Monitoring, Evaluation, and Learning (MEL) is the natural process of watching how things are going, asking if they're working and why, and then changing our behavior to continuously improve.
Humans, many wild animals, and plants engage in MEL-like processes to survive. Thus, we stress that MEL is natural. Let's use an example from a romantic relationship and a hypothetical education project.
Monitoring
Monitoring is the ongoing process of ensuring things are progressing well and identifying problems early. Think of it like paying attention in a relationship: noticing whether your partner is responsive, whether you're making future plans together, or whether something feels off.
For an education project, monitoring might track:
- Books distributed
- Teachers trained or hired
Evaluation
Unlike monitoring, evaluation only occurs at key intervals, typically: before projects start, at conclusion, and every few years in between. It examines what changed, why it changed, and what comes next.
In a relationship, think of evaluation like having a formal sit-down where you assess whether you're happy, and if the relationship is worth continuing.
For an education project, evaluation might assess:
- Whether the project contributed to improved test scores
- The intended and unintended consequences of the work
Learning
Learning is the combined act of discovering something and then making a change. The latter part is where people tend to struggle the most.
In a relationship, learning is like discovering your partner loves flowers, then surprising them with a bouquet once a month to keep the spark alive.
For an education project, learning could be finding out that 40% of students spend less than one hour a week reading at home, then creating a reading competition that encourages children to read more.
Remember, if you don't change something, then you didn't really learn.
Dealing with the Unavoidable
Even though we try not to use technical language as much as possible, it is very important that you are familiar with common MEL terms. We've chosen a few that are hard to escape. You're very likely to see these words in project documentation, and they will definitely come up in our design sessions.
Project-Level Terms
Intervention / Project / Program
A carefully planned effort toward achieving specific goals.
Context
The overall landscape (including the socio-cultural, economic, political, and environmental factors) within which a project operates.
Results Chain
The logical progression of a project, including inputs (what goes in), activities (what gets done), outputs (what comes out), outcomes (what short- to long-term changes occurred), and impact (what community or society-level change is achieved).
Theory of Change / Project Logic
An articulation of how and why a project is expected to create change.
Assumptions
Conditions that must hold true for the project logic to work.
Indicators
Signs that something is happening, such as a desired outcome or assumption.
Reach
The people, animals, or things (e.g., schools) touched by a project.
Informed Consent
The ongoing process of making sure someone has vital information and actively agrees to participate in something (a project, evaluation, experiment, etc.).
Do No Harm
The principle that evaluation (or other activities) should not increase risk or harm. When there is tension between learning and safety, safety wins.
Evaluation-Specific Terms
Evaluation Purpose
The reason for evaluating and the decisions the evaluation will inform.
Primary Users
The people who will use evaluation findings to make decisions.
Evaluation Questions
The specific questions an evaluation is designed to answer.
Methodology
The overarching theory justifying the evaluation approach.
Methods
The specific techniques used to collect, analyze, and interpret data. (There are countless methods; we'll support you in choosing the ones appropriate for this evaluation.)
Triangulation
Using multiple methods or data sources to validate results. This is a best practice in evaluation and research.
Quantitative Data
Numerical information, or things that can be calculated (e.g., height, weight).
Qualitative Data
Non-numerical information such as words, actions, or observations.
Limitations
Constraints that affect what data can confidently tell us. (Every approach has limitations that we will help you weigh when choosing methods.)
Bias
A systematic error that skews data, leading to inaccurate conclusions.
Baseline, Midterm, and End Line
Evaluations conducted at the beginning, middle, and end of a project.
Setting the Record Straight
Now that you know what MEL is and have started to develop a good understanding of important terms, the last thing to do is address some damaging misconceptions:
Evaluations are audits.
Auditors ask if people are following the rules. Evaluators ask what the effect of someone's work has been, and what can be done to improve. We are not the same.
Evaluations prove programs work.
Evaluations don't set out to prove things, including success. They are designed to get an honest picture of what is happening. Sometimes evaluations confirm our feelings that things are working; however, they may also show where improvements are needed. Both outcomes are useful.
Evaluations must show causation.
Evaluations assessing causation (e.g., "this project caused in-school suspensions to decrease by 5%") can be expensive and are not suited for every situation.
It is perfectly legitimate, and often more appropriate, to assess contribution. That is, whether and how a project contributed to change, not whether it is solely responsible for it.
Numbers are more credible than stories.
Numbers tell us what and stories tell us why. We need both to make improvements.
It is a best practice to triangulate findings by collecting various types of data. That said, more data does not mean more credibility. It is best to collect only what is necessary to answer the evaluation question(s).
