KS2 progress scores will be returning for the current Y6 cohort. But can we predict what targets children need to hit in order to attain positive progress scores?

The short answer to the above question is no, we can’t say for sure what these target attainment scores will be.

The reason for this is that the progress model changes every year, based on what has happened nationally. A positive progress score is achieved when your pupils have attained a higher scaled score than the national average scaled score for children with the same starting point (i.e. KS1 average attainment) – as I explained in this previous blog.  It is always the case that we can’t say for sure what the national pattern of average progress will be in any given year. But it’s even more complicated than normal this year.

We last had KS2 progress scores in summer 2023, so we know what the national rates of progress looked like then. We could, therefore, use these as a guide to what children should be attaining next summer. However, there are very good reasons to hypothesise that the national pattern of progress might look like different in 2026. The children who completed KS2 in 2023 had endured national lockdowns and a sudden switch to online lessons for significant chunks of 2020 and 2021 (i.e. during their Years 3 and 4). This is likely to have negatively affected progress rates across the whole country. For the current Year 6 cohort, their KS2 has not been interrupted in this way - therefore we might expect the national average progress rates to be higher in 2026 than they were in 2023.

On the other hand, for this Y6 cohort, the effect of the pandemic hit them particularly hard in their Reception and Year 1, both extremely important formative years. They might, therefore, have under-achieved (compared to what we might normally expect) when they completed KS1 in 2022 – and might have had major gaps in their learning.  How that has affected their progress across KS2 is impossible to predict – for some children in some schools, they might have caught up on lost learning very effectively. Progress in these cases will be strong. For other children, particularly those experiencing multiple barriers such as socio-economic disadvantage, SEND etc, it could have been a real challenge to catch up on lost learning.

In short, it is impossible to predict what the national average outcomes for each prior attainment group will look like.

A better proxy for what progress might look like this year might be to go back to the 2019 model, before Covid. Unfortunately, this model is not comparable because of differences in the way KS1 was assessed. For children who completed KS2 in 2019 – and hence completed KS1 in 2015 – their KS1 assessment was reported using the old ‘levels’ system. We can’t really draw equivalences between the different ways of assessing KS1. (Believe me, I have made some attempts to statistically align the different models and ended up concluding that it was a futile exercise.)

Our best bet, then, if we want to try to establish some ‘target scaled scores’ that we would hope will yield positive progress measures, is to look across both the 2022 and 2023 models and use these to set a ‘minimum expectation’ – with the understanding that the 2026 model could end up being a point or so higher.

The table below illustrates 3 example children – the first assessed at Working Towards in all 3 subject areas at KS1 (prior attainment score of 6); the second assessed at EXS across the board (score of 8); the third assessed at GDS across the board (score of 10).

Using the above, I might be inclined to set the following ‘minimum targets’ for each prior attainment group shown above:

The above scores might give us a safe indication of where ‘average progress’ will fall, but to be on the safe side we might want to add an extra scaled score point on to the above.

A word about the writing target. Due to the way the model is calculated (which I discussed in this blog) we end up with national average writing scores that are not actually attainable by any individual child. For example, in 2023, the national average writing score for children with a prior attainment of 10 was close to 109.  Given that an outcome of EXS is scored as 103 and GDS is scored as 113, no child can end up with a score of 109.  But, roughly speaking, if around 3/5 of the children in this group achieved GDS and the other 2/5 achieved EXS, we would end up with this average score. From the point of view of target-setting, then, if you happened to have 5 children all with this prior attainment score, then statistically you might expect 3 of them to achieve GDS and the other 2 to achieve EXS. If this does indeed happen, your average progress for this group would be at least average (provided the national average progress rates don’t significantly change).

In the downloadable spreadsheet below, I have produced what we might consider to be realistic minimum targets for every prior attainment score. It will even calculate them for you, if you enter the children’s KS1 assessment data. However – important disclaimer – there is no guarantee that, by attaining these targets, children will achieve positive progress scores, for the reasons explained above. This tool is intended to be used as a guide only. As stated earlier, for a safer bet, aim 1 scaled score higher (or more).

We won’t know the actual 2026 progress model until after the children have taken the tests and the data has all been processed. The good news, though, is that once we do have the model, we might expect it to be a reasonably good basis for predicting the 2027 model, as the experience of these two cohorts of children nationally should have been broadly very similar.

Of course, it all changes again in 2028, when the Y6 children’s progress will be measured from their Reception Baseline Assessment scores. Quite what that national model will look like is anybody’s guess.

This blog continues our series exploring how to enable children to showcase their mathematical understanding, reasoning and problem solving in the KS2 maths SATs papers. We know that for the reasoning papers, pupils need to be able to process and manage quite a range of information for some questions, and there needs to be specific teaching of strategies to enable them to do this.

In part 1 (Measures made visual: using bar modelling to support reasoning in KS2 SATs), the bar model was used to exemplify how to solve questions from reasoning paper 2 from the KS2 SATs 2025 with a particular focus on problems involving measures. And like our blog series from 2024, the focus of part and whole remains at the forefront.

