A PRIMER FOR EDTECH PRODUCT AND TECH PARTNERS WHO ARE NOT EDUCATORS

Designing for Learning

How the science of learning, teaching and human development should shape the design principles for a national learning system.

A working brief for the designers, developers, product owners and vendors who build learning products and are not trained in education. It explains what is known about how people learn, why that differs from how most digital products are designed, and how to turn that knowledge into design principles the team owns and can defend.

It does not hand you a finished set of principles. It gives you the evidence and a method.

WHO THIS IS FOR

Written for people who build learning products and are not educators

An evidence-based overview of what matters to educators, for the product managers, designers, engineers and vendors who design and build learning systems. It gives you the evidence and a method. The principles you write from them will be yours to defend.

Educators carry a body of knowledge about how people learn that rarely reaches the people who build the tools they use. This primer closes that gap. It is not a rulebook: it sets out what the research says, where product instinct pulls against it, and how to turn the findings into design principles a team can own.

Grounded in the learning-sciences and cognitive-science sources collected for this project: Clark and Mayer (2016), the Cambridge Handbook of the Learning Sciences (Sawyer, 2014), Deans for Impact (2015), Design Principles for Schools (Darling-Hammond et al., 2021), Immordino-Yang (2016) and Kirschner and Hendrick (2020). Where this deck goes beyond the library it cites the primary study.

Part One is meant to be read end to end. It is short by design. Part Two is the seven findings, each stated as a claim, followed by why it holds, what it implies for the system, and the questions it should make us ask. Part Three turns the science into a method and a lens. The appendix at the close is a reference layer to dip into when a decision needs backing.

HOW TO READ THIS DECK

Colours mean things here

One colour per intent, on every slide and in every panel. The teal ink is the explanation.

SAGE

What to do

• Design moves the evidence supports
• Patterns to protect

CORAL

What not to do

• Product instincts that mislead here
• Anti-patterns to rule out

LILAC

Evidence

• Studies, meta-analyses, effect sizes
• Cited APA-style, listed in full at the close

MUSTARD

Contested

• Where findings are debated or smaller than their reputation
• Read before you build on them

Effect sizes appear as Cohen’s d or Hedges’ g: the difference between two groups in standard deviations. As a rough guide, 0.2 is small, 0.5 is medium, 0.8 is large, and in education anything above 0.4 is worth a designer’s attention. Where a finding is contested the deck says so in mustard rather than dropping it or overstating it.

Part one of three

The reframe

Ease of use and ease of learning are different things, and they can pull in opposite directions.

• ONE The reframe
• TWO Seven things that are true about how people learn
• THREE From science to design principles

Most digital products are designed to be effortless. We remove friction, shorten paths, anticipate needs and reward every action so the user keeps moving and keeps coming back. Those instincts are correct for shopping, streaming and social feeds. They are actively misleading when the goal is learning.

§00

THE REFRAME

Part one of three

§00

Ease of use is not ease of learning

Most products are designed to be effortless. Learning is the one domain where difficulty is often the point.

Decades of research converge on an uncomfortable finding. The conditions that make performance feel fast, smooth and confident in the moment are often the conditions that produce the weakest long-term learning. Conditions that feel slower and more effortful, which Bjork and Bjork (2011) call desirable difficulties, tend to produce learning that lasts and transfers.

Soderstrom and Bjork (2015) reviewed the literature on learning versus performance and concluded that performance during practice is an unreliable index of what has been learned. The two routinely diverge.

THE INSTINCT

Most products are designed to be effortless

Remove friction. Shorten the path. Anticipate the need. Reward every action. Right for shopping, streaming and feeds. Misleading for learning.

Learning is the one domain where difficulty is often the point. Not every difficulty, and not difficulty for its own sake. The particular kind of effort that comes from retrieving, generating, spacing and struggling with the material is what builds memory that lasts.

The frictionless default is a design decision that has already been made for you by every other product the learner uses. It has to be unmade deliberately.

THE EVIDENCE

What feels best now often teaches least

Five minutes after studying, re-reading wins. A week later, the learners who were tested instead remember more.

FIG 01 — RE-READING WINS THE SESSION AND LOSES THE WEEK (ROEDIGER & KARPICKE, 2006)

Roediger and Karpicke (2006) had students study a prose passage and then either re-read it or take a free-recall test. Five minutes later the re-readers recalled more (about 81% against 75%). One week later the order reversed: about 56% for the tested group against 42% for the re-readers. The condition that felt more productive was the one that decayed fastest.

This is the pattern Bjork and Bjork (2011) call a desirable difficulty, and Soderstrom and Bjork (2015) generalise it: performance during acquisition is a poor guide to learning. A design team that measures only in-session performance is measuring the wrong curve.

THE FLUENCY TRAP

Learners choose what feels effective, and they are usually wrong

Two studies, one pattern: the method that worked was the one participants rated worse.

KORNELL & BJORK, 2008

Spaced beat massed. Learners voted for massed.

• Participants learned painting styles either massed (one artist at a time) or spaced (artists interleaved)
• Spacing produced better induction of the styles
• Asked which condition worked better, most chose massing, even after seeing their own results

DESLAURIERS ET AL., 2019

Active learning taught more. Students felt they learned less.

• Harvard physics students were taught the same material by active learning and by polished lecture
• The active-learning group scored higher on the test of learning
• They also reported a lower feeling of learning and rated the instructor lower

Carpenter, Wilford, Kornell and Mullaney (2013) found the same divergence from the other side: a fluent, confident lecturer raised students’ judgements of how much they had learned without raising what they had actually learned.

Satisfaction, perceived ease and self-reported learning are real signals about experience. They are not signals about learning, and a product team that treats them as one will optimise the wrong thing (Deslauriers et al., 2019; Kornell & Bjork, 2008).

THE THROUGH-LINE OF THIS PRIMER

Our job is not to make the system feel effortless. It is to make the right effort possible, visible and worthwhile, and to remove only the effort that is wasted.

A learner who feels fluent is not necessarily learning. A learner who is struggling productively often is.

Ease of use and ease of learning are not the same thing, and can pull in opposite directions. Hold this as you read the rest. Nearly every principle the team eventually writes should be testable against it.

TWO KINDS OF DIFFICULTY

This does not mean learning products should be hard to use

Confusing navigation, cluttered screens and unclear instructions waste mental effort that should go into learning. The discipline is to tell two kinds of difficulty apart.

PROTECT

Difficulty intrinsic to thinking hard about the material

• Recalling rather than recognising
• Working out a step before it is shown
• Meeting an idea in an unfamiliar case
• Getting it wrong and finding out why

ELIMINATE

Difficulty incidental to a bad interface

• Hunting for the next action
• Reading a label and a legend in two places
• Decorative motion and sound competing for attention
• Holding instructions in mind while doing the task

FIG 02 — THREE LOADS: MANAGE THE FIRST, CUT THE SECOND, PROTECT THE THIRD

Cognitive load theory gives this distinction a name and a method. Intrinsic load comes from the material and is managed by sequencing and chunking. Extraneous load comes from the presentation and is waste. Germane load is the effort that builds understanding, and it is what the interface must leave room for (Sweller, 1988; Sweller, van Merriënboer & Paas, 2019).

The theory’s own authors acknowledge that measuring the three loads separately remains unsolved (Sweller et al., 2019). Treat the categories as a design heuristic, which they are good at, rather than as a measurement instrument, which they are not yet.

THE TEST

Does this help the learner do the thinking that causes learning, or does it just make the experience feel good?

One question, for every principle you write and every screen you review.

Nearly every design principle the team eventually writes should be testable against this single question. If a principle cannot be checked against it, the principle is probably about the product rather than about the learner.

