Classroom Practice & Educational Technology: Attention First, Screens Second

Watch a good teacher open a lesson and you can miss what they are doing. Pupils walk in, pick up a sheet, and begin a short task before anyone has been told to. Nothing dramatic happens. But three minutes of the fifty have been saved, and the tone is set. Now watch a lesson in which the teacher asks for books out, waits for quiet, repeats the instruction, and hands out materials one desk at a time. Same room, same pupils, and ten minutes are gone.

Education technology walks into these rooms with big promises. A laptop for every child. Software that adapts to each pupil. A tutor chatbot that never tires. In 2023 and 2024, governments from the Netherlands to France moved the other way and restricted phones in schools, and UNESCO's monitoring reported that about 40% of education systems had some school phone ban by 2024. Both movements claim to be about learning. What does the evidence say about each?

This piece grades it. Part one covers what a classroom runs on, with evidence that does not need a screen. Part two grades each kind of technology use separately, because one number for "technology" hides everything that matters. Where the evidence is thin, it says so.

Attention is the first thing a classroom runs on

In 1970 the researcher Jacob Kounin filmed classrooms and asked what separated teachers whose pupils stayed on task from those whose pupils drifted. The answer was not how teachers punished misbehaviour. It was how they ran the lesson. His four labels still turn up in training: withitness (the teacher visibly knows what is happening everywhere in the room), overlapping (dealing with an interruption without stopping the lesson), momentum (no dead stretches) and smoothness (no abrupt jumps in direction). A teacher who is withit stops a small problem before it has an audience.

The practical form of this is routines: the same way of entering, handing out, asking for attention and finishing, taught early and practised until it is automatic. A routine costs minutes to teach and returns them every lesson. The interactive counts the minutes. It is plain arithmetic on your own numbers.

Without JavaScript: six transitions of 90 seconds in a 50-minute lesson cost 9 minutes, 18% of the lesson, and about 135 hours over a year at five lessons a day.

Good routines are not about control for its own sake. They free the minutes in which pupils can think, and they let a teacher spend attention on the pupil who is stuck, not the pupil who is loud.

Ask, wait, check: seeing what the whole room thinks

A teacher explains, asks "does that make sense?", sees a few nods and a raised hand, and moves on. Half the class is lost. The nods are not evidence. Barak Rosenshine's 2012 summary of the research on effective teaching, Principles of Instruction, puts it as a short list: review prior learning, present new material in small steps, ask many questions and check the responses of all pupils, give models and worked examples, guide practice, and keep practising until the skill is automatic. The checking step is what turns teaching into a loop.

A second finding is about silence. Mary Budd Rowe's studies in the 1970s found that teachers typically wait only about a second after asking a question. When they were trained to wait three seconds or more, answers got longer and more pupils took part. A pause feels long to the one holding it.

Without JavaScript: if a quarter of the class is confused, asking one fair-sampled pupil spots it 25% of the time, three pupils 58%, ten pupils 94%. Mini-whiteboards show all 30.

That is why teachers use cold-calling (anyone might be asked) and all-pupil responses such as mini-whiteboards or hands-up voting on a multiple-choice question: they raise k from one volunteer towards the whole room. Doug Lemov's Teach Like a Champion catalogues these moves.

Reading an effect size, and why one trial cannot settle it

Most of the research in this piece is reported as an effect size: how many standard deviations the average treated pupil moved compared with the comparison group. A value of 0.2 means the typical treated pupil ends up where the 58th percentile of the comparison group would be. John Hattie's Visible Learning (2009) pooled thousands of such numbers and suggested 0.40 as a "hinge point" for what is worth doing. Critics including Terry Wrigley and Robert Slavin answer that studies differ in design, age and the outcome measured, so pooling them into one league table mixes unlike things. Matthew Kraft (2020) proposed benchmarks calibrated to rigorous education trials: under 0.05 is small, 0.05 to 0.20 medium, 0.20 and above large. Seen that way, many real interventions are "large" at 0.2.

Without JavaScript: d = 0.2 puts the typical treated pupil at the 58th percentile of the comparison group; a random treated pupil beats a random comparison pupil 56% of the time.

Now the other half: whether one trial can even see such an effect. A small study often cannot. If the true effect is 0.2 and you compare two groups of 25 pupils, you will find a clear result only about one time in nine, and you will see a difference in the wrong direction about one time in four. When whole classrooms are assigned to a method, the effective sample is the number of classrooms, which is smaller still. That is why a single enthusiastic school pilot proves little, and why large randomised trials and reviews carry the weight.

Without JavaScript: with a true effect of 0.2 and 25 pupils per group, power is about 11%; with 400 per group it is about 81%.

Technology, graded use by use

"Does technology help learning?" is like asking whether paper helps learning. It depends what is written on it. The EEF's Teaching and Learning Toolkit puts digital technology at about four months of extra progress on average, but the reviews behind that average range from about two months to more than a year, and the strand lost a padlock of confidence for that reason. A 2020 review of randomised trials by Escueta, Quan, Nickow and Oreopoulos reached the same shape: simply giving pupils access to devices did not reliably raise achievement, while software that changed what pupils practised, mostly in maths, often did.

Grade five uses before you read the verdicts.

Without JavaScript: adaptive maths software, promising; a laptop for every pupil with no change in teaching, little or no effect; typed versus handwritten notes, mixed; unguarded AI chat for practice, early evidence of harm; school phone restrictions, mixed or too early.

