Education · Guide
A Practical Guide to Study Techniques and Note-Taking That Research Supports
Published:

In brief
The learning techniques with the strongest research backing are practice testing (self-quizzing) and distributed practice (spacing study sessions over time), according to a widely cited review of the scientific literature. Popular habits like highlighting and rereading show only weak effects, while structured note-taking systems such as the Cornell method add a built-in way to retrieve and review material.
Students have long relied on habits such as highlighting, rereading and summarizing, but a 2013 review of the scientific literature found that these common strategies deliver only modest benefits. The review, led by cognitive psychologist John Dunlosky and colleagues, rated ten widely used study techniques from least to most supported by evidence, in an analysis summarized by the American Federation of Teachers (AFT).
The techniques with the strongest evidence
Two techniques stood out as having the strongest research support: practice testing and distributed practice. Practice testing means actively retrieving information from memory, for example through self-made quizzes or flashcards, rather than simply rereading notes. According to the AFT summary of Dunlosky's work, college students who used practice testing earned higher grades, and middle-school students who took daily practice quizzes performed better on later assessments.
Distributed practice means spacing study sessions over days or weeks instead of cramming in one sitting. The AFT summary notes that students who distribute their practice over time retain material longer than those who cram, even when total study time is the same. A related technique, interleaved practice - mixing different types of problems within a study session rather than practicing one type at a time - was rated as moderately effective in the same review. In a geometry study cited by the AFT, students who interleaved different problem types scored roughly three times better on a later exam than students who massed one problem type at a time, even though the interleaved-practice group performed worse during the practice sessions themselves.
A separate 2021 synthesis published in the journal Frontiers in Education put numbers on these effects using a statistical measure called effect size: distributed practice had an effect size of 0.85 and practice testing 0.74. The same analysis found that effects were considerably stronger for lower-ability students (effect size 0.47) than for higher-ability students (effect size -0.11), and that most of the underlying studies measured short-term recall of factual material rather than deep conceptual understanding.
Techniques with weaker or mixed evidence
Highlighting, underlining, rereading and summarizing are among the most commonly used strategies - the AFT summary notes that 84 percent of surveyed students relied on rereading - but Dunlosky's review found their benefits were inconsistent. One study referenced in the review found that students who highlighted text performed worse on questions requiring them to make inferences, compared with students who did not highlight. Summarizing and imagery-based techniques were found to require extensive training before they produced reliable gains, which limits their practicality for most students.
Two further techniques, elaborative interrogation and self-explanation, showed promise but were judged to need more research before being rated among the most effective. In one logical-reasoning experiment cited by the AFT, students who used self-explanation - talking through why a step in a problem is correct - scored about three times higher on a later test than students who did not, roughly 90 percent versus under 30 percent.
| Technique | Effect size (Cohen's d) |
|---|---|
| Distributed practice | 0.85 |
| Practice testing | 0.74 |
| Underlining | 0.44 |
| Summarization | 0.44 |
Note-taking: the Cornell method
One structured note-taking system frequently recommended alongside these techniques is the Cornell method, created by Walter Pauk, who directed Cornell University's Reading and Study Skills Center, and introduced in his 1962 book 'How to Study in College,' according to Wikipedia's entry on Cornell Notes and a detailed guide published by e-Student.org. The method divides a page into a narrow left-hand 'cue column,' a wider right-hand note column, and a summary strip at the bottom.
- Record: during a lecture or while reading, write main ideas in the note column using brief, abbreviated phrases rather than full sentences.
- Reduce: after the session, fill the cue column with questions or keywords based on the notes, and write a short summary at the bottom of the page.
- Recite: cover the note column and try to answer the cue-column questions from memory.
- Reflect: assess which topics are still unclear and need further study.
- Review: revisit the notes regularly, especially before tests or new lectures.
The 'recite' step is essentially a built-in form of practice testing, which is why some researchers link the Cornell method to the retrieval-practice effects documented in the broader learning-science literature. However, e-Student.org's review notes that direct research on the Cornell system itself is limited: a 2012 study of high-school students found no positive impact, while a 2019 study of Egyptian university students learning English as a foreign language found a significant positive effect on critical-reading skills. The guide concludes there is 'no general scientific consensus' on the method's effectiveness, even though note-taking in general is associated with better learning outcomes.
Putting the evidence into practice
- Replace passive rereading with self-quizzing: close the book and try to recall key points before checking notes.
- Spread study sessions across several days or weeks instead of concentrating them before a single deadline.
- Use any note-taking format - Cornell or otherwise - that forces a later retrieval step, such as covering notes and answering self-written questions.
- Treat highlighting and underlining as, at most, a first pass for identifying material to actively study later, not as study in itself.
- When using self-explanation, explain in your own words why a step or fact follows from what came before, rather than restating it verbatim.
The advantage of practice testing with feedback over restudy is extremely robust.
Questions
- What is the single most effective study technique according to research?
- A widely cited 2013 review by John Dunlosky and colleagues, and a 2021 synthesis in Frontiers in Education, both rank distributed practice (spacing study over time) and practice testing (self-quizzing) as the techniques with the strongest evidence, with the 2021 analysis giving them effect sizes of 0.85 and 0.74 respectively.
- Does highlighting help you study?
- Research summarized by the AFT found highlighting's benefits are inconsistent, and one cited study found students who highlighted performed worse on inference questions than students who did not; the 2021 Frontiers in Education review gave underlining a lower effect size of 0.44.
- Is the Cornell note-taking method proven to work?
- According to e-Student.org's review, there is 'no general scientific consensus' on the Cornell system specifically - a 2012 high-school study found no positive impact while a 2019 study on Egyptian EFL students found a significant positive effect on critical reading - though its built-in recitation step overlaps with the well-supported practice-testing effect.
- Why is cramming less effective than spaced study?
- The AFT summary of Dunlosky's review notes that students retain material longer when they space study sessions over time rather than cramming, even with the same total study time. A related geometry study on interleaved practice - mixing different problem types rather than practicing one type at a time - found students scored about three times better on a later exam than those who massed their practice, though the interleaved group performed worse during practice itself.
- Does spaced practice help all students equally?
- No. The 2021 Frontiers in Education synthesis found effects were considerably stronger for lower-ability students (effect size 0.47) than for higher-ability students (effect size -0.11).
Sources
This piece was prepared with artificial intelligence and passed an automated source check before publication. If you see an error, ask for a correction. Request a correction or reply · How we make this
Related

