what is MEL?

MEL is short for monitoring, evaluation, and learning. The Studio defines MEL as:


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. In humans, it shows up when we decide which policies to pass, what programs to fund, and even who to date.

On that note, to keep things interesting, we'll explain MEL using a romantic relationship, with examples from a hypothetical program that aims to improve children's education. Let's get started!

monitoring

Monitoring is an ongoing process we use to make sure things are progressing well...and spot if things are not going well. Spotting trends early makes it possible to keep moving in the right direction and to change course before things get out of hand.


Think of monitoring like keeping an eye on the state of your relationship. Is your partner responsive to texts? Do you both show interest by planning date nights? Monitoring concerns the day-to-day data we collect.


To monitor an education project, for example, we might regularly collect data such as:

  • How many books have been distributed.
  • How many teachers have been trained/hired.


evaluation

Unlike monitoring, evaluation only happens at key times, usually before a program starts, around the time it ends, and every 1, 5, 10, or 15 years in between. We ask specific questions about what has changed, why, and what next steps should be.


Think of evaluation like sitting down with your partner once a year and formally examining whether there have been positive or negative changes in your relationship. Are you are more or less happy than you were this time last year? Is the relationship still working? Is it worth continuing? If so, how can you can grow even stronger together?


The evaluation of an education project might ask:

  • Has the project contributed to improved student test scores?
  • Have there been any unintended consequences of the project (both positive and negative)?

learning

As you may have noticed looking at Cardi B and Offset's on again, off again relationship, learning is the part people sometimes struggle with.


In fact, prior to 2010, most people just said M&E. The "L" is a relatively new addition. It is important to stress that "learning" is not just gaining new information. It is also about taking action.

In a relationship, learning would be the combined act of discovering your partner likes flowers, then surprising them with a bouquet once a month to keep the spark alive.


For an education program, learning could be finding out that 40% of students spend less than one hour a week reading at home, then creating a "Book Blitz" that encourages children to read more.

Remember, if you don't change your behavior, you didn't really learn anything.

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 program documentation and they will definitely come up in our design sessions.

  • Intervention / Project / Program: a carefully planned effort (or set of efforts) designed to achieve a goal.

  • Context: the overall landscape or situation in which a project takes place. It includes the various socio-cultural, economic, political, environmental, and other factors that affect a project. Contexts vary across space and time and can change very rapidly - sometimes permanently.

  • Results chain: the logical progression from inputs to impact:
  • Inputs: the resources put into a project (e.g., money or staff time)
  • Activities: the actions taken as part of the project (e.g., building schools or training teachers)
  • Outputs: the immediate product of an activity (e.g., number of schools built or teachers trained)
  • Outcomes: the short and mid-term results of an activity (e.g., increased student enrollment or improved understanding of best teaching practices) 
  • Impact: the long-term, sustained result of one or more projects (e.g., increased income due to higher academic attainment)


  • Theory of Change / Program logic: an articulation of how a project is expected to create change.

  • Assumptions: the conditions that must hold true for the program logic to work (e.g., assuming that teachers will retain training materials and not forget them as soon as a training is over).


  • Indicators: signs that something is happening, such as a desired outcome or assumption.

  • Reach: the people, animals, or things (e.g., plants, schools) that have been directly or indirectly touched by a program (usually in a positive way).


  • Informed consent: the ongoing process of making sure someone has vital information and actively agrees to participate in something (a program, 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.

Alright, those were the broad terms that relate to projects as a whole (including MEL work). The remaining terms are slightly more technical and specifically relate to evaluation and research. They will come up in our conversations, so spend some getting familiar with them:

  • Evaluation purpose: the reason we’re evaluating, including what decisions it will inform.

  • Primary users: the people who will use the evaluation findings (e.g., mothers, local leaders, organizations, government officials, etc.).

  • Evaluation questions: the specific questions the evaluation will answer (e.g., "Has this program contributed to a measurable increase in the percent of children passing state exams?").

  • Methodology: the overall theory underpinning and justifying the evaluation approach. It determines the selection of methods.


  • Methods: the techniques used to collect, analyze, and interpret data to answer evaluation questions. (There are countless methods; we'll support you in choosing the ones appropriate for this evaluation.)

  • Triangulation: using multiple methods to validate a result (a best practice in evaluation).

 

  • Quantitative data: numerical data, or things that can be calculated (e.g., height, weight).


  • Qualitative data: non-numerical data, like words or actions (e.g., the content of Google reviews).

  • Limitations: constraints or tradeoffs that change what the data can confidently tell us. (Every evaluation approach has limitations that we will help you weigh when choosing methods.)

  • Bias: a systematic error that skews data, leading to inaccurate conclusions. (This is not the same as prejudice. There are numerous biases in evaluation and research and we'll support you to avoid or manage them.)

  • Baseline, midterm, and end line: evaluations that coincide with the (approximate) beginning, middle, and end of a program, respectively.

That it! If you develop a good understanding of these terms, you'll be in a very good position. Don't worry if you have to read them over a few times. Rest assured, if you have questions, we're here to support you.

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:

Misconception: Evaluations are audits.

Truth: Auditors ask if people are following the rules. Evaluators ask what has been the effect of someone's work, and what can be done to improve. We are not the same.

Misconception: Evaluation is a form of verification.

Truth: 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.

Misconception: Evaluations must show causation/attribution.

Truth: Assessing causation (evaluators say "attribution") allows you to claim something like, "this project caused in-school suspensions to decrease by 5%."


The reality is that evaluations unlocking these types of claims can be very expensive, and they are not suited for every situation.


Projects (and people) rarely exist in a vacuum. So most outcomes are the result of numerous factors (other programs, personal circumstances, changes in the context, etc.). Therefore, it is perfectly legitimate, and often more intelligent, to assess contribution. That is, whether and how a program contributed to change, not whether it is solely responsible for it.

Misconception: Numbers are more credible than stories.

Truth: Numbers tell us what is happening and stories tell us why. We need both to make improvements.


It is best practice to triangulate findings by collecting quantitative data, qualitative data, and something else, such as publicly available data or observations.


On this point, more data does not mean more credibility. It is best to collect only what is necessary to answer the evaluation question(s).

We address these misconceptions (and call them damaging) because they have severely altered some people's relationship with evaluation and evaluators. They can create unrealistic expectations that lead to distrust and hostility.

Evaluators aspire to be trusted partners in learning, helping communities and organizations ask good questions, gather credible data, interpret it responsibly, and use it to take action.

© Eval Design Studio 2026