Stop Treating Note-Taking Like Learning

Walk into almost any classroom and you can still predict the workflow: the teacher talks, students copy, and the notebook becomes proof that learning happened.

That routine survives because it is easy to manage. It is also outdated.

I have been arguing this for years, starting with a simple claim from my 2008 article, Ditch Paper and Get to the Thinking Faster: “Taking notes is never necessary. Everything is posted.” That was not theory. It was a description of what happened when my professional development went paperless: participants walked in, downloaded the materials, and used their time to think, discuss, and produce instead of copying.

The real problem is transcription, not the tool

The laptop versus handwriting debate is often framed as a verdict: handwriting is better.

A more honest reading is that the real risk is verbatim transcription and shallow processing. In Mueller and Oppenheimer (2014), the key difference is not the device itself, but that laptop note-takers were more likely to type verbatim, which can reduce the kind of generative processing that supports understanding.

A direct replication, Urry et al. (2021), found that while laptop note takers typed more and showed more verbatim overlap, the expected longhand advantage on immediate quiz performance did not reliably appear.

So the question schools should be asking is not “pen or keyboard?” It is “are we designing learning that turns students into transcription machines instead of thinkers?”

Note-taking is not learning, even when it works

Notes can be useful as a personal thinking tool, but they should not be the primary classroom task. The most useful research frame is not tool-based. It is purpose-based.

Across decades of work, researchers distinguish between the “encoding” benefit (processing while writing) and the “external storage” benefit (having notes available later). Encoding effects exist, but they are generally modest, and depend heavily on conditions and what students actually do while taking notes (see Kobayashi’s meta-analysis).

Translation: copying more does not mean learning more.

If notes are the output, give students the notes

This is the move schools resist for cultural reasons: provide students the notes, slides, and materials up front. When you do, class becomes the place students work with ideas, not copy them.

I put it bluntly in 2016 in my article, Don’t Waste Student’s Time with Note Taking. Something Better.: “Teachers must update their practice by removing notetaking from the work their students do.” In the same post I describe the alternative as routine practice: students get the teacher notes, slides, videos, transcripts, and materials so they can spend class time making meaning instead of recording words.

Research backs that stance. Students’ notes are often incomplete, and instructor-provided notes can improve achievement, especially when students use them for review (see Kiewra, 1985, Providing the Instructor’s Notes).

This is not lowering expectations. It is removing busywork that masquerades as rigor.

Lecture culture is the real problem

If students are taking notes because they are trapped in lecture, the bigger issue is the model.

In Moving From Lecture to Learning, I lean on Harvard professor Eric Mazur’s critique and make the point plainly: students do not need to “watch someone talk and furiously copy down notes.” A speaker can provide notes, transcripts, materials, and video for review, and then class time can shift to learning.

When you stop treating information delivery as the main event, you can design class around what students can do with information.

The equity angle schools avoid naming

The “notes equal learning” model is disproportionately common in environments where compliance is treated as achievement. Meanwhile, many students with more access get more of the learning experiences that demand doing, making, presenting, and iterating.

Opportunity-to-learn gaps are not only about resources. They are also about who gets consistent access to higher-order learning experiences. In some schools, students spend class time copying. In others, they spend it presenting, building, and defending ideas. The Learning Policy Institute’s Equity and ESSA report is a strong reference point for naming this clearly.

Where AI note-taking fits now

Artificial intelligence (AI) transcription and summarization tools make the old argument for mass manual note-taking even weaker. Capture is becoming automated.

That does not mean learning is automated. It means educators can stop pretending capture is the goal, provide the materials up front, and use class time for judgment, synthesis, and creation.

For an example of how this looks in practice, read my Tech & Learning post, Forget Taking Notes. 2 Strategies to Get to Thinking & Sharing Faster, which describes a simple system: shared digital materials plus collective, networked capture, so participants can get to the thinking faster.

