The evidence
How automating works, and why
This is not a times-tables quiz with an animal stuck on it. It is one didactic idea, from start to finish: how you move a number fact from working it out to knowing it. Below is that idea, with the research underneath it.
The idea
Automating is more than learning by heart
Automating arithmetic is the ability to solve a sum quickly, accurately and almost by itself, without thinking about it for long Kennisrotonde, 2023. The literature distinguishes two things inside that, and the difference is the heart of this whole programme Gerrits & Noteboom, 2018:
- Working it outYou can work it out. You have a method — a strategy — that gets you to the right answer, even if it costs a few seconds.
- Knowing itYou know it instantly. The answer comes out of memory, with no working out. This is what memorisation means.
A fact is only truly automated when you can do both: work it out and know it directly. Which is why practising speed alone (drilling without understanding) or strategy alone (understanding without ever becoming fluent) is not enough either way. The order is: first be able to work it out, then know it by heart.
This four-state model comes straight out of Dutch primary maths teaching — the idea that a fact is first «worked out» and only later «known by heart» Gerrits & Noteboom, 2018; Danhof et al., 2013. Geography uses exactly these four states: in your account you can see, per table, which sums are grey, red, orange or green.
Not just arithmetic. The same idea holds for the other subjects: in spelling a word image you remember instead of puzzling it out every time, in grammar a test that runs by itself, in geography a country you know the place of straight away, in typing a key you find without looking, and in traffic a situation you recognise at once. First be able to work it out, then know it.
- Newnot practised yet7 × 8 = ?
- Rednot yet7 × 8 =
54 - Orangecan work it out5 × 8 + 2 × 8 = 56
- Greenknows it straight away7 × 8 = 56
Why it matters
A full head cannot think
Working memory — the place where you «hold» something while you think — is small. If a child still has to work out 7 × 8, that small memory is already full before the actual problem (a division, a fraction, a word problem) even begins. A child who has automated the basic facts keeps that memory free for the real thinking. Working on automation therefore has a positive effect on arithmetic in general Kennisrotonde, 2023.
It is not a side effect that arrives on its own. In one study, children who trained deliberately for speed and automation had measurably better arithmetic two months later than children who practised only with the right strategy without training for speed Singer-Dudek & Greer, 2005. And it keeps going all through primary school: addition tends to settle around age 7–8, subtraction and multiplication around 9–10, and division reaches fluency only after primary school for many children Gliksman et al., 2022.
Still working it out
Knowing it
Understand first
You do not learn a table on its own, you derive it
Programmes that teach the strategy first and automate after turn out to be more effective than programmes that do only one or the other Ok & Bryant, 2016, Carr et al., 2011, Woodward, 2006. Knowledge you understand can also be applied flexibly to new sums Boaler et al., 2015. So the app teaches the tables in the order in which they hold each other up — the «anchor tables» first (×1, ×2, ×10, ×5), because every other one can be derived from those SLO — kerndoelen.
Times 10
7 × 10
A zero goes on the end. 7 → 70. The easiest anchor table, and the basis for nearly every other one.
Times 5 is half of times 10
6 × 5
6 × 10 = 60, and half of that is 30.
Times 9 is times 10, minus one lot
8 × 9
8 × 10 = 80, minus 8, is 72.
Split the hard tables
7 × 6
5 × 6 = 30 (you know that one), plus 2 × 6 = 12, is 42.
Where Geography does this. Every table carries its strategy on the child’s own path — «this is how you work it out», with a worked example. And every time a child gets something wrong, what appears is not just the right answer but that same way of reaching it, applied to that sum.
- anchor: × 1, × 2, × 5, × 10
- derived
7 × 6 = 5 × 6 + 2 × 6
together: 30 + 12 = 42
How you practise
Four things the research shows over and over
Short, daily and spread out
Ten to fifteen minutes a day is more effective than one long stretch. It works better still when that time is spread across the day with generous gaps, for instance three times five minutes.
Short and spread out
All in one go
In the app: 3 short sessions a day, with rest in between — never one long one.
Inspectie van het Onderwijs, 2011; Powell et al., 2022; Schutte et al., 2015; Cepeda et al., 2006; Rohrer & Taylor, 2007
A good mix of easy and hard
Effective practice keeps a good ratio between sums a child already knows and sums that are still hard. Enough footing to keep going, enough resistance to learn something.