Part two of three

Seven things that are true about how people learn

The load-bearing findings from cognitive science, the learning sciences, and developmental and affective research.

• ONE The reframe
• TWO Seven things that are true about how people learn
• THREE From science to design principles

Each finding is stated as a claim, followed by why it holds, what it means for the system we are designing, and the questions it should make us ask. Treat the design implications as raw material for principles, not as principles themselves.

§01

WORKING MEMORY

Part two of three

§01

Learning is a lasting change in long-term memory, and working memory is the bottleneck

Everything bound for long-term memory must first pass through a narrow gate. Overload the gate and little gets through.

We think with working memory, the small temporary space where conscious processing happens. It holds only a few items and empties quickly (Baddeley, 1992). We learn by moving information into long-term memory, which is effectively unlimited and durable.

Cowan (2001) put the pure capacity limit at about four chunks, not the seven of Miller (1956), which Miller himself treated as partly rhetorical. Sweller (1988) showed that means-ends problem solving imposes a load heavy enough to block the schema building that learning depends on.

WHY IT HOLDS

Everything bound for long-term memory passes through a narrow gate

Working memory holds about four chunks and empties in seconds. Present too much at once, split attention, or add noise, and little gets through.

FIG 03 — THE GATE HOLDS THREE TO FOUR CHUNKS, NOT SEVEN (COWAN, 2001)

FIG 04 — HELP FOR A NOVICE IS A HINDRANCE FOR AN EXPERT (KALYUGA ET AL., 2003)

Baddeley (1992) described working memory as a small system of a central executive and two short-lived stores. Cowan (2001) reviewed the capacity evidence and put the limit at about four chunks when rehearsal and chunking are prevented. Sweller (1988) showed that heavy problem-solving load blocks the schema acquisition that learning consists of.

The expertise reversal effect adds a twist a design team needs: the scaffolding that helps a novice, such as worked examples and step-by-step guidance, becomes a hindrance once a learner has the schema, because it now competes with knowledge they already hold (Kalyuga, Ayres, Chandler & Sweller, 2003). Support that never fades ends up in the way.

WHAT IT MEANS FOR US

Treat “clean” as a learning requirement, not an aesthetic preference

DO

Ration attention

• Chunk and sequence. Reveal complexity progressively; a screen that shows everything shows nothing well
• Protect attention. Every element competing for the eye spends capacity the learner needs for the task
• Use worked examples and scaffolds for novices, then fade them as competence grows
• Make support removable, never permanent

DON’T

Spend it for them

• Dashboards that show every metric at once
• Options added “for power users” in the middle of a task
• Decoration, ambient motion and sound that carry no meaning
• One level of scaffolding for everyone, from first lesson to last

Visual restraint directly increases how much can be learned. This is the one place where a design system’s ordinary discipline, hierarchy, whitespace, one primary action, is not only good craft but a learning intervention in its own right (Sweller et al., 2019).

Design scaffolds with an exit. A hint, a worked step or a template should have a defined condition under which it stops appearing, or the system keeps treating experts like novices (Kalyuga et al., 2003).

QUESTIONS TO ASK OURSELVES

Where are we asking the learner to hold too much in mind?

• 01 Where in our flows are we asking the learner to hold too much in mind at once?
• 02 Which on-screen elements are essential to the task, and which are we adding out of habit?
• 03 Does support fade as the learner improves, or does the system keep treating experts like novices?
• 04 If we removed half the elements on this screen, what would the learner lose?

Grounding: Sweller (1988), cognitive load theory; Baddeley (1992), working memory; Cowan (2001), capacity; Kalyuga et al. (2003), expertise reversal; Deans for Impact (2015).

§02

PRIOR KNOWLEDGE

Part two of three

§02

People understand new things only through what they already know

New knowledge sticks to old knowledge. Learners are never blank slates, and some of what they carry is wrong.

We make sense of anything by connecting it to prior knowledge already in long-term memory, organised into structures called schemas. This is why the same explanation lands for one learner and bounces off another: they arrive with different scaffolding (Bransford, Brown & Cocking, 2000).

Chi, Feltovich and Glaser (1981) showed that physics experts sort problems by the principle underneath while novices sort by surface features. What you already know decides what you can see.

WHY IT HOLDS

Experts see structure. Novices see surface.

The same screen is a different object to a learner with the schema and a learner without it.

CHI, FELTOVICH & GLASER, 1981

Experts sort physics problems by the principle underneath

• Novices sorted the same problems by what they looked like: inclined planes with inclined planes, pulleys with pulleys
• Experts grouped by conservation of energy, Newton’s second law
• What you already know decides what you can see

MISCONCEPTIONS

Wrong prior knowledge does not sit quietly

• Learners arrive with prior conceptions, some of them wrong, and those actively distort new information unless they are surfaced and addressed (Bransford et al., 2000)
• Presenting the correct version alongside the wrong one is not enough; the wrong one has to be engaged (Willingham, 2023)

Kirschner, Sweller and Clark (2006) argued from the same evidence that novices learn more from explicit, guided instruction than from minimal-guidance discovery, because they lack the schemas that make discovery productive.

That paper drew rejoinders in the same journal. Hmelo-Silver, Duncan and Chinn (2007) argued that well-scaffolded problem-based and inquiry learning is not minimal guidance and shows good results. The consensus that survives is narrower than either side’s headline: novices need more structure than experts, and how much depends on what they already know.

WHAT IT MEANS FOR US

A single fixed path assumes a single starting point, which no national population has

DO

Find out what they know before you teach

• Activate prior knowledge before introducing something new; a brief connection to what the learner already knows does real cognitive work
• Adapt to where the learner is, with entry points rather than one path
• Design to surface misconceptions, not just deliver correct content
• Treat prerequisites as anchor points, not bureaucracy

DON’T

Assume a common start

• One onboarding, one sequence, one pace for every learner in the country
• Flows that only let learners receive right answers, never reveal and revise wrong ones
• Pre-tests that gather data and change nothing downstream
• Skipping the “what do you already think?” step to save a screen

Sequence matters because new learning attaches to what is already there (Bransford, Brown & Cocking, 2000). A system that knows what a learner believes can put the right example next to the wrong belief. A system that does not is teaching into the dark.

QUESTIONS TO ASK OURSELVES

How does the system find out what a learner already knows?

• 01 How does the system find out what a learner already knows before it teaches?
• 02 Where do we assume a common starting point that will not hold across a whole country?
• 03 Do our flows give learners a chance to reveal and revise wrong ideas, or only to receive right ones?
• 04 Which of our “prerequisites” are real anchor points, and which are just ordering?

Grounding: Bransford, Brown and Cocking (2000), How People Learn; Chi, Feltovich and Glaser (1981); Willingham (2023); Kirschner, Sweller and Clark (2006).

§03

EFFORTFUL LEARNING

Part two of three

§03

Durable learning is effortful, and effort often feels like failure

Retrieval, spacing and feedback are three of the most robust findings in the field. All three feel worse in the moment than passive review.

Trying to recall something strengthens memory far more than re-reading it. The same study time spread across days beats massed practice. Timely, specific feedback about the gap between attempt and target is one of the highest-leverage inputs to learning. The problem is that learners, left to their preferences, reliably choose the strategies that work least well.

Dunlosky et al. (2013) reviewed ten study techniques and rated practice testing and distributed practice as high utility, and highlighting, re-reading and summarising as low utility.

WHY IT HOLDS · RETRIEVAL

Retrieval beats re-reading, in the lab and in classrooms

Three independent meta-analyses land on the same number: about half a standard deviation over re-studying the same material.

