By the end of this workshop, participants will be able to:
Distinguish between monitoring, evaluation, and learning and explain their interconnections
Develop a comprehensive Theory of Change for a development program
Design results chains that link inputs to long-term outcomes
Select appropriate indicators for different levels of the results framework
Plan basic data collection strategies for MEL systems
Part 1: What is MEL and Why Does it Matter?
20 minutes
Opening Scenario: Two NGOs, Different Approaches
NGO A: Implements a girls' education program for 3 years, collects attendance data monthly, conducts final evaluation, reports 85% attendance rate as "success."
NGO B: Implements similar program, tracks attendance plus learning outcomes, family attitudes, teacher capacity. Uses data quarterly to adjust program design. Discovers attendance is high but learning is low—pivots to teacher training focus.
Question: Which approach is more likely to create lasting change? Why?
Understanding MEL: Three Interconnected Functions
Monitoring
Purpose: Track progress and performance
Ongoing data collection
Performance against targets
Early warning systems
Accountability to stakeholders
Example: Monthly tracking of girls enrolled, attendance rates, dropout reasons
Evaluation
Purpose: Assess effectiveness and impact
Systematic assessment
Causal attribution
Value for money analysis
Evidence for scaling
Example: Comparing learning outcomes of program girls vs. control group
Learning
Purpose: Generate insights for improvement
Reflection and adaptation
Knowledge management
Continuous improvement
Innovation and iteration
Example: Understanding why some girls succeed despite barriers—scaling effective approaches
Why MEL Matters: The Evidence-to-Impact Pipeline
Strong MEL systems don't just measure—they drive better outcomes by:
Improving program design through real-time feedback
Increasing accountability to beneficiaries and funders
Building evidence for policy and practice change
Facilitating scaling of effective interventions
Contributing to sector knowledge and innovation
Part 2: Theory of Change - Your Program's Story of Change
25 minutes
What is a Theory of Change?
Definition: A comprehensive description and illustration of how and why a desired change is expected to happen in a particular context.
Key Components:
Long-term outcomes: The ultimate change you want to see
Preconditions: What must happen for change to occur
Assumptions: Beliefs about how change happens
Activities: What you will do to catalyze change
Context: External factors that influence change
Building a Theory of Change: Step-by-Step Process
Impact
Long-term sustainable change
Reduced gender inequality in education
Outcomes
Medium-term changes in behavior/conditions
Girls complete secondary education
Outputs
Direct products of activities
Girls enrolled in school, teachers trained
Activities
What you do
Scholarship program, teacher training
Inputs
Resources invested
Staff, funding, materials
Theory of Change Workshop (15 minutes)
Scenario: Design a Theory of Change for improving maternal health in rural areas.
Context:
High maternal mortality in remote villages
Limited access to skilled birth attendants
Cultural barriers to facility-based delivery
Poor transport infrastructure
Your Task (work in pairs):
Start with Impact: What's the ultimate change you want? (3 minutes)
Work backwards to Outcomes: What changes need to happen first? (4 minutes)
Identify Outputs: What products/services will create these changes? (3 minutes)
Design Activities: What will you actually do? (3 minutes)
List key Assumptions: What must be true for this to work? (2 minutes)
Reflection: What are the critical assumptions that could break your theory?
Strong Theory of Change Characteristics
Plausible: Links between levels make logical sense
Feasible: Achievable given context and resources
Testable: Assumptions can be verified through evidence
Specific: Clear about who, what, where, when
Evidence-based: Grounded in research and experience
Part 3: Indicators - Making Change Measurable
20 minutes
Types of Indicators
Indicator Type
Purpose
Example (Girls' Education)
Data Source
Input Indicators
Resources invested
Number of teachers trained
Training records
Output Indicators
Products/services delivered
Number of girls enrolled
School registration
Outcome Indicators
Changes in people's lives
Girls' learning achievement levels
Test scores, assessments
Impact Indicators
Long-term change
Gender parity in employment
Labor force surveys
SMART Indicators Framework
Making Indicators SMART
S - Specific
Clear about what is being measured
Poor: "Improved health"
Good: "Reduced under-5 mortality rate"
M - Measurable
Quantifiable with clear units
Poor: "Better nutrition"
Good: "Percentage of children with normal weight-for-height"