- 10 goes well
- 5 not yet
In the app: Every session is 10 sums that already work and 5 that do not yet.
Burns, 2005; Codding et al., 2011; Fuchs et al., 2008
See straight away whether it is right — and why
Children need to see immediately whether their answer is right, and a worked example helps them repair the mistake instead of repeating it. Modelling and immediate feedback are constants in what works.
7 × 6 = 48
Nearly. It is 42.
- 5 × 6 = 30
- 2 × 6 = 12
- 30 + 12 = 42
In the app: A mistake stops the session: the screen turns, the right answer appears, and so does the explanation.
Codding et al., 2011; Fuchs et al., 2008; Renkl & Atkinson, 2003
Produce the answer yourself
Recognising (pointing at the right answer among options) is not the same as remembering (calling the answer up yourself). Only the second trains the memory that automating needs.
Typing it
7 × 8 = 56
1234567890Picking from four
48545663In the app: No multiple choice anywhere: your child types or taps the answer.
Roediger & Karpicke, 2006; Karpicke & Roediger, 2008
Fitted to the child
One continuous line, and the child finds its place on it
Mental arithmetic is not a loose collection of facts but a continuous line: the bonds to ten make bridging the ten possible, that makes the tables possible, and division is a table read backwards. Every step rests on the one before, and every step has a threshold that has to be crossed before the next one makes any sense Danhof et al., 2015. Geography puts that whole line on one ladder — from the first splits to the big tables, division and telling the time — in the order the curriculum prescribes SLO — kerndoelen.
A child should not start halfway through a school year at whatever point their class happens to be. Anyone who does not know where to start can therefore take a short placement test per area. It is a choice, not a gate: questions from every topic in that area, around the level of the child’s year, from which the app sees what is already fluent and what is not. On that basis it moves the child a step up if they already have the material, or a step down if the foundation is not there yet — with one line of explanation why. That way nobody practises what they already know, and nobody gets stuck on something whose stepping stone is missing Danhof et al., 2013.
And it does not stop there. Every sum adjusts the level: how fast and how often it goes right decides whether a fact turns green and drops out of sight, or comes back. If a block goes too easily the line shifts up a school year by itself; if a block keeps catching, the app pulls the part underneath it — sometimes a year lower — back in until it holds again. The level is not a setting chosen once, but something that keeps moving with the child. Underneath that colour, the app also watches reaction time and which sub-step — a borrow column, a denominator — is still catching, without that ever changing the colour itself.
- The placement test finds your place
- Going smoothly? A step up.
- Basics shaky? A step back.
Move on once it sticks
Mastery learning: the next step comes once the previous one sticks
Benjamin Bloom saw that pupils get much further when they only start the next step once the previous one is truly mastered, with extra explanation and practice for those who are not there yet Bloom, 1968. A review of dozens of studies on this mastery learning found clearly better results, above all for the pupils who struggled Kulik et al., 1990. The heart of it is not being stricter, but letting nobody move on with a gap underneath Guskey, 2007.
In Geography this happens by itself: a new step only opens once the previous one comes from memory, and whatever has not stuck comes back until it does. A teacher can set this even stricter per subject, so the pupil cannot jump ahead on their own, and sees in the class heatmap, down to the single sum, who has mastered it and who has not. Whatever a child needs, the teacher or parent sets with one tap.
- × 9locked until × 5 is green
- × 5up now
- × 10secure
- × 1 · × 2secure
Formative assessment
See what has stuck, and shape the next lesson around it
Formative assessment is teaching on what you have just seen: finding out where each child stands, and choosing the next step on that straight away. A much-cited review of the research found that exactly this feedback during learning — and not a mark afterwards — moves pupils forward, and the weaker pupils most of all Black & Wiliam, 1998.
In Geography the looking happens while children practise. A teacher sees a heatmap per child and per class: every step grey, red, orange or green, down to the single sum. Who has mastered something and who has not is visible at a glance, and whatever a child or a small group needs, you set with one tap. A short test — the placement round, or a quiz with the class — shows where a new lesson should start, without turning it into a mark.