EducationArticle
How Spaced Repetition Improves Memory: The Science Explained
Spaced repetition works because reviewing information just as it starts to fade — rather than cramming it all at once — forces the brain to strengthen the memory each time it is retrieved. Decades of cognitive-psychology research, from Hermann Ebbinghaus's 1885 forgetting-curve experiments to modern meta-analyses, show that spaced study produces longer-lasting recall than massed practice using the same amount of time.

EducationGuide
Science-Based Study Strategies That Actually Improve Exam Performance
Decades of cognitive-psychology research point to a small set of study methods with proven effects on exam results: self-testing, spaced-out study sessions, mixed-topic practice, and adequate sleep. Popular habits like rereading notes and highlighting text show far weaker results in controlled studies. This guide lays out the evidence and how to apply it to exam preparation.
EducationGuide
Critical Thinking and Problem-Solving: A Practical Guide for Students and Professionals
Critical thinking means objectively analyzing information against clear standards before deciding what to believe or do, while structured problem-solving means identifying a problem's root cause before acting on it. Employers rank analytical thinking among the most in-demand skills through 2030, and established frameworks from critical-thinking researchers and Harvard Business School/Review outline concrete, repeatable steps to build these abilities.
EducationArticle
How Artificial Intelligence Actually Works, and Why It Matters Now
Artificial intelligence is software that infers, from data it is given, how to produce outputs such as predictions, content or decisions — it does not "think" the way a person does. Most of today's AI runs on machine learning, where algorithms find patterns in large datasets rather than following rules written by hand. Adoption has jumped sharply: 78% of organizations reported using AI in 2024, up from 55% a year earlier, according to Stanford's AI Index.