards now want more than activity counts like “200 participants trained” or “50 sessions held.” They demand real answers:

• What changed?
• For whom?
• Why did it happen?
• Can it be sustained or scaled?

Yet most organizations spend more time preparing data than learning from it. Surveys sit in spreadsheets, transcripts get lost in PDFs, and frameworks are applied inconsistently. The result is an evaluation process that feels slow, fragmented, and disconnected from daily decision-making.

This is where the Theory of Change (ToC) comes in. At its best, ToC is not just a diagram but the backbone of a Monitoring, Evaluation, and Learning (MEL) system. It makes assumptions explicit, connects activities to outcomes, and provides a shared roadmap that funders, implementers, and communities can use. But most importantly, it creates a structure for continuous learning, not just annual reporting.

At Sopact, we see ToC as a living system. We are framework-agnostic—whether you align with SDGs, donor logframes, or custom outcomes maps, the framework isn’t the point. The point is whether your data is clean, connected, and AI-ready at the source. With that foundation, Sopact Sense helps organizations turn a ToC from a static proposal artifact into a continuous evidence loop, where insights surface in hours, not months, and teams adapt in real time.

The Anatomy of a Theory of Change

A Theory of Change is more than boxes and arrows. It is a structured way of linking stakeholders, activities, outputs, and outcomes into a system of learning. Let’s break down the layers:

1. Stakeholders

Every ToC begins with who you are trying to reach. Stakeholders could be students, farmers, patients, entrepreneurs, employees, or entire communities. Without clarity on who the change is for, the rest of the chain becomes guesswork.

Questions to ask:

• Who are our primary stakeholders?
• What barriers do they face?
• How do they define success?

2. Long-Term Impact

Impact is the end-state change you want to see in the world. It’s often broad—reduced poverty, healthier communities, gender equity, restored ecosystems. While impact can take years to measure, naming it anchors the ToC in purpose.

Questions to ask:

• What is the ultimate positive change we want for stakeholders?
• How will the world look different if our work succeeds?

3. Activities

Activities are the things you do—training, awareness campaigns, service delivery, policy advocacy. Activities are within your control, but they are only the starting point.

Questions to ask:

• What do we deliver directly to stakeholders?
• Which activities are core vs. supportive?

4. Activity Metrics

Every activity needs simple measures of effort and reach. These are not outcomes—they’re just signals that you delivered what you promised.

Examples:

• Number of training hours provided
• Number of clinics held
• Number of farmers reached with new tools

5. Outputs

Outputs are the immediate results of activities—skills gained, knowledge improved, services accessed. They are short-term and measurable.

Examples:

• Students complete STEM training modules
• Patients receive preventive care
• Farmers adopt new irrigation practices

6. Output Indicators

These are the specific ways outputs are measured.

Examples:

• % of students passing STEM tests
• % of patients completing screenings
• % of farmers using improved methods

7. Outcomes

Outcomes are the changes in behavior, condition, or status that follow from outputs. They are the real proof of progress.

Examples:

• Higher STEM enrollment in universities
• Lower incidence of preventable diseases
• Increased crop yields and income stability

8. Outcome Metrics

These capture the strength of outcomes. Unlike outputs, outcomes often require both quantitative and qualitative data to tell the full story.

Examples:

• % of graduates employed in STEM-related jobs
• % reduction in blood pressure among patients
• % increase in household income among farmers

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The Trap: Trying to Perfect the ToC

Too many organizations fall into the trap of spending months “perfecting” their ToC diagrams. They hire consultants, hold workshops, and try to anticipate every possible pathway. The result: beautiful charts that rarely get used.

The reality is you don’t need a perfect ToC. You need a useful ToC—one that identifies:

• 3–4 key outcomes that really matter
• The metrics that can validate those outcomes
• A practical way to collect and learn from data continuously

This is not about getting every arrow right. It’s about focusing on what you most want to learn, then building evidence around it.

Balancing Quantitative and Qualitative Data

Traditional ToCs rely heavily on quantitative metrics—numbers, percentages, rates. These are important, but they rarely tell the whole story.

• Quantitative data tells you what happened: test scores improved, clinic visits increased, incomes rose.
• Qualitative data tells you why it happened: confidence grew, access barriers fell, communities trusted the program.

The strongest ToCs combine both. But qualitative data is often dismissed as “too subjective” because coding transcripts and analyzing themes is time-consuming and inconsistent.

This is where Sopact Sense AI changes the game. By cleaning, coding, and analyzing transcripts, open-ended surveys, and documents, Sopact makes qualitative data objective, scalable, and easily combined with quantitative metrics. The result is a ToC that reflects both the numbers and the lived experiences of stakeholders.

Creating a Culture of Daily Learning, Not Annual Reports

Most organizations still treat M&E as an annual ritual. Data is collected, cleaned, and analyzed months later—long after it could have influenced program design.

A modern Theory of Change should enable daily or weekly learning:

• Teams see early signals of what’s working.
• Assumptions are tested continuously.
• Adjustments are made in real time.

This is a culture of experimentation. Instead of waiting for the “big evaluation,” programs learn and adapt constantly. Failures become visible early, successes scale faster, and organizations evolve into true learning systems.

With Sopact Sense, this shift is possible. By integrating survey data, transcripts, and outcomes into a single evidence loop, organizations no longer have to wait a year to learn. They can track, compare, and adapt in near real time.

Examples of Theory of Change in Practice

Example 1: STEM Education (InnovateEd, South Africa)

• Stakeholders: Primary and secondary students
• Activities: Deliver STEM curriculum
• Activity Metrics: # of classes delivered, # of students enrolled
• Outputs: Students complete curriculum modules
• Output Indicators: % of students passing STEM exams
• Outcomes: Increased interest and enrollment in STEM pathways
• Outcome Metrics: # of students pursuing higher education or careers in STEM fields

👉 With Sopact Sense, InnovateEd connects student grades, teacher feedback, and survey data to continuously test whether curriculum changes lead to improved STEM participation.