Concrete Examples: A Learning Strategy for Abstract Ideas

The concrete examples learning strategy means pairing each abstract idea you study with specific, real cases, then comparing several cases that look different but share the same underlying idea. "Opportunity cost" is a definition. "The concert you miss because you took a Saturday shift" is something you can picture, reason about, and remember.

One example helps. Two or three different examples, compared side by side, help more, because the comparison shows you which parts of an example are the concept and which are just details. That second step is the one most students skip.

This guide covers the research on learning from examples, the ways examples can backfire, a six-step method, one abstract idea worked through four subjects, and prompts for writing your own.

What is the concrete examples learning strategy?

It's one of six study strategies that cognitive psychologists Yana Weinstein, Christopher Madan, and Megan Sumeracki highlighted in a 2018 review, alongside spaced practice, interleaving, retrieval practice, elaboration, and dual coding. The idea rests on a basic finding about memory: concrete words like "button" are recognized and recalled better than abstract words like "bound", and information you can picture is easier to learn.

Most of what you study is abstract: definitions, laws, principles, theories. A concrete example turns one of those into a situation:

Does learning from examples work? What the research shows

The evidence is strongest for two things: studying examples alongside definitions, and comparing several examples rather than studying one.

Examples beat rereading definitions. Katherine Rawson, Ruthann Thomas, and Larry Jacoby ran three experiments in which students studied definitions of concepts, followed either by illustrative examples or by extra time with the definitions. On a later test, students saw new examples and had to name the concept each one illustrated. Students who had studied examples did much better, with large effect sizes (d = 0.74 to 1.67). Showing the example before or after the definition made little difference. Whether examples of different concepts were mixed together, and whether the definition stayed in view, did matter.

Comparing several examples beats studying one. In classic problem-solving studies by Mary Gick and Keith Holyoak, people were more likely to apply a principle to a new problem after seeing two examples of it than after seeing one. A 2013 meta-analysis by Louis Alfieri and colleagues pooled 57 experiments (336 tests): comparing cases side by side produced more learning than studying the same cases one after another, a single case, or traditional instruction, with a moderate average effect (d = 0.50). Comparison helped more when learners were asked to find similarities and when the general principle was given after the comparison.

It works for procedures too. Bethany Rittle-Johnson and Jon Star randomly assigned 70 seventh graders to learn equation solving by comparing two solution methods side by side or by studying the same methods one at a time. The comparison group gained more procedural skill and flexibility, with similar gains in conceptual knowledge.

When do concrete examples backfire?

Examples aren't automatically good. Three traps show up in the research.

All the examples share the same surface features

In a 2008 Science paper, Jennifer Kaminski, Vladimir Sloutsky, and Andrew Heckler taught college students a simple mathematical rule system. Students who learned it with generic symbols applied it to a new situation better than students who learned it through concrete scenarios such as measuring cups of liquid. Headlines suggested dropping concrete examples from math class.

Later work told a narrower story. The Learning Scientists pointed out that the concrete examples in the study all shared similar surface features, so students could latch on to those instead of the rule. And when Dragan Trninic, Manu Kapur, and Tanmay Sinha ran a replication with improved concrete examples, students did as well as, or better than, those who learned with abstract symbols. The lesson: badly chosen examples hurt; well-chosen, varied ones don't.

You remember the example, not the idea

Novices tend to notice an example's surface (moths, pollution, a particular year) and miss its structure. Experts see past the surface. If your only example of natural selection is the peppered moth, you may come away thinking natural selection is about color changes. Varied examples and an explicit "what do these share?" step guard against this.

The example includes interesting but irrelevant details

Shannon Harp and Richard Mayer found that students who read science passages containing "seductive details" (interesting but irrelevant facts) recalled fewer main ideas and solved fewer transfer problems. A funny, relevant example is fine. A vivid example whose memorable part has nothing to do with the concept steals attention from what you need.

One more caution from Weinstein and colleagues' review: when an example and the stated rule disagree, students tend to follow the example. A wrong example teaches a wrong rule, so check examples you find online or generate yourself.