FIG 05 — THREE META-ANALYSES, ONE ANSWER: ABOUT g = 0.5 OVER RE-STUDY

Rowland (2014) pooled 159 lab effects and found g = 0.50 for testing over restudy, larger when the test demanded recall rather than recognition. Adesope, Trevisan and Sundararajan (2017) found g = 0.51 against restudy and 0.93 against doing nothing. Yang, Luo, Vadillo, Yu and Shanks (2021) looked only at real classrooms: 222 studies, 48,478 students, g = 0.50.

Karpicke and Blunt (2011) set retrieval practice against concept mapping, an elaborative technique students rate highly. Retrieval won on both verbatim and inference questions a week later, and the students had predicted the reverse.

WHY IT HOLDS · SPACING

The same hours, spread out, last longer

Massed practice feels productive and decays fast. Spaced practice feels slower and holds. The schedule is a design variable.

FIG 06 — SCHEMATIC. SAME STUDY TIME, DIFFERENT SCHEDULE, DIFFERENT RESULT

Cepeda, Pashler, Vul, Wixted and Rohrer (2006) synthesised 839 assessments of distributed practice and found spaced study reliably beat massed study, with the best gap growing as the retention interval grew. Cepeda et al. (2008) mapped that ridge: to remember for a week, the optimal gap between sessions was roughly 20 to 40% of the week; to remember for a year, roughly 5 to 10% of the year.

The 2006 synthesis covers verbal recall tasks. Carpenter, Cepeda, Rohrer, Kang and Pashler (2012) and Kang (2016) review the classroom evidence and find the effect holds beyond word lists, though with less precision about the ideal gap. Interleaving related problem types is the practical cousin: Rohrer and Taylor (2007) found 63% against 20% on a delayed maths test, and Rohrer, Dedrick and Stershic (2015) 72% against 38% with seventh graders.

WHY IT HOLDS · FEEDBACK

Feedback helps on average, and hurts more often than you would think

The average effect is solid. The distribution is the design brief.

0.48

Average effect of feedback on learning, 435 studies (Wisniewski, Zierer & Hattie, 2020)

38%

Share of feedback interventions that made performance worse (Kluger & DeNisi, 1996)

Kluger and DeNisi (1996) reviewed 131 studies and found a mean effect of d = 0.41, with more than a third of interventions lowering performance. Feedback that drew attention to the self rather than the task was the main culprit. Wisniewski, Zierer and Hattie (2020) updated the picture with 435 studies: d = 0.48 overall, higher for feedback that carries information about how to improve than for praise or a bare score.

Hattie and Timperley (2007) frame useful feedback as answering three questions: where am I going, how am I going, and where to next. An error state that answers none of them is a status message, not feedback.

WHAT IT MEANS FOR US

Build in retrieval, not just presentation

Systems that only show and explain skip the step that actually builds memory.

DO

Ask before you tell

• Design for return visits and spacing, not one long session; the schedule of practice is a design variable
• Make feedback fast, specific and usable, about the work rather than the person
• Prompt recall before revealing an answer
• Measure what is remembered later, not what is completed now

DON’T

Optimise the feeling of progress

• Review modes that re-show content and call it practice
• Streaks and time-on-task as the success metric
• Answer reveals and autocompletes that remove the retrieval
• Praise and scores with nothing about what to do next

Resist the fluency trap. A satisfying, frictionless review session can leave nothing behind. Do not optimise for the feeling of progress over the fact of it (Soderstrom & Bjork, 2015; Dunlosky et al., 2013).

QUESTIONS TO ASK OURSELVES

Does our design ask learners to retrieve, or mostly to recognise?

• 01 Does our design ask learners to generate and retrieve, or mostly to recognise and re-read?
• 02 Are we optimising for time-on-task and streaks, or for durable learning that may need fewer sessions?
• 03 When a learner gets something wrong, what exactly happens next, and is it useful?
• 04 What does the system do to bring a learner back at the right interval, rather than as often as possible?

Grounding: Roediger and Karpicke (2006); Rowland (2014); Adesope et al. (2017); Yang et al. (2021); Cepeda et al. (2006, 2008); Kluger and DeNisi (1996); Wisniewski et al. (2020); Bjork and Bjork (2011).

§04

MULTIMEDIA FORM

Part two of three

§04

How we combine words, visuals and sound changes how much is learned

The form of delivery is not neutral. This is the part of the science most directly in a design team’s hands.

Richard Mayer’s multimedia research, three decades of controlled studies, shows that specific and repeatable design choices raise or lower learning because they route information through the two channels we process with, visual and verbal (Clark & Mayer, 2016).

The effects are among the largest in educational research. Ginns (2006) found spatial contiguity at about d = 1.12 and temporal contiguity at about 0.87 across the studies he pooled. The full principle set is in Appendix B.

WHY IT HOLDS

Multimedia effects, sized

Putting words next to the picture they describe is one of the largest effects in the literature. Decoration is a reliable negative. One famous effect shrank when someone checked.

FIG 07 — CONTIGUITY IS LARGE, DECORATION HURTS, AND THE MODALITY EFFECT IS SMALLER THAN ITS REPUTATION

Ginns (2006) pooled the contiguity studies: spatial contiguity, words placed beside the graphic they describe, about d = 1.12; temporal contiguity, narration synchronised with the visual, about d = 0.87. Sundararajan and Adesope (2020) pooled the seductive-details studies and found interesting-but-irrelevant material lowered learning by g = −0.33; Rey (2012) found the harm was larger on transfer than on retention.

The modality effect, learning better from graphics with narration than from graphics with on-screen text, was d = 0.72 in Ginns (2005). Reinwein (2012) re-ran the analysis with 86 effects and found d = 0.38, and about 0.20 after correcting for publication bias. It exists, and it is much smaller than its reputation. Do not build an audio-first policy on it.

WHAT IT MEANS FOR US

Are our graphics doing cognitive work, or decorating?

DO

Route information cleanly

• Words plus relevant graphics beat words alone, but only when the graphic carries meaning
• Place words next to the visual they describe; sync narration with what is on screen
• Break content into learner-paced segments with a clear “next” control
• Introduce key terms before the process that uses them

DON’T

Split or flood the channels

• Narrate and show the same text at once; identical words in ear and eye compete rather than reinforce
• Seductive details, background music and ornamental flourishes
• Unstoppable auto-play, carousels and streams the learner cannot pause
• Legends far from the thing they label

This is the part of the science most directly in a design team’s hands. Every one of these is a decision made in a component, a layout or a content template (Clark & Mayer, 2016). Appendix B states each principle with its practical instruction.

QUESTIONS TO ASK OURSELVES

Does the learner control the pace, or does the medium?

• 01 Are our graphics doing cognitive work, or decorating?
• 02 Where might our layouts be splitting attention between text and image?
• 03 Does the learner control the pace, or does the medium?
• 04 Which “engaging” elements would a seductive-details study count as noise?

Grounding: Clark and Mayer (2016), e-Learning and the Science of Instruction; Ginns (2005, 2006); Rey (2012); Sundararajan and Adesope (2020); Reinwein (2012).

§05

EMOTION AND MOTIVATION

Part two of three

§05

Emotion, motivation and belonging are the engine, not the extras

We only deeply process what we care about. The brain systems for emotion and cognition are inseparable.

Neuroscience has retired the idea that emotion interferes with thinking. Immordino-Yang and Damasio (2007) put it as “we feel, therefore we learn”. Motivation research adds structure: people sustain effort when they have a sense of choice, a sense of progress, and a sense of connection and safety (Ryan & Deci, 2000).

Some of the most popular claims in this area are weaker than their reputation. Growth-mindset interventions help, but the effects are small and conditional (Yeager et al., 2019; Macnamara & Burgoyne, 2023). The panels in this section say where the evidence is firm and where it is contested.

