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Can Learning Be Measured? A Necessary Defense from the Social Sciences
A few days ago, during a conversation with colleagues, one of them firmly stated that “learning cannot be measured”, referring to students’ academic learning. The statement isn’t new —it’s a common claim in certain educational circles— but it reflects a misunderstanding that should be addressed. Especially given its implications for disciplines such as Pedagogy, Psychology, or Sociology, which present themselves as scientific fields aiming to build rigorous knowledge about how people learn.
It’s true that learning is an internal process, invisible to the eye. But it also produces observable manifestations. When someone clearly explains the process of plate subduction, solves an algebra problem, or identifies an ad hominem fallacy in a debate, they are exhibiting behaviors (observable) that reflect learning (not directly observable). These behaviors can be observed, recorded, and therefore, measured.
Denying this would be like saying we can’t measure temperature because we can’t see heat. We don’t see learning directly, but we do see its effects. And, like in many scientific fields, we use tools to translate an unobservable phenomenon into observable indicators.
Measuring is not the same as counting
There is a crucial distinction that is often overlooked: to measure is not simply to count. Measuring means assigning values (numerical or categorical) to phenomena according to specific rules. In this sense, the scale types proposed by S. S. Stevens (1946) —nominal, ordinal, interval, and ratio— remain a useful framework for analyzing measurement processes. Thanks to them, we can adapt our tools to the type of learning we want to assess: from categorized open responses to standardized tests or performance rubrics.
This doesn’t mean reducing learning to a number, but rather finding systematic, valid, and reliable ways to capture its presence and development. Properly done, such measurements allow us to compare, analyze, make informed decisions, and —most importantly— improve teaching and learning processes.
Denying the possibility of measuring learning has serious consequences: it condemns the Social Sciences to speculation without empirical validation, hinders the evaluation of intervention processes (educational ones in the case of Pedagogy), and leaves us without tools to determine whether something works or not.
The Social Sciences don’t advance despite measurement —they advance because they’ve learned to measure complex phenomena rigorously. This includes intelligence, motivation, satisfaction, or skill acquisition. These dimensions have decades of accumulated methodological research that cannot be dismissed with a simple gesture of rejection.
Final reflection for skeptics
To those who distrust measurement in the Social Sciences because they see it as “soft” or imprecise, I invite you to shift your perspective. Instead of asking whether measurement is possible, ask how it can be done. The complexity of the phenomenon doesn’t justify avoiding analysis —it demands it. When Social Sciences take on methodological challenges seriously, they don’t just describe the world —they help transform it through informed understanding.
Rejecting measurement doesn’t make us more human. It makes us less capable of improving the education we provide.
Selected bibliography
- Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957
- Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.
- Popham, W. J. (2009). Assessment literacy for teachers: Faddish or fundamental?. Theory Into Practice, 48(1), 4–11.
- Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academies Press.
- Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749.b