What replaces note-taking

Once students are not stuck in capture mode, class can be built around real work:

• Analyzing a case or scenario

• Designing a solution for an authentic audience

• Building, testing, and revising

• Defending choices with evidence

• Publishing work that leaves the classroom

This shift is not just philosophy. A major meta-analysis (Freeman et al., 2014) found that active learning improves performance and reduces failure rates in STEM (science, technology, engineering, and mathematics) courses. Project-based learning (Chen and Yang, 2019) shows positive effects on academic achievement across studies as well.

The point is not the notes. It is the learning design.

The debate about note-taking is often framed as a choice between tools: paper or laptop, handwriting or typing. That framing misses the point. The real question is what we want students doing with their limited time and attention.

If we treat transcription as learning, we will keep designing classrooms where the safest move is to copy, comply, and wait for the “right answer.” If we provide the content up front, we can design for what matters: making meaning, building, revising, and creating for real audiences.

This is also an equity issue. Students with the most access often spend less time proving they were paying attention and more time doing work that proves they can think. All students deserve that.

AI makes the choice even clearer. Capture has become automatic, so schools need to stop spending human brainpower on it. Give students the slides. Give them the transcript. Give them the notes. Then use learning time for judgment, synthesis, and creation.

When we stop grading students on capture, we can start designing for thinking and work that leaves the room. If you want a concrete starting point, begin where I began years ago: Don’t Waste Student’s Time with Note Taking. Something Better. and Ditch Paper and Get to the Thinking Faster.

Laptops Did Not Take Away Their Brains. The School Model Did.

Neuroscientist Jared Cooney Horvath's new book and article argue that when we gave students laptops, student performance declined, so the tech broke their brains.

That story skips the real culprits:

1. high-stakes standardized testing that reshaped public schooling, and

2. inequitable access to effective models of learning, not to devices.

Students in well-resourced schools are more likely to experience project-based, passion-driven models where technology is used for real-world work. Students in under-resourced and segregated schools are more likely to sit in “drill and kill” environments, whether the drill is on paper or on a screen.

Drill and kill is the problem. The model and the instruction, not the laptop, drive outcomes.

When Testing Took Over, Powerful Models Got Squeezed Out

In the 1990s and early 2000s, the No Child Left Behind Act (NCLB) locked public schools into annual high-stakes testing in reading and math. Research on that era is clear: it narrowed the curriculum and increased time spent on test preparation, especially in high-poverty schools.

At the same time, computers were entering classrooms. The timing is not an accident. As accountability pressure grew, many districts used technology to deliver more “practice” and test-aligned content.

Models like the Schoolwide Enrichment Model, Montessori, and other talent development approaches depend on flexible time, original investigations, and creative products. Scholars such as Joseph Renzulli warned that rigid standards and high-stakes testing could turn standards into a “new cage,” crowding out enrichment and creativity.

Public Montessori schools show this tension clearly. Studies note that accountability demands for state test scores push public Montessori programs to compromise core principles built around multi-age, student-directed exploration with minimal testing.

So the period people point to as “the time laptops ruined learning” is also the period when high-stakes testing made it difficult or impossible for many public schools to adopt or sustain Schoolwide Enrichment, Montessori, and similar models at scale.

We did not just add laptops. We changed the rules so that deep, interest-driven learning became harder to run in public systems.

SAMR: Why Shallow Tech Use Makes Things Worse

The Substitution, Augmentation, Modification, Redefinition (SAMR) model, created by Ruben Puentedura, is a simple way to think about tech integration.

• Substitution: typing instead of handwriting.

• Augmentation: adding spellcheck, comments, or formatting.

• Modification: redesigning tasks, for example, real-time collaborative writing.

• Redefinition: doing tasks that were not realistic before, such as publishing multimedia projects to authentic audiences or collaborating globally.

Most uses of technology in drill-heavy environments never get past substitution, and often are not even good substitution. A bad worksheet becomes a bad online quiz. A shallow test becomes a shallow test with a progress bar.

The Organisation for Economic Co-operation and Development (OECD) says the same thing in its “Students, Computers and Learning” report: technology can amplify great teaching, but great technology cannot compensate for poor pedagogy and unchanged models. 

When tech is stuck at the lower levels of SAMR inside a test prep model, results are predictably weak or worse. That is not because laptops melt brains. It is because we are using powerful tools to automate low-quality tasks.