WHY IT HOLDS · REWARDS

Rewards can crowd out the reason to learn

Across 128 experiments, tangible rewards for doing, finishing or performing lowered intrinsic motivation. Informational feedback raised it.

FIG 08 — PAY FOR THE BEHAVIOUR AND YOU CAN LOSE THE REASON FOR IT (DECI, KOESTNER & RYAN, 1999)

Deci, Koestner and Ryan (1999) meta-analysed 128 experiments. Rewards contingent on taking part (d = −0.40), on finishing (−0.36) and on performing well (−0.28) all reduced free-choice intrinsic motivation. Verbal feedback with no tangible reward increased it (+0.33). The mechanism is control: a reward that feels like a lever reduces the sense of autonomy that sustains effort.

The picture is contested in two ways. Cerasoli, Nicklin and Ford (2014), across 40 years of studies, found intrinsic motivation and incentives jointly predict performance and only incentives tied directly to performance crowd intrinsic motivation out. And gamification meta-analyses find small positive effects on cognitive outcomes (g = 0.49) and motivation (0.36) (Sailer & Homner, 2020), though the motivational effects are unstable in the most rigorous studies, and Hanus and Fox (2015) found a leaderboard-and-badges class ended a semester less motivated and lower-scoring than the control. The design reading is not “never reward”. It is: use rewards to support the behaviour, never to replace the reason for it.

WHY IT HOLDS · MINDSET AND BELONGING

Mindset: real, small and conditional

The most-cited motivation idea of the last two decades survives scrutiny, but at a fraction of its reputation.

+0.10

GPA gain for lower-achieving students, national experiment (Yeager et al., 2019)

0.05

Average effect of growth-mindset interventions on achievement, 63 studies (Macnamara & Burgoyne, 2023)

Yeager et al. (2019) ran a pre-registered national experiment across 65 US secondary schools. A short online growth-mindset intervention raised lower-achieving students’ grades by about 0.10 GPA points, and only in schools whose peer norms supported challenge-seeking. Dweck (2006) is the trade account of the underlying idea.

Sisk, Burgoyne, Sun, Butler and Macnamara (2018) found mindset correlated with achievement at only r ≈ .10 and interventions averaged d ≈ 0.08. Macnamara and Burgoyne (2023) found d = 0.05 overall, not significant after bias correction, and 0.02 in the highest-quality studies. Yeager and Dweck replied in the same issue. The honest design reading: framing ability as improvable costs nothing and may help the learners who need it most, in the right environment. It is not a lever that moves outcomes on its own.

Belonging shows the same shape. Walton and Cohen (2011) found a brief belonging intervention improved outcomes for minority students. Walton et al. (2023), across 26,911 students at 22 institutions, found it raised first-year completion mainly for historically lower-progressing groups, and only where those students had real opportunities to belong. Context does the work the intervention unlocks.

WHY IT HOLDS · THREE NEEDS

People sustain effort when three needs are met

Autonomy, a sense of choice. Competence, a sense of progress. Relatedness, a sense of connection and safety. Learners who see ability as improvable persist where those who see it as fixed give up.

FIG 09 — AUTONOMY, COMPETENCE AND RELATEDNESS (RYAN & DECI, 2000)

Self-determination theory (Ryan & Deci, 2000) identifies autonomy, competence and relatedness as basic psychological needs. When they are supported, motivation is more intrinsic and more durable; when they are thwarted, motivation becomes controlled and brittle. Immordino-Yang (2016) grounds the same point in affective neuroscience: we only deeply process what we care about.

Psychological safety is a design requirement, not a tone-of-voice preference. Learners take the risks that learning requires only when failure is low-stakes and not shaming (Darling-Hammond et al., 2021).

WHAT IT MEANS FOR US

Frame mistakes as information

Error states are teaching moments, and their tone shapes whether a learner tries again.

DO

Build competence, honestly

• Design for psychological safety: failure is low-stakes and never shaming
• Support autonomy with meaningful choice, and competence with visible, honest progress
• Make feedback about the work, informational rather than evaluative
• Use extrinsic mechanics to support a behaviour, never to replace the reason for it

DON’T

Manufacture engagement

• Inflated praise and confetti for trivial actions
• Leaderboards that turn learning into social comparison for the many who will not win
• Streak anxiety as a retention strategy
• Points that pay for the click rather than the thinking

Points and streaks can crowd out the intrinsic motivation that sustains real learning (Deci et al., 1999; Hanus & Fox, 2015). They can also, in some designs, help (Sailer & Homner, 2020). The difference is whether the mechanic points the learner at the work or at the mechanic.

QUESTIONS TO ASK OURSELVES

Does our error design make a struggling learner want to continue, or want to hide?

• 01 Does our error and feedback design make a struggling learner want to continue or want to hide?
• 02 Are our motivational mechanics building genuine competence, or just engagement metrics?
• 03 Where can we give real choice without fragmenting the learning path?
• 04 If we removed every point, badge and streak, what would still bring the learner back?

Grounding: Immordino-Yang (2016); Immordino-Yang and Damasio (2007); Ryan and Deci (2000); Deci, Koestner and Ryan (1999); Cerasoli et al. (2014); Yeager et al. (2019); Macnamara and Burgoyne (2023); Walton et al. (2023).

§06

VARIABILITY

Part two of three

§06

Learners differ, and development is uneven, so design for variability rather than the average

A national system spans ages, languages, prior attainment, home support, devices and connectivity. There is no average learner.

Development is not a tidy staircase of age-linked stages. Mastery comes in fits and starts, and readiness is better judged by prerequisites met than by age (Deans for Impact, 2015). Relationships and context shape learning as much as any curriculum (Osher et al., 2020).

Universal Design for Learning responds by building flexibility in from the start, with multiple means of engagement, representation and expression, rather than designing for the middle and retrofitting for everyone else (CAST, 2024; Darling-Hammond et al., 2021).

WHY IT HOLDS

There is no average learner to design for

Ages, languages, prior attainment, home support, devices and connectivity. Readiness is better judged by prerequisites met than by age.

Development is not a tidy staircase of stages. Mastery comes in fits and starts, and Deans for Impact (2015) list fixed developmental stages among the misconceptions that mislead design. Osher, Cantor, Berg, Steyer and Rose (2020) synthesise the developmental evidence: relationships and context shape learning as strongly as any individual trait, and variability is the norm, not noise around a mean.

Universal Design for Learning is the framework most often reached for here (CAST, 2024). Capp (2017) reviewed 18 studies from 2013 to 2016 and found UDL improves the learning process for all students, but that the evidence for improved educational outcomes is still thin and mostly from pre-post designs. Use UDL as a design discipline for flexibility. Do not cite it as proof of learning gains.

WHAT IT MEANS FOR US

Design for the edges

Accessibility and flexibility built in from the start serve everyone, not only the learners they were meant for.

DO

Build flexibility in from the start

• Offer multiple means of representation and expression: more than one way to meet an idea, more than one way to show understanding
• Support different entry points, paces and levels of support without fragmenting into chaos
• Treat low bandwidth, shared or low-end devices and varied language backgrounds as the operating conditions
• Judge readiness by prerequisites mastered, not by age or level

DON’T

Design for the middle and retrofit

• A “typical learner” persona that no real learner matches
• Accessibility as a compliance pass after the design is done
• One modality, one format, one way to answer
• Assuming the device, the connection and the home the design team has

Equity is a design constraint, not an afterthought. Design Principles for Schools (Darling-Hammond et al., 2021) synthesises the science of learning and development into five system-level principles, listed as Appendix D. Written for schools, they translate directly to a national learning environment, and they are a reminder that cognition, emotion and relationship are one system.

