You Understand the Example but Can’t Solve the Next Problem. Here’s Why.
You watch the professor solve it and every step makes sense. Then you open the homework and have no idea where to start. You are not bad at math. You are practicing the wrong skill.
The short answer: if you understand the examples but can’t solve the problems, the reason is that these are two different skills. Following a solution only asks you to recognise that each step is correct. Solving a problem asks you to recall the method and decide which method it needs. Worked examples and grouped homework sets train the first and skip the second. Cover the solution, predict each step and practice with mixed problems to train the part the exam tests.
It is one of the most common complaints in any math or science course. The lecture made sense. The worked example made sense. The textbook solution made sense. Then you sit down with a blank problem and nothing comes. It feels like you understood and then forgot. What actually happened is more interesting, and much easier to fix.
Following is not the same as solving
When you read a worked solution, every step is handed to you. Your only job is to check that it follows from the one before. That is recognition, and recognition is easy. It is why a multiple choice question feels easier than a blank one.
Solving a problem from scratch asks for two harder things. You have to recall the method without a prompt. Before that you have to decide which method the problem needs. A worked example does both of those for you, which is why it feels so smooth. It is also why it leaves you stuck.
This is the same illusion that makes rereading feel productive. In a 2006 study by Henry Roediger and Jeffrey Karpicke, students who reread a passage felt more confident than students who tested themselves. A week later the students who tested themselves remembered far more.
Why homework sets hide the hardest part
Look at a typical problem set at the end of a textbook section. Every problem uses the method from that section. You never have to decide which method to use, because the chapter title already told you.
Exams do not work that way. Problems come mixed together and choosing the method is half the question. Doug Rohrer and Kelli Taylor showed how much this matters in a 2007 experiment. College students learned to find the volume of four unusual solids. One group practiced the problems grouped by type. The other practiced them shuffled together. During practice the grouped students did better, getting 89% right compared with 60%. On a test a week later the result flipped. The shuffled group scored 63%. The grouped group scored 20%.
Mixed practice felt harder and tripled the test score a week later.
Researchers call this interleaving. It works because every shuffled problem forces the decision that grouped practice lets you skip.
How strong students use worked examples
Worked examples are not the enemy. For a beginner they are one of the fastest ways to learn a new method. What matters is what you do while you read them.
In a classic 1989 study Michelene Chi and her colleagues watched students learn physics from worked examples. The strongest students kept explaining the steps to themselves. They asked why each step was allowed and linked it to the principle behind it. The weaker students read the steps and moved on. Later the weaker students leaned heavily on the examples while solving new problems. The self-explainers needed them far less.
A method that fixes it: cover, predict, check
- Cover the solution with a sheet of paper or your hand.
- Read the problem and decide which method it needs before you look. Say why.
- Predict the first step and write it down.
- Reveal only that step and compare. If yours is different, work out why before you go on.
- Repeat for each step until the end.
- Solve a fresh problem of the same type with the example closed.
The first few times this will feel slow. That is the point. Every prediction is a small act of recall, and every mismatch shows you exactly what you did not understand. Researchers have found that gradually removing steps from worked examples until students solve problems alone, an approach they call fading, helps learners make the jump from following to solving.
Try it with the chain rule
Here is the method on a derivative that trips up a lot of calculus students.
Find the derivative of sin(x²).
Decide the method first. The function is sine of something, and that something is not just x. A function inside a function means the chain rule.
Predict step one. Name the outside function and the inside function. The outside is sin(u). The inside is u = x².
Predict step two. Differentiate the outside and leave the inside alone. The derivative of sin(u) is cos(u), which gives cos(x²).
Predict step three. Multiply by the derivative of the inside. The derivative of x² is 2x.
Answer: 2x cos(x²)
The most common mistake here is writing cos(2x). That happens when you differentiate the inside and put the result back inside the cosine. If you made it, you now know exactly which step to practice.
Now try these two with the answers covered. They are deliberately different, so you have to choose the method each time.
1. Find the derivative of e3x + 1.
2. Find the derivative of x² sin(x).
The answers are at the end of the article.
Using AI without falling into the same trap
An AI tutor can make this problem worse or better. A full AI solution is just another worked example, and reading it gives you the same false sense of understanding. In a 2025 study published in PNAS, students who practiced math with a plain ChatGPT-style assistant did better on practice problems and 17% worse on the exam. Students who got hints instead of answers held their ground.
Use AI for the parts of the method that are hard to do alone:
“I think this needs the chain rule because it’s a function inside a function. Is that the right call? Don’t solve it.”
“Here is my working. Tell me the first line that’s wrong but don’t fix it.”
“Give me five derivative problems that mix the chain rule, the product rule and the quotient rule. Don’t label which is which.”
Check any arithmetic an AI gives you. Language models still make calculation mistakes, especially in long problems with many steps. For more on this, read what the research says about studying with AI.
The night before the exam
Make a mixed set of ten problems from different sections of the course, the kind you would expect on the test. Do them without notes and with a timer. Mark every problem where you could not decide which method to use. Those are what to review tonight. Focus less on the ones you got slightly wrong and more on the ones where you did not know where to start.
Answers to the practice problems
1. The derivative of e3x + 1 is 3e3x + 1. It uses the chain rule because 3x + 1 sits inside the exponential.
2. The derivative of x² sin(x) is 2x sin(x) + x² cos(x). It uses the product rule because two functions are multiplied together. If you reached for the chain rule on this one, you have just found the decision that mixed practice trains.
Frequently asked questions
Why can I follow the lecture but not do the homework?
Following a solution only asks you to recognise that each step is correct. Solving asks you to recall the method and decide which one the problem needs. Cover solutions and predict each step to practice recall.
Is it bad to look at the solution when I’m stuck?
Not if you look at one step at a time. Reveal only the next step, cover it again and continue on your own. Reading the whole solution at once gives you the answer without the practice.
How many practice problems should I do?
Fewer than you might think, as long as they are mixed. A shuffled set of problems from different topics trains the method choice that exams test. Ten mixed problems usually teach more than thirty from the same section.
Does this apply outside math?
Yes. It applies to physics, chemistry, economics, statistics and programming. It works in any subject where you learn methods from examples and then have to choose the right one yourself.
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- Rohrer, D. and Taylor, K., “The shuffling of mathematics problems improves learning”, Instructional Science, 2007.
- Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P. and Glaser, R., “Self-explanations: How students study and use examples in learning to solve problems”, Cognitive Science, 1989.
- Roediger, H. L. and Karpicke, J. D., “Test-enhanced learning: Taking memory tests improves long-term retention”, Psychological Science, 2006.
- Renkl, A., Atkinson, R. K., Maier, U. H. and Staley, R., “From example study to problem solving: Smooth transitions help learning”, Journal of Experimental Education, 2002.
- Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö. and Mariman, R., “Generative AI without guardrails can harm learning: Evidence from high school mathematics”, Proceedings of the National Academy of Sciences, 2025.