In this blog, I will explore questions from the KS2 SATs Reasoning Paper 3. Like part 1, the focus will be on questions involving measures, as there were a number of these, in different styles and formats. Like part 1, there will also be a focus on part and whole. But in this blog, I will exemplify questions where bar modelling might not be an obvious strategy choice.

When you read the examples in this blog, you may find yourself thinking that bar modelling is not a particularly efficient strategy for the questions that I have chosen. I challenge you though to disregard efficient strategies for the time being and instead consider how exploring these questions in a visual way may aid the conceptual understanding which underpins the more efficient strategies your children will be aiming for. 

Example 1:

2025 KS2 SATs – Mathematics Paper 2, question 10

Bar modelling focussing on proportion and comparison

In this question, information is shared in a data table format. The table is followed by four statements, and we need to ascertain which are true and which are false.

For the first 3 statements, I have exemplified below how different multiplicative models can be useful to support children to uncover the needed calculations.

For the first statement,  a comparison, or ratio model (as all parts are equal) is a useful visual aid.

Solution (to this statement)

The first statement is false as the elephant would need to have a mass of 7,200kg.

The comparison, or ratio model, is useful here to illustrate the relationships between numbers and equal parts where multiplicative language such as ‘three times’ is used. In this case, we could either divide the greater mass by 3 or multiply the smaller mass by 3.

Now for statement 2. 

Solution (to this statement)

The second statement is true as the hippo’s height is 1.5m which is one quarter of 6m.

Exploration of this statement using the bar model may be a useful reminder of models used to find fractions of amounts where all of the parts are equal and the whole is the total of the equal parts. In this example,  I have used division to find the answer, but I could also have used multiplication and multiplied the hippo’s height by 4 to see if this matched the given height of the giraffe.

For statement 3, a multiplicative model (involving equal parts) would not be helpful as this statement is asking us to consider the difference. 

Solution (to this statement)

The third statement is true as the rhino’s height (1.7m) is 20cm greater than the hippo’s (1.5m).

I know that models involving the difference, and/or language such as ‘taller than’ can be tricky to visualise and so the bar model, where the whole can be compared with the parts, is a useful way of reinforcing this.

Although not exemplified here, the final statement could be used to consider proportional relationships in both the heights and the masses of the animals when constructing bar models and considering how accurately we can draw them. 

Solution to the question

Statements two and three are true.

Example 2:

2025 KS2 SATs – Mathematics Paper 3, question 19

Bar modelling focussing on equal parts

The information in this question is almost already presented to us as a bar model but perhaps in a less familiar, vertical representation. By putting the information into horizontal bars, and once again using a comparison, or ratio model (as all the parts, or blocks in this case, are equal), the operations needed to solve the problem become much clearer.

Solution

The height of the smaller tower is 13.2cm.

As well as the bar model being a useful tool for this question, this question is also helpful in highlighting the importance of base facts. Here, as long as we know that 20 divided by 5 equals 4, then we can use this fact to help us to calculate that 2 divided by 5 will be 0.4. We can draw on the relationship that 2 is ten times smaller than 20 so the quotient of 2 divide by 5 will be ten times smaller than the quotient of 20 divided by 5. 

Example 2:

2025 KS2 SATs – Mathematics Paper 3, question 22

Bar modelling focussing on part and whole in the contexts of missing angles

Bar modelling might not be an obvious tool for this question but once we pull on our knowledge of interior angles in shapes and on a straight line, the usefulness of considering parts and wholes becomes clearer and helps to make a complex question much simpler.  

Angle a equals 50°.

By using different bar models to present what is known about each of the shapes including angle a and then angle b, this helps us to consider each whole in turn and gives us a good starting point in solving the problem.

As Charley states at the end of blog 1 in this year’s SATs bar modelling series, bar modelling isn’t just a useful strategy for solving these questions at the end of KS2 – Year 6 – but a strategy that could be taught, rehearsed and honed as children move through the primary phase. In addition, bar modelling is a thoroughly useful tool for assessment for learning as through building and exploring models, possible misconceptions are often uncovered. Hopefully, no matter what year group you teach in, by exploring these KS2 assessment examples, you can link these to your year group curriculum whether that be exploring part and whole in addition and subtraction in KS1 to moving into more complex multiplicative models in KS2

Further professional development

Our popular training is back this year in a new format.

Across four half-day sessions, we unpick tools for supporting children in meeting age-related expectations in maths using a range of different strategies, including the bar model!

Resources available on PA Plus, all included within the subscription and some also available as a separate purchase if you are not subscribed, which support with Bar Modelling and Year 6:

Bar modelling in maths progression

• Download sample pages
• A combination of photos of concrete resources, pictorial bar models and abstract calculations brought together to exemplify how to solve problems from across the maths curriculum.