QUESTIONS TO ASK OURSELVES

Who is our design quietly excluding, and would we notice?

• 01 Who is our design quietly excluding, and would we notice?
• 02 Does the system offer more than one way in and more than one way to demonstrate learning?
• 03 What are our real-world constraints on device, connectivity and language, and are we designing for them or around them?
• 04 Where does the design assume a home, a device or a level of support that many learners do not have?

Grounding: Deans for Impact (2015); CAST (2024), Universal Design for Learning Guidelines 3.0; Darling-Hammond et al. (2021); Osher et al. (2020); Capp (2017).

§07

TRANSFER

Part two of three

§07

Knowledge does not automatically travel, so transfer has to be designed for

The goal of most learning is to use it somewhere new. Transfer is also the thing learners are worst at.

Knowledge tends to stay welded to the context it was learned in. A skill practised inside one narrow format often fails to appear when the situation looks different (Sawyer, 2014; Deans for Impact, 2015). Barnett and Ceci (2002) map how far transfer has to travel along content and context, and the further it travels the rarer it gets.

Gick and Holyoak (1983) found that people who compared two analogous cases induced the underlying schema and transferred it; people who saw one case mostly did not. Variation, with the structure made explicit, is what carries an idea out of its first example.

WHY IT HOLDS

Vary the surface, hold the structure

Practising the same idea across different-looking problems feels harder and scores worse in the session. On the delayed test it wins by a wide margin.

FIG 10 — INTERLEAVED PRACTICE ON A DELAYED TEST (ROHRER & TAYLOR, 2007; ROHRER ET AL., 2015)

Rohrer and Taylor (2007) had students practise four kinds of geometry problem either blocked by type or shuffled. Blocked practice scored higher during the session; a week later the shuffled group scored 63% against 20%. Rohrer, Dedrick and Stershic (2015) replicated the effect with seventh graders over three months: 72% against 38% on a test 30 days later.

Koedinger, Kim, Jia, McLaughlin and Bier (2015) analysed a large open online course and found that each interactive activity completed predicted about six times as much learning as each video watched or page read. Correlational, and one course, but the direction is the same one the lab finds: learning is not a spectator sport.

WHAT IT MEANS FOR US

Are we teaching a principle, or just one instance of it?

DO

Design for the situation it will be used in

• Vary the surface, hold the structure: the same principle across different-looking cases
• Use authentic, meaningful tasks over decontextualised drills where possible
• Make underlying structure explicit; help learners see the principle beneath the example
• Test whether learning holds up outside the exact interface it was built in

DON’T

Mistake performance in one flow for learning

• One polished practice format, repeated
• Drill that never changes the framing of the problem
• Templates that do the abstracting for the learner
• Success metrics measured only inside the product

Transfer improves when learners meet ideas in varied contexts, practise abstracting the underlying principle, and work on tasks that resemble where the knowledge will actually be used (Barnett & Ceci, 2002; Gick & Holyoak, 1983; Sawyer, 2014). Do not mistake performance inside one polished flow for learning that will survive outside it.

QUESTIONS TO ASK OURSELVES

How will we know that learning holds up outside this interface?

• 01 Are we teaching a principle, or just one instance of it?
• 02 Where does our design let learners practise the same idea in genuinely different situations?
• 03 How will we know that learning holds up outside the exact interface it was built in?
• 04 What would a teacher see in class that would tell us the platform worked?

Grounding: Sawyer (2014), Cambridge Handbook of the Learning Sciences; Deans for Impact (2015); Barnett and Ceci (2002); Gick and Holyoak (1983); Rohrer and Taylor (2007); Rohrer et al. (2015); Koedinger et al. (2015).

Part three of three

From science to design principles

Where product instinct collides with learning reality, a method for writing principles you can defend, and a lens to review any screen.

• ONE The reframe
• TWO Seven things that are true about how people learn
• THREE From science to design principles

The seven truths add up to a set of tensions between good product instinct and good learning design. These are the conflicts your principles will need to resolve. A strong design principle usually takes a deliberate position on one of them.

§08

INSTINCT VS REALITY

Part three of three

§08

Where design instinct and learning reality collide

The seven truths add up to a set of tensions between good product instinct and good learning design. Naming them is half the work.

These are the conflicts your principles will need to resolve. A strong design principle usually takes a deliberate position on one of these tensions rather than pretending both sides win.

Each of the next three slides sets a product instinct against a learning reality and states the design question it forces. The instinct is not wrong in general. It is wrong here.

TENSIONS 1 TO 3

Friction, engagement and help

PRODUCT INSTINCT

What we would normally do

• Remove all friction so the user flows through
• Maximise time-in-app, sessions and streaks
• Help instantly whenever the user hesitates

LEARNING REALITY

What the evidence says

• Some friction is where learning happens; effortful retrieval and productive struggle build memory
• Efficient learning may mean fewer, better-spaced sessions and less time, not more
• Immediate help can pre-empt the struggle and retrieval that cause learning

The design questions these force. Which friction is wasted, and which is the actual work? Remove the first, protect the second. Are we measuring engagement or learning, and what is our real success metric? When should help be delayed, faded or earned rather than given at the first sign of difficulty?

Grounding: Bjork and Bjork (2011); Soderstrom and Bjork (2015); Cepeda et al. (2006); Kalyuga et al. (2003).

TENSIONS 4 TO 7

Delight, reward, personalisation and the typical user

PRODUCT INSTINCT

What we would normally do

• Delight with rich visuals, animation and sound
• Reward every action to keep motivation high
• Personalise to make things feel easy
• Design for the typical user

LEARNING REALITY

What the evidence says

• Decorative media splits attention and lowers learning (coherence, redundancy)
• Extrinsic rewards can crowd out the intrinsic motivation that sustains deep learning
• Real personalisation means adapting to prior knowledge and appropriate challenge, not just reducing effort
• There is no typical learner; variability is the norm across a national population

The design questions these force. Does each visual and sound carry meaning, or is it there to delight at the cost of learning? Are our mechanics building competence and interest, or substituting for them? Does our adaptivity target the right difficulty, or just the most comfortable path? Are we designing for an average that excludes the edges, or for the full range from the start?

Grounding: Sundararajan and Adesope (2020); Deci, Koestner and Ryan (1999); Hanus and Fox (2015); Bransford et al. (2000); Kalyuga et al. (2003); Osher et al. (2020); CAST (2024). The contested side of rewards: Sailer and Homner (2020); Cerasoli et al. (2014).

THE POINT OF NAMING THEM

A strong principle takes a position on one of these

It does not pretend both sides win. If a principle cannot tell you which of two designs is better, it is decoration.

Naming the tensions is half the work. The other half is deciding, for this system, where each one is settled, writing that down as a principle, and being able to point at a screen and say whether it honours or breaks it.

§09

A METHOD

Part three of three

§09

From science to principles: a method

This primer stops short of writing your principles. Principles the team derives, argues over and owns will be used. Lists handed down will not.

What follows is a repeatable way to get from a finding to a principle you can defend, plus five tests a principle has to pass before it is adopted, and five traps that catch teams designing for learning for the first time.

THE TRANSLATION PATTERN

Four steps from a finding to a principle. Do not skip from the science to a slogan.

• 01 Finding. State the evidence-based claim in one sentence. “Retrieval strengthens memory more than re-reading.”
• 02 Implication. Name what it means for a system like ours. “Learners need to generate answers, not just review content.”
• 03 Principle. Write the design commitment it demands, in the team’s voice. “The system asks before it tells.”
• 04 Pattern and anti-pattern. Make it concrete: what this looks like done well, and the tempting mistake it rules out.