What Programme for International Student Assessment (PISA) Actually Shows

Critics often point to the Programme for International Student Assessment (PISA) to argue that computers hurt learning.

The OECD’s own analysis shows something more nuanced:

• Students who use computers moderately at school tend to perform slightly better than those who rarely use them.

• Students who use computers very frequently tend to do worse, even after controlling for background.

That is an inverted U curve. It matches what you would expect if:

• Limited, purposeful tech use supports learning.

• Very intensive, unfocused, or low-level use correlates with worse outcomes.

And crucially, PISA is correlational; it does not show that computers caused the decline, only that heavy, low-quality use tends to show up where scores are already lower.

The same reports note that heavy spending on devices on its own has not produced big gains in reading, mathematics, or science, and emphasize that how technology is used and whether students can navigate digital texts matter as much as access.

So PISA does not say “laptops broke kids.” It says that bolting devices onto a drill and test model, and especially overusing them, is a losing strategy.

The Data Story Is Skewed By Who Is Being Tested

There is another missing piece. The population of students taking these tests has changed.

The National Center for Education Statistics (NCES) reports that the U.S. “status dropout” rate for 16 to 24-year-olds fell from 7.0 percent in 2012 to 5.3 percent in 2022, with declines across every major racial and ethnic group.

At the same time, the percentage of public school students who are English learners increased from 9.4 percent (4.6 million students) in 2011 to 10.6 percent (5.3 million students) in 2021.

That means:

• More students who would previously have dropped out are still in school and being tested.

• More non native English speakers, often in under-resourced schools, are taking tests largely in English.

Layer on persistent poverty and segregation, and it is obvious that average scores reflect who is sitting for the tests, not just what tools they are using.

If you ignore dropout, language, and disadvantage, then blame laptops for every score change, you are not doing serious analysis.

Teacher Preparation Has Not Caught Up

Most teachers were never trained to use technology beyond substitution.

The International Society for Technology in Education (ISTE) and other reviews of teacher preparation programs show that many new teachers feel underprepared to integrate technology meaningfully into instruction.

As artificial intelligence arrives, early studies suggest schools of education tend to frame AI as a plagiarism threat rather than as a tool for feedback, differentiation, or creative tasks.

So we have:

• Devices arriving quickly.

• High-stakes tests shaping what “counts.”

• Teacher prep that rarely takes educators past substitution on the SAMR ladder.

Under those conditions, most tech use will be shallow, and outcomes will reflect that.

Where EdTech Leaders Should Focus

If you work in or around education technology, the takeaway is not “get rid of laptops.” It is “stop letting weak models and shallow uses define the narrative.”

Key priorities:

• Reclaim space from high-stakes testing so models like Schoolwide Enrichment, Montessori, Big Picture, and Agile Learning can exist in public schools, not just in boutique or private settings.

• Push technology up the SAMR ladder, toward modification and redefinition, especially for real-world projects, creative production, and authentic audiences.

• Demand that schools of education treat technology and AI integration as core pedagogy, not extras.

• Read test data through the lens of dropout, language, and poverty, not only through the lens of device counts.

You can absolutely decide when to close laptops and pull out paper, or when to limit phones. That is tactical.

Strategically, the question is: are we building models where technology supports real-world, passion-driven learning for all students, or models where tech is just a faster worksheet?

Laptops did not take away their brains. High-stakes testing, inequitable school models, shallow tech integration, and weak preparation did. Fix those, and the same devices people blame today become some of the best tools we have for helping students do meaningful work that actually uses their brains for real and relevant learning. 

Help Adults to Stop Embarrassing Themselves By Spreading Misinformation with SIFT

In my new article for Tech & Learning, I take a hard look at how adults, who should know better, continue to spread misinformation. Not because they’re bad people, but because they were never taught the skills we now teach students. I break down the SIFT method and show how schools can help both students and adults verify information responsibly. 

If you’re tired of seeing plausible-sounding falsehoods go viral, this one’s for you.

Read it here: https://www.techlearning.com/technology/social-media/closing-the-media-literacy-gap-why-adults-need-sift-as-much-as-students