- New
- Red
- Orange
- Green
Set: × 5 for Ben and Noah
The engine
What is doing the sums under the colour
Red, amber and green are what you see; underneath, the app keeps a probability model of three states per fact — new, can work it out, knows it instantly — and moves it after every answer with the same kind of maths that has been used for thirty years to track what someone knows: Bayesian Knowledge Tracing Corbett & Anderson, 1994. Two kinds of answer do not move that model: asking for help (it proves the child can work it out, not that they remember it) and a correct retry after a mistake — that does not count as first evidence.
Alongside those three states per fact, the app also keeps a skill rating per learning line that moves with the child, on the same kind of scale that has ranked chess players against each other for a hundred years: an Elo rating Elo, 1978. Every fact gets its own difficulty from the rung it sits on; a correct answer on something harder than expected pushes the rating up more than a correct answer on something easier. That way the app already has a sense of how likely a child is to know a sum before it has ever been asked — and that is what makes the placement test, and the very first guess at any new fact, land in the right place.
Time counts as a third observation alongside right and wrong: a slow correct answer mostly feeds «can work it out», a fast correct answer mostly feeds «knows it instantly» — exactly the distinction this whole page opens with. And where a question has several steps inside it (a borrow column in subtraction, the minutes on a clock, the denominator of a fraction), the app keeps a separate profile of those too: a block that leans on a sub-step still catching stays lower, even when the rest of the line is doing fine.
This model steers exactly two things, and never a third: which question comes up in a sprint, and where the line sits between «practised enough» and «keeps coming back». It never steers the colour itself — that keeps counting on the same three fields a parent and a teacher see (how often right, how often tried, how fast), so a green tick keeps meaning the same thing, model or no model.
Three states of 7 × 8 example
- new10%
- can work it out30%
- knows it straight away60%
Time counts
- right in 7 s: can work it out
- right in 2 s: knows it
Child and sum on one scale
Ownership
You practise for yourself, not for a grade
Children who can see for themselves where they stand and what is left to learn practise more purposefully and are more motivated — in one study, self-regulated practice with your own progress in view was more effective than fifteen minutes of drilling rows Sleeman et al., 2021. An educational game adds no miracle of learning on top of that, but it does add something that counts: children feel more confident and keep practising for longer Pan et al., 2022, Cozad & Riccomini, 2016.
Which is why the only number a child sees about themselves in the app is how many days in a row they were there. No score, no leaderboard, no comparison with anyone else. Turning up is the behaviour we reward; the animal and its world are the reason to come back. «You are not doing it for the teacher, not for a mark, but for yourself» Gerrits & Noteboom, 2018.
For facts that are hard to remember, a mnemonic can help Nelson et al., 2013 — and knowledge that is not maintained fades Danhof et al., 2015. So the app keeps asking known facts back among the 10 of 15 «already works» sums, long after they have turned green.
4 days in a row
Honest about what this is
This page is a plain-language summary of published research on automating arithmetic. It is not evidence that this app itself has been trialled — that would be a different claim. What we can show is that every choice in the app traces back to a source below, and that a green fact means: 3 seconds or faster, and almost always right. The numbers on this page come from the same code the app runs on.
Accountability
Sources
- Kennisrotonde. (2023). Is een kwartier rekenautomatisering per dag effectief voor de rekenautomatisering van basisschoolleerlingen? Welke invulling (qua aanpak en inhoud) is het effectiefst per leerjaar? (KR.1849). NRO.
- Gerrits, P., & Noteboom, A. (2018). Rekenen op je basisvaardigheden: automatiseren en memoriseren. JSW, 102(10), 12–15.
- Danhof, W., Bandstra, P., & Hofstetter, W. (2015). Rekendrempels nemen. Volgens Bartjens, 34(3), 4–7.
- Danhof, W., Bandstra, P., Faber, S., Minnaert, A., & Ruijssenaars, W. (2013). Rapport Rekenproject Leerbaarheid van hoofdrekenen. Groningen: Rijksuniversiteit Groningen.
- SLO. Kerndoelen rekenen-wiskunde (o.a. kerndoel 26 en 27: de basisbewerkingen tot 100 uit het hoofd, de tafels van buiten). tule.slo.nl.