The discipline is the order. Teams that start at step three write slogans that sound like principles and decide nothing. Teams that start at step one and stop there produce a literature review nobody designs from. All four, in sequence, every time.

A WORKED EXAMPLE

One idea in focus at a time

FINDING → IMPLICATION → PRINCIPLE

The commitment

• Finding. Working memory is limited and easily overloaded; learning fails when too much is presented at once (Cowan, 2001; Sweller, 1988).
• Implication. Our interfaces must ration the learner’s attention and reveal complexity in stages.
• Principle. “One idea in focus at a time.” The system never makes the learner hold more than the task requires.

PATTERN → ANTI-PATTERN

What it looks like

• Pattern. Progressive disclosure; worked examples that fade; a single clear action per step.
• Anti-pattern. Dashboards that show everything at once; screens that add options “for power users” mid-task.
• Test. Point at any screen and say whether it honours this or breaks it.

Notice what the worked example does not do. It does not say “keep it simple”, which decides nothing. It names a mechanism, a commitment, a pattern and a tempting mistake, and every one of them can be checked against a real screen.

TESTS OF A GOOD LEARNING-DESIGN PRINCIPLE

A principle that fails these is a slogan, not a tool

• 01 Evidence-anchored. It traces back to a real finding about how people learn, not just to taste or trend.
• 02 Decidable. It can settle a real disagreement. If it cannot tell you which of two designs is better, it is decoration.
• 03 Takes a position. It resolves one of the tensions in §08 rather than pretending both sides win.
• 04 Learner-outcome oriented. It is justified by what the learner ends up able to do, not by how the product feels or performs commercially.
• 05 Falsifiable in the interface. You can point at a screen and say whether it honours the principle or breaks it.

Run every candidate principle against all five before adopting it. Most first drafts fail the second and fifth: they sound right and decide nothing.

TRAPS SPECIFIC TO DESIGNING FOR LEARNING

Patterns that are neutral or positive elsewhere become anti-patterns here

THE TRAP

What it looks like

• The fluency trap. A smooth session taken as proof of learning
• Engagement as a proxy. Streaks and time-in-app because they are easy to measure
• Helpfulness that removes the work. Hints and reveals that delete the effort
• Decoration mistaken for motivation. Appeal up, learning down
• Designing for the average. A typical learner who does not exist

THE EVIDENCE BEHIND IT

Why it is a trap

• Feeling and fact of learning diverge (Soderstrom & Bjork, 2015)
• Engagement metrics do not measure durable capability (Kornell & Bjork, 2008)
• Retrieval is the step that builds memory (Roediger & Karpicke, 2006)
• Seductive details and tangible rewards both cost learning (Sundararajan & Adesope, 2020; Deci et al., 1999)
• Variability is the norm; retrofitting serves the edges worst (Osher et al., 2020)

Watch for these as you design and as you write principles. Each one is a product instinct that is correct in most contexts. That is what makes them hard to see.

§10

A WORKING LENS

Part three of three

§10

A working design lens, and the myths to design against

The seven truths, turned into a standing review for any screen, flow or component. Keep it next to the workfile.

The lens is the most portable thing in this primer. The myths are the beliefs most likely to walk into a design review uninvited: widely held, intuitively appealing, and unsupported by evidence.

A WORKING DESIGN LENS

Seven questions to ask of any screen, flow or component

• LOAD Is the learner being asked to hold more than the task requires? What can we remove or reveal in stages?
• PRIOR How does this meet learners where they are, and surface what they already (wrongly) believe?
• EFFORT Does this ask the learner to retrieve and generate, with spacing and useful feedback, or only to receive?
• FORM Do words, visuals and pace follow the multimedia principles, or split attention and decorate?
• MOTIVE Is this psychologically safe, autonomy-supporting and competence-building, or shaming and extrinsically driven?
• RANGE Does this work across the full range of learners, devices and contexts, from the start?
• TRANSFER Will what is learned here hold up outside this exact interface?

These are the seven truths, turned into a checklist. Use them as a standing review. This is the most portable takeaway from the primer; keep it next to the workfile.

Deans for Impact (2015) offer a parallel diagnostic: six questions every educator, and every designer of a learning system, should be able to answer. They are Appendix C.

MYTHS TO DESIGN AGAINST

Widely believed, intuitively appealing, and not supported by evidence

Each has misled real product decisions. Name them so they do not quietly shape the design. Nine in ten educators still believe the first one.

FIG 11 — THE PEOPLE WHO TEACH BELIEVE THEM TOO (NEWTON & SALVI, 2020; DEKKER ET AL., 2012)

Pashler, McDaniel, Rohrer and Bjork (2008) found no adequate evidence that matching instruction to a learner’s preferred style improves learning, and Kirschner (2017) asked the field to stop propagating it. Newton and Salvi (2020) pooled 37 surveys of 15,405 educators in 18 countries and found 89% still believe in matching. Dekker, Lee, Howard-Jones and Jolles (2012) found UK and Dutch teachers endorsed about half of 15 neuromyths: learning styles at 93% and 96%, left-brain and right-brain learners at 91% and 86%, and the ten-percent myth at 48% and 46%.

The prevalence figures come from convenience samples with varied survey wording, as Newton and Salvi note. The direction is not in doubt. The six myths and what the evidence says about each are Appendix E.

IF YOU KEEP ONE SENTENCE FROM THIS PRIMER

Design the system so the learner does the thinking that causes learning, remove the effort that is merely wasted, and never mistake a smooth experience for a learning one.

Part One is meant to be read; the appendix is meant to be used. The next step is the team’s: run the science through the method in §09 and draft the principles you can defend.

§11

WHERE TO GO NEXT

SUPPORTING MATERIALS · IN THE READING VERSION

Six appendices sit behind this deck

• A Glossary. Thirteen terms, in plain language for designers.
• B Multimedia design principles. Mayer’s ten, each with its practical instruction.
• C Six questions about how learning works. Deans for Impact’s diagnostic, with the design consequence of each.
• D Whole-child design principles. The five SoLD principles, translated to a national system.
• E Myths to design against. Six, with what people believe and what the evidence says.
• F Sources and further reading. Every work cited, APA style, linked where a link exists.

The appendix is a reference layer to dip into when a decision needs backing. It follows the close of the article. Items held in the project library are marked in the source list.

The evidence is in.

The principles are not written yet.

That part is yours.

THANK YOU

Now write the principles you can defend

Run the science through the method in §09. Test each principle against the five checks. Keep the lens next to the workfile.

• PREPARED BY geraldajam.com

§11 · WHERE TO GO NEXT

Reference layer

Appendix

Detail to dip into. You do not need to read this straight through; use it when a decision needs backing. Open the ones you need.

Appendix A

Glossary

Thirteen terms, in plain language for designers.

A1Working memory The small, short-lived mental workspace where conscious thinking happens. Holds only a few items. The main bottleneck in learning.

A2Long-term memory Durable, effectively unlimited store of what we know. Learning means getting information to stick here.

A3Cognitive load How much working memory a task demands. Split into intrinsic (the material itself), extraneous (poor design, waste) and germane (effort that builds understanding).

A4Schema An organised mental structure of related knowledge. New learning attaches to existing schemas.

A5Prior knowledge What a learner already knows or believes. The strongest predictor of what they will learn next.

A6Misconception A prior belief that is wrong and distorts new learning until surfaced and corrected.

A7Retrieval practice Recalling information from memory rather than re-reading it. One of the most effective ways to build durable memory.

A8Spacing Distributing practice over time rather than massing it. Improves retention.

A9Desirable difficulty A challenge that slows performance now but improves long-term learning.