How to use concrete examples: a 6-step method

1. Write the definition in one sentence, in your own words. If you can't, reread the source before going further.
2. Collect two or three examples that look different. Pull one from the textbook, one from lecture, and one from your own life or the news. Different settings, same concept.
3. Line them up and name what they share. Finish the sentence "In every case, ___." That sentence is the concept. Cross out anything that appears in only one example.
4. Add a near miss. Find something that looks similar but isn't an example, and say why. Positive punishment versus negative reinforcement is a classic pair.
5. Generate a new example of your own. Producing it yourself, rather than reading one, is the generation effect at work. Check it against your sentence from step 3, or ask your teacher or TA. Don't trust an unchecked example.
6. Test yourself later. A day or two later, classify unlabeled examples and write a fresh example from memory. That's active recall applied to understanding.

For math and science, a related approach called concreteness fading moves in stages: start with a concrete representation, then gradually strip it down to the abstract form. A 2014 review by Emily Fyfe and colleagues recommends exactly this sequence. For fractions, that might be pizza slices, then shaded circles, then the symbol ¾.

One abstract idea across four subjects: negative feedback

Here is the method applied to one concept that shows up in biology, physiology, engineering, and economics.

Definition (step 1): A negative feedback loop is a process in which a change in a system triggers a response that reverses the change, pulling the system back toward a set point.

Examples (step 2):

What they share (step 3): In every case, there's a target level, something that detects a move away from it, and a response that pushes the opposite way. Hormones, machines, and money are incidental.

Near miss (step 4): Contractions during childbirth. Contractions trigger release of oxytocin, which makes contractions stronger. The response amplifies the change instead of reversing it, so this is positive feedback.

Your own example (step 5): Cruise control in a car. Going uphill, the car slows (change); the system adds engine power (response) until the car is back at the set speed (target). All three parts are present, so it counts.

Notice what the comparison did. After one example, "negative feedback" might have meant "something to do with hormones." After four, you know it's a structure you can spot anywhere.

Prompts to generate your own examples

Making your own examples is harder than reading the textbook's, which is part of why it helps. Pick the row for your subject and answer the prompt for one concept from this week's notes:

If you can't come up with an example, you've found a gap in your understanding. Go back to the definition and the textbook examples, then try again. Explaining why your example fits, out loud, is a form of self-explanation and makes the gap obvious.

Turn your examples into practice questions

Examples become far more useful when you test yourself with them rather than just reading them.

• Classification cards. Put an unlabeled scenario on the front and the concept on the back: "A student finishes homework early to stop a parent's nagging. Which concept?" Mix in scenarios for concepts you tend to confuse; mixing similar categories is the idea behind interleaving.
• Generation cards. "Give a new example of opportunity cost that isn't in your notes." Check your answer against the definition each time. Our guide on how to use flashcards effectively covers how to write and schedule these.
• Why cards. "Why does a market surplus push prices down?" Asking why an example works is elaborative interrogation, a natural partner to this strategy.
• Quiz questions. Multiple-choice items that describe a situation and ask which concept applies test exactly this skill. See how to write practice quizzes.

The goal is transfer: recognizing the concept in a situation you've never seen before, which is what most exam questions ask you to do.

Build a classification drill with an AI assistant

Writing a dozen unlabeled scenarios by hand takes a while. An AI assistant can draft them from your notes, as long as you do the classifying and check every scenario against your definitions, because generated examples can be subtly wrong. Paste the definitions after a prompt like this:

I'm studying [course]. Below are my definitions of [concept A],
[concept B] and [concept C]. Write 9 short, realistic scenarios: two
for each concept, each from a different setting (home, work, school,
the news), plus three near misses that look like one of the concepts
but aren't. Shuffle them and don't label them. After I've classified
all 9, give the answers and explain each near miss in one sentence.

Score yourself, then turn every scenario you missed into a classification card. The pairs you keep confusing, such as negative reinforcement and positive punishment, are the ones to drill again next week. Our list of ChatGPT prompts for studying has more prompts that make you do the thinking.