The AI case deserves a sentence of its own because it is new. In a field experiment with nearly 1,000 high-school maths pupils in Turkey (Bastani and colleagues, PNAS, 2025), pupils who practised with a chatbot that simply answered like a general assistant did better on the practice problems and then scored 17% lower on an exam taken without it, compared with pupils who never had access. A version built to give hints without giving answers largely removed that harm. It was one school in one semester, so it is a caution about design, not a ruling on AI. Practising is how people learn; a tool that does the practising for them can remove the learning.

Adaptive practice: the screen use with the best record

Where technology does well, the mechanism is usually the same. A class has pupils who are two years behind and pupils who are two years ahead, and one worksheet at grade level wastes the time of both. Adaptive software gives each pupil items just beyond what they can already do. In a well-known Indian trial of the Mindspark programme (Muralidharan, Singh and Ganimian, 2019), pupils in after-school centres gained about a third of a standard deviation in maths over a few months. The reason is not the screen. It is that the software did what one teacher with 40 pupils cannot: matched the task to the child, again and again.

Educational psychologists describe the target as the zone just beyond current skill, the idea behind Vygotsky's "zone of proximal development". The toy below encodes that assumption: a pupil gains most from items near their level and almost nothing from items far above it. It is a model of the mechanism, not a measurement, but the pattern it shows is the argument behind adaptive tools and behind a teacher who groups flexibly.

Without JavaScript: a pupil two grade levels behind who works 300 grade-level items gains almost nothing in this model; with matched items the same pupil gains about half a grade level.

The model's lesson cuts both ways. Matched practice helps the pupil who is far behind; it also depends on the software choosing well, on pupils actually using it, and on a teacher who connects it to the lesson. Reviews find that programmes fail more often than their sales pages say.

Phones, wellbeing and the argument

Phones are the most argued-over technology in school, partly because two questions get tangled. The first is about attention in lessons: do phones in a classroom, in a pocket or on a desk, cost learning? The second is about wellbeing: has smartphone use harmed young people's mental health, and should schools act on that?

On the first, the case for restriction is plain. A phone is built to pull attention, and its owner is not the only one affected: the OECD's PISA 2022 reported that many pupils were distracted by their own or others' devices in maths lessons. A study of English schools by Beland and Murphy (2016) found test scores rose after phone bans, most for the lowest-attaining pupils. A ban is cheap to run and easy to explain. The cautious reply is that most of the evidence is observational, that schools which ban phones differ in other ways, and that a ban does not teach pupils to manage their attention elsewhere.

On the second, the sides are further apart. Jonathan Haidt (The Anxious Generation, 2024) argues that a phone-based childhood, spreading from about 2010, is a main cause of rising teen anxiety and depression, and that schools are the easiest place to take phones out of children's days. Critics such as Candice Odgers and Amy Orben answer that the evidence is mostly correlational and effect sizes are small: Orben and Przybylski (2019), analysing large datasets, found that digital technology use explained at most about 0.4% of the variation in adolescent wellbeing. Haidt's side replies that average screen time is a poor measure of what harm looks like. Each is a position held by researchers who have read the same studies. Evidence of harm to wellbeing has not been settled, and nor has evidence of safety.

This piece describes research and is not medical or mental-health advice. If a young person you know is struggling, a doctor or a local service can help; findahelpline.com lists free helplines by country.

A fair reading separates the claims. Restricting phones during lessons is a modest, plausible attention measure with some supporting evidence. Claims that bans will fix teen mental health go well beyond what has been shown. Claims that phones are harmless to learning go beyond it the other way.

What a graduate just sees

• Lessons run on attention, and routines are an investment: minutes taught once are returned every day.
• Nods and a volunteer are not evidence; checking many pupils' thinking is what turns teaching into a loop.
• A pause after a question is not dead air, and it is longer than it feels.
• An effect size means little until you know the design, the age and the outcome, and 0.2 can be a large effect.
• A small trial can miss a real effect, and a single pilot proves less than a large randomised trial.
• "Technology" is not one thing: graded by use, adaptive practice has the best record and handing out devices alone the weakest.
• A tool that does the practising for a pupil can remove the learning.
• Phones in lessons and phones in mental health are separate questions with different amounts of evidence.

Where it shows up next

• Assessment & Measurement (36/55): the checking step and the reliability of any score you use to decide who needs help.
• Education Systems & Policy Worldwide (36/59): national phone rules, device programmes and testing policy are the same arguments at the scale of a country.
• Learning Sciences (36/53): retrieval, spacing and worked examples are what a good routine and a good check are made of.
• Inclusive & Special Education (36/57): adaptive tools and assistive technology serve pupils for whom the one-size lesson fails.
• In the world: teacher training, school leaders deciding on a phone policy, and anyone buying software for a school.

If you want to go deeper

• 📘 Textbook. Carolyn Evertson and Edmund Emmer, Classroom Management for Elementary Teachers (Pearson), with the matching secondary text, the standard classroom-management course books; and Richard Mayer, Multimedia Learning (Cambridge University Press), the research behind how to design screen-based explanations. Check for the current editions.
• 📘 Gentler. Doug Lemov, Teach Like a Champion (3rd edition, 2021), a catalogue of classroom techniques; Daniel Willingham, Why Don't Students Like School?; and Justin Reich, Failure to Disrupt (Harvard University Press, 2020), a sober history of why ed-tech promises fall short.
• 🌐 Free text. Barak Rosenshine, "Principles of Instruction" (American Educator, 2012), free from the American Federation of Teachers, and the Education Endowment Foundation's guidance reports at educationendowmentfoundation.org.uk.
• 🌐 Free course. MIT OpenCourseWare CMS.595 Learning, Media, and Technology (Spring 2024, taught by Justin Reich).