A10Transfer Applying learning to a new, different situation. The usual end goal, and the hardest to achieve.

A11Scaffolding Temporary support (hints, worked examples, structure) that is gradually removed as competence grows.

A12Universal Design for Learning (UDL) A framework for building flexibility in from the start: multiple means of engagement, representation and expression.

A13Self-determination Theory that sustained motivation rests on autonomy, competence and relatedness.

Appendix B

Multimedia design principles (Mayer)

The most directly actionable body of evidence for a design team, derived from decades of controlled experiments on how people learn from words and pictures on screens (Clark & Mayer, 2016). Each principle is stated with its practical instruction.

B1Multimedia People learn better from words and relevant graphics together than from words alone. Pair meaningful visuals with text; avoid text-only where a graphic would carry the idea.

B2Coherence People learn better when extraneous words, graphics and sounds are excluded. Cut seductive details, background music and decoration. Less is more.

B3Spatial contiguity Learning improves when words are placed near the graphics they describe. Put labels and captions next to the relevant part, not in a distant legend.

B4Temporal contiguity Learning improves when narration and matching visuals are presented at the same time. Sync spoken words with what appears on screen; do not stagger them.

B5Modality People learn better from graphics with narration than graphics with on-screen text, though the effect is smaller than once thought (Reinwein, 2012). For animations and diagrams, prefer narration where feasible, and test it.

B6Redundancy People learn worse from graphics, narration and identical on-screen text all at once. Do not display the same words you are narrating; it splits, not reinforces.

B7Segmenting People learn better in learner-paced chunks than in one continuous stream. Break content into segments the learner advances through; give a “next” control.

B8Pre-training People learn better when they already know the names and characteristics of key concepts. Introduce key terms and components before the full process that uses them.

B9Personalisation People learn better from a conversational than a formal style. Write in a warm, direct, human voice rather than stiff institutional prose.

B10Learner control and pacing People generally learn better when they can control the pace. Let learners pause, replay and advance; avoid unstoppable auto-play.

Appendix C

Six questions about how learning works

Deans for Impact (2015) distils cognitive science into six questions every educator, and every designer of a learning system, should be able to answer. A useful diagnostic to run against any design.

C1How do students understand new ideas? By connecting them to prior knowledge, through examples, analogies and well-sequenced content, without overloading working memory. Design consequence: activate prior knowledge; sequence carefully; manage load.

C2How do students learn and retain new information? Through effortful retrieval and spaced, repeated practice, not passive review. Design consequence: build in low-stakes retrieval and spacing.

C3How do students solve problems? By drawing on stored knowledge; there is no substitute for foundational knowledge in a domain. Design consequence: do not assume skills without the knowledge they rest on.

C4How does learning transfer to new situations? Slowly and unreliably; transfer needs varied practice and explicit underlying structure. Design consequence: vary contexts; make principles explicit; use authentic tasks.

C5What motivates students to learn? A mix of belief in improvability, appropriate challenge, belonging and value, not rewards alone. Design consequence: support competence, autonomy and safety; use extrinsic rewards with care.

C6What are common misconceptions about learning? Learning styles, “digital natives” and fixed developmental stages are myths that mislead design. Design consequence: design against these myths.

Appendix D

Whole-child design principles (SoLD)

Design Principles for Schools (Darling-Hammond et al., 2021) synthesises the science of learning and development into system-level principles. Written for schools, they translate directly to the design of a national learning environment, and they are a reminder that cognition, emotion and relationship are one system.

D1Positive developmental relationships Learning is relational. Even in a digital system, design should support connection, human support and being known, not isolation.

D2Environments filled with safety and belonging Learners engage and take risks only where they feel safe and included. Psychological safety is a design requirement.

D3Rich learning experiences and knowledge development Meaningful, well-designed content and tasks, made flexible through Universal Design for Learning, not thin engagement.

D4Development of skills, habits and mindsets Integrate social, emotional and cognitive development; support self-regulation, persistence and a growth mindset.

D5Integrated, whole-child design These elements reinforce each other and must be designed together, not as separate features bolted on.

Appendix E

Myths to design against

Widely believed, intuitively appealing, and not supported by evidence. Each has misled real product decisions. Each is a myth for the same reason: the claim has been tested and the tests came back negative, or the mechanism it assumes does not exist.

Appendix F

Sources and further reading

Grouped by how the team is likely to use them. Items marked ▪ are in the project library. The full alphabetical list, with links, follows in the references.

F1Start here, short and practical Deans for Impact (2015), The science of learning ▪ Deans for Impact (2022), Deepening meaning and learning ▪ Kirschner and Hendrick (2020), How learning happens ▪ Willingham (2023), Outsmart your brain ▪

F2Directly design-relevant Clark and Mayer (2016), e-Learning and the science of instruction ▪ Floor (2023), This is learning experience design ▪ CAST (2024), Universal Design for Learning Guidelines 3.0

F3Foundational and comprehensive Sawyer (2014), The Cambridge handbook of the learning sciences ▪ Seel (2012), Encyclopedia of the sciences of learning ▪ Benassi, Overson and Hakala (2014), Applying science of learning in education ▪ Bransford, Brown and Cocking (2000), How people learn

F4Emotion, development and the brain Immordino-Yang (2016), Emotions, learning, and the brain ▪ Darling-Hammond et al. (2021), Design principles for schools ▪ Tokuhama-Espinosa (2022), Bringing the neuroscience of learning to online teaching ▪ McNeil (2016), Learning with the brain in mind ▪ Schwartz, Tsang and Blair (2016), The ABCs of how we learn ▪ Watson and Busch (2021), A parent’s guide to the science of learning ▪

F5Motivation, persuasion and social behaviour Cialdini (2007), Influence ▪ Berne (1964), Games people play ▪ Sassenberg and Vliek (2019), Social psychology in action ▪

F6Key studies cited in the primer Bjork and Bjork (2011), desirable difficulties Sweller (1988), cognitive load Roediger and Karpicke (2006), the testing effect Cepeda et al. (2006), spacing Deci, Koestner and Ryan (1999), rewards and intrinsic motivation Ryan and Deci (2000), self-determination theory Pashler et al. (2008), learning styles Kalyuga et al. (2003), the expertise reversal effect Ginns (2006), contiguity Yeager et al. (2019) and Macnamara and Burgoyne (2023), mindset

Source trail

References

Every work the deck and this article rest on, in one alphabetical list. APA 7th edition. Links go to the DOI or publisher page where one exists; books and reports without a stable link are unlinked rather than guessed. Items marked ▪ are in the project library.