- Boaler, J., Williams, C., & Confer, A. (2015). Fluency without fear: Research evidence on the best ways to learn math facts. Reflections, 40(2), 7–12.
- Ok, M. W., & Bryant, D. P. (2016). Effects of a strategic intervention with iPad practice on the multiplication fact performance of fifth-grade students with learning disabilities. Learning Disability Quarterly, 39(3), 146–158.
- Carr, M., Taasoobshirazi, G., Stroud, R., & Royer, J. M. (2011). Combined fluency and cognitive strategies instruction improves mathematics achievement in early elementary school. Contemporary Educational Psychology, 36(4), 323–333.
- Woodward, J. (2006). Developing automaticity in multiplication facts: Integrating strategy instruction with timed practice drills. Learning Disability Quarterly, 29(4), 269–289.
- Singer-Dudek, J., & Greer, R. D. (2005). A long-term analysis of the relationship between fluency and the training and maintenance of complex math skills. The Psychological Record, 55, 361–376.
- Powell, S. L., Duhon, G., Poncy, B. C., Mwavita, M., & Englen, A. J. (2022). Distributed practice in math facts fluency: A comparative analysis of varied intersession intervals. School Psychology Review, 51(5), 517–525.
- Schutte, G. M., Duhon, G. J., Solomon, B. G., Poncy, B. C., Moore, K., & Story, B. (2015). A comparative analysis of massed vs. distributed practice on basic math fact fluency growth rates. Journal of School Psychology, 53(2), 149–159.
- Fuchs, L. S., Fuchs, D., Powell, S. R., Seethaler, P. M., Cirino, P. T., & Fletcher, J. M. (2008). Intensive intervention for students with mathematics disabilities: Seven principles of effective practice. Learning Disability Quarterly, 31(2), 79–92.
- Inspectie van het Onderwijs. (2011). Automatiseren bij rekenen-wiskunde: Een onderzoek naar het automatiseren van basisbewerkingen rekenen-wiskunde in het basisonderwijs. Utrecht.
- Burns, M. (2005). Using incremental rehearsal to increase fluency of single-digit multiplication facts with children identified as learning disabled in mathematics computation. Education and Treatment of Children, 28(3), 237–249.
- Codding, R. S., Burns, M. K., & Lukito, G. (2011). Meta-analysis of mathematic basic-fact fluency interventions: A component analysis. Learning Disabilities Research & Practice, 26(1), 36–47.
- Gliksman, Y., Berebbi, S., & Henik, A. (2022). Math fluency during primary school. Brain Sciences, 12(3), 371.
- Sleeman, M., Friesen, M., Tyler-Merrick, G., & Walker, L. (2021). The effects of precision teaching and self-regulated learning on early multiplication fluency. Journal of Behavioral Education, 30, 149–177.
- Pan, Y., Ke, F., & Xu, X. (2022). A systematic review of the role of learning games in fostering mathematics education in K-12 settings. Educational Research Review, 36, 100448.
- Cozad, L. E., & Riccomini, P. J. (2016). Effects of digital-based math fluency interventions on learners with math difficulties: A review of the literature. Journal of Special Education Apprenticeship, 5(2), 1–19.
- Nelson, P. M., Burns, M. K., Kanive, R., & Ysseldyke, J. E. (2013). Comparison of a math fact rehearsal and a mnemonic strategy approach for improving math fact fluency. Journal of School Psychology, 51(6), 659–667.
- Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.
- Elo, A. E. (1978). The rating of chessplayers, past and present. Arco Publishing. (Toegepast op leren in o.a. Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27, 313–350.)
- Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255.
- Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968.
- Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational Psychologist, 38(1), 15–22.
- 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.
- Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35(6), 481–498.
- Bloom, B. S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1–12.
- Kulik, C.-L. C., Kulik, J. A., & Bangert-Drowns, R. L. (1990). Effectiveness of mastery learning programs: A meta-analysis. Review of Educational Research, 60(2), 265–299.
- Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.
- Guskey, T. R. (2007). Closing achievement gaps: Revisiting Benjamin S. Bloom’s “Learning for Mastery”. Journal of Advanced Academics, 19(1), 8–31.
See the theory in practice?
Try it for two minutes, no account needed: pick a subject and a year and practise a real round. Nothing is saved.