1. Adesope, O. O., Trevisan, D. A., & Sundararajan, N. (2017). Rethinking the use of tests: A meta-analysis of practice testing. Review of Educational Research, 87(3), 659–701. https://doi.org/10.3102/0034654316689306
2. Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359
3. Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612
4. Benassi, V. A., Overson, C. E., & Hakala, C. M. (Eds.). (2014). Applying science of learning in education: Infusing psychological science into the curriculum. Society for the Teaching of Psychology. ▪
5. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. https://doi.org/10.1111/j.1467-8535.2007.00793.x
6. Berne, E. (1964). Games people play: The psychology of human relationships. Grove Press. ▪
7. Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64). Worth Publishers. https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork_RBjork_2011.pdf
8. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school (Expanded ed.). National Academies Press. https://doi.org/10.17226/9853
9. Capp, M. J. (2017). The effectiveness of universal design for learning: A meta-analysis of literature between 2013 and 2016. International Journal of Inclusive Education, 21(8), 791–807. https://doi.org/10.1080/13603116.2017.1325074
10. Carpenter, S. K., Cepeda, N. J., Rohrer, D., Kang, S. H. K., & Pashler, H. (2012). Using spacing to enhance diverse forms of learning: Review of recent research and implications for instruction. Educational Psychology Review, 24(3), 369–378. https://doi.org/10.1007/s10648-012-9205-z
11. Carpenter, S. K., Wilford, M. M., Kornell, N., & Mullaney, K. M. (2013). Appearances can be deceiving: Instructor fluency increases perceptions of learning without increasing actual learning. Psychonomic Bulletin & Review, 20(6), 1350–1356. https://doi.org/10.3758/s13423-013-0442-z
12. CAST. (2018). Universal Design for Learning Guidelines version 2.2. https://udlguidelines.cast.org/
13. CAST. (2024). Universal Design for Learning Guidelines version 3.0. https://udlguidelines.cast.org/
14. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. https://doi.org/10.1037/0033-2909.132.3.354
15. Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, 19(11), 1095–1102. https://doi.org/10.1111/j.1467-9280.2008.02209.x
16. Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis. Psychological Bulletin, 140(4), 980–1008. https://doi.org/10.1037/a0035661
17. Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2
18. Cialdini, R. B. (2007). Influence: The psychology of persuasion (Rev. ed.). Harper Business. ▪
19. Clark, R. C., & Mayer, R. E. (2016). e-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). Wiley. https://doi.org/10.1002/9781119239086 ▪
20. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922
21. Darling-Hammond, L., Cantor, P., Hernández, L. E., Theokas, C., Schachner, A., Tijerina, E., & Plasencia, S. (2021). Design principles for schools: Putting the science of learning and development into action. Learning Policy Institute & Turnaround for Children. https://k12.designprinciples.org/ ▪
22. Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791
23. Deans for Impact. (2015). The science of learning. Deans for Impact. https://deansforimpact.org/resources/the-science-of-learning/ ▪
24. Deans for Impact. (2022). Deepening meaning and learning. Deans for Impact. ▪
25. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668. https://doi.org/10.1037/0033-2909.125.6.627
26. Dekker, S., Lee, N. C., Howard-Jones, P., & Jolles, J. (2012). Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology, 3, 429. https://doi.org/10.3389/fpsyg.2012.00429
27. Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116
28. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
29. Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
30. Floor, N. (2023). This is learning experience design: What it is, how it works, and why it matters. New Riders. ▪
31. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38. https://doi.org/10.1016/0010-0285(83)90002-6
32. Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15(4), 313–331. https://doi.org/10.1016/j.learninstruc.2005.07.001
33. Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16(6), 511–525. https://doi.org/10.1016/j.learninstruc.2006.10.001
34. Hanus, M. D., & Fox, J. (2015). Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers & Education, 80, 152–161. https://doi.org/10.1016/j.compedu.2014.08.019
35. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
36. Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368
37. Immordino-Yang, M. H. (2016). Emotions, learning, and the brain: Exploring the educational implications of affective neuroscience. W. W. Norton. ▪
38. Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3–10. https://doi.org/10.1111/j.1751-228X.2007.00004.x
39. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4
40. Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12–19. https://doi.org/10.1177/2372732215624708
41. Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331(6018), 772–775. https://doi.org/10.1126/science.1199327
42. Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
43. Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. https://doi.org/10.1016/j.tate.2017.06.001
44. Kirschner, P. A., & Hendrick, C. (2020). How learning happens: Seminal works in educational psychology and what they mean in practice. Routledge. https://doi.org/10.4324/9780429061523 ▪
45. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
46. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. https://doi.org/10.1037/0033-2909.119.2.254
47. Koedinger, K. R., Kim, J., Jia, J. Z., McLaughlin, E. A., & Bier, N. L. (2015). Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (pp. 111–120). ACM. https://doi.org/10.1145/2724660.2724681
48. Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories: Is spacing the “enemy of induction”? Psychological Science, 19(6), 585–592. https://doi.org/10.1111/j.1467-9280.2008.02127.x
49. Macnamara, B. N., & Burgoyne, A. P. (2023). Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices. Psychological Bulletin, 149(3–4), 133–173. https://doi.org/10.1037/bul0000352
50. McNeil, F. (2016). Learning with the brain in mind. Sage. ▪
51. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
52. Newton, P. M., & Salvi, A. (2020). How common is belief in the learning styles neuromyth, and does it matter? A pragmatic systematic review. Frontiers in Education, 5, 602451. https://doi.org/10.3389/feduc.2020.602451
53. Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583–15587. https://doi.org/10.1073/pnas.0903620106
54. Osher, D., Cantor, P., Berg, J., Steyer, L., & Rose, T. (2020). Drivers of human development: How relationships and context shape learning and development. Applied Developmental Science, 24(1), 6–36. https://doi.org/10.1080/10888691.2017.1398650
55. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
56. Reinwein, J. (2012). Does the modality effect exist? And if so, which modality effect? Journal of Psycholinguistic Research, 41(1), 1–32. https://doi.org/10.1007/s10936-011-9180-4
57. Rey, G. D. (2012). A review of research and a meta-analysis of the seductive detail effect. Educational Research Review, 7(3), 216–237. https://doi.org/10.1016/j.edurev.2012.05.003
58. Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
59. Rohrer, D., Dedrick, R. F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107(3), 900–908. https://doi.org/10.1037/edu0000001
60. Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35(6), 481–498. https://doi.org/10.1007/s11251-007-9015-8
61. Rowland, C. A. (2014). The effect of testing versus restudy on retention: A meta-analytic review of the testing effect. Psychological Bulletin, 140(6), 1432–1463. https://doi.org/10.1037/a0037559
62. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
63. Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32(1), 77–112. https://doi.org/10.1007/s10648-019-09498-w
64. Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31. https://doi.org/10.1016/j.compedu.2012.10.003
65. Sassenberg, K., & Vliek, M. L. W. (Eds.). (2019). Social psychology in action: Evidence-based interventions from theory to practice. Springer. ▪
66. Sawyer, R. K. (Ed.). (2014). The Cambridge handbook of the learning sciences (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526 ▪
67. Schwartz, D. L., Tsang, J. M., & Blair, K. P. (2016). The ABCs of how we learn: 26 scientifically proven approaches, how they work, and when to use them. W. W. Norton. ▪
68. Seel, N. M. (Ed.). (2012). Encyclopedia of the sciences of learning. Springer. ▪
69. Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/10.1177/0956797617739704
70. Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199. https://doi.org/10.1177/1745691615569000
71. Sundararajan, N., & Adesope, O. (2020). Keep it coherent: A meta-analysis of the seductive details effect. Educational Psychology Review, 32(3), 707–734. https://doi.org/10.1007/s10648-020-09522-4
72. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
73. Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
74. Tokuhama-Espinosa, T. (2022). Bringing the neuroscience of learning to online teaching: An educator’s handbook. Teachers College Press. ▪
75. Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451. https://doi.org/10.1126/science.1198364
76. Walton, G. M., Murphy, M. C., Logel, C., Yeager, D. S., Goyer, J. P., Brady, S. T., ... Krol, N. (2023). Where and with whom does a brief social-belonging intervention promote progress in college? Science, 380(6644), 499–505. https://doi.org/10.1126/science.ade4420
77. Watson, E., & Busch, B. (2021). A parent’s guide to the science of learning: 77 studies that every parent needs to know. Routledge. ▪
78. Willingham, D. T. (2023). Outsmart your brain: Why learning is hard and how you can make it easy. Gallery Books. ▪
79. Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, 3087. https://doi.org/10.3389/fpsyg.2019.03087
80. Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399–435. https://doi.org/10.1037/bul0000309
81. Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., ... Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. https://doi.org/10.1038/s41586-019-1466-y

End of the primer

Back to geraldajam.com