Year 6 maths gap finder: SATS preparation toolkit

• Diagnostic summative assessment papers for early identification of gaps
• Resources to support teaching and rehearsal of arithmetic and reasoning

Year 6 SATS analysis toolkit

• a suite of resources and analysis tools to identify specific areas of learning strength and development for pupils and classes when using any past SATs papers as practice (2016-2024).

AI presents many challenges and opportunities for schools, governors & trustees who should ensure and monitor that it’s being used in ways that enhance school life and opportunities for pupils, as well as helping reduce the workload of school leaders and staff. Rebecca Jones, from our school’s HR team discusses just such an opportunity and considers the associated risks.

AI is transforming recruitment across sectors, and education is no exception. From automated screening to predictive analytics, and sample questions, AI promises faster, smarter hiring decisions. But in schools - where recruitment isn’t just about qualifications, but also safeguarding and cultural fit, its use raises serious questions. When you strip away the buzzwords and automation, one question remains: Can a synthetic decision maker keep children safe?

As someone working in education recruitment, I’ve found myself increasingly curious and concerned about the growing role of AI in hiring. The promises are bold: faster shortlisting, reduced bias, streamlined processes. But beneath the surface, I’ve started digging into the pitfalls, especially around safer recruitment, which is far more than a compliance exercise - it’s a safeguarding imperative. The more I explore, the clearer it becomes: While AI might offer efficiency, it struggles with the very things that matter most in schools - human nuance, ethical judgment, and the instinct to protect.

Let’s start with the facts:

• AI use in recruitment has tripled in the UK in the past year.
• 30% of UK employers now use AI to recruit; 43% of large companies use AI to interview candidates.
• 70% of recruiters say AI improves hiring decisions, and it cuts hiring costs by up to 71%.

There’s no denying that recruitment can be time-consuming and expensive. Some roles attract large volumes of applications, and shortlisting can be a logistical challenge. AI offers solutions:

• Speed and efficiency: Algorithms can scan hundreds of applications in minutes, flagging those that meet key criteria.
• Consistency: AI applies the same rules to every candidate, reducing variability and helping ensure fairness.
• Data-driven decisions: Predictive analytics can assess which candidates are most likely to succeed based on historical data.
• Bias reduction: AI can be programmed to ignore demographic data, potentially reducing unconscious bias.

For governors, trustees and school leaders under pressure to make good hiring decisions quickly, these benefits are appealing. AI is alluring, but here’s the problem: schools aren’t hiring for productivity - they’re hiring for trust. And trust isn’t something you can measure with an algorithm.

AI is no silver bullet, and in education, its limitations aren’t just inconvenient, they’re dangerous. Schools aren’t hiring for output; they’re hiring for trust, integrity, and safety. Recruitment in this sector is a safeguarding process, not a productivity exercise. Every appointment carries risk, and safer recruitment isn’t a checklist - it’s a mindset rooted in human judgment.

Yes, AI can flag gaps in employment and track DBS checks. But it cannot detect charm masking manipulation, interpret vague or evasive references, or ask the uncomfortable follow-up question. It can’t sense when something feels off - and in safeguarding, that instinct can be the difference between protection and catastrophe.

Worse still, AI is only as unbiased as the data it’s trained on. If past hiring decisions reflect systemic inequalities, AI doesn’t correct them, it amplifies them. And when we start relying on algorithms to make decisions that demand human scrutiny, we risk sidelining professional judgment and missing the very red flags we’re supposed to catch.

In a sector where the stakes are children’s safety, outsourcing vigilance to an algorithm isn’t just flawed, it’s reckless.

In serious case reviews where children have come to harm in school settings, time and time again it is the same failures that come to light: Incomplete checks and blind trust in processes. Between 2019 and 2021, 59 schools were judged by Ofsted to have “not effective” safeguarding. The most common failures? Poor record keeping, weak leadership, and failure to follow up concerns. In one study of 41 professionals who sexually offended against children, 92.5% were aware of their interest before age 21, and 15% chose their career specifically to abuse.

The sad truth is that some people choose education roles specifically to access children. And we think AI can screen them out?

AI has a place in education recruitment, but only if we’re brutally honest about its limits. 

It can help with admin. Elements of AI have been incorporated into the Teach in Herts website, for example, to flag gaps in employment. But it cannot replace the human responsibility to protect children. It’s crucial that school leaders retain full control over their recruitment process.

Let’s not sleepwalk into risk. AI might be the future of recruitment, but in education, we cannot afford to follow blindly. Safeguarding isn’t optional. It’s not a feature. It’s the foundation.

If you're considering AI in your recruitment process, ask yourself: Is it helping you make safer decisions, or just faster ones? Are you confident your recruitment process puts safeguarding first? Or are you trusting a system that doesn’t know what danger looks like?

Let’s keep the conversation going. Let’s challenge the hype. And most importantly, let’s keep children safe.

Further reading 

Recruitment service | HFL Education
Safer Recruitment Training 
AI for School Leaders and Business Managers
AI in Recruitment statistics UK 2025 | Latest reports & data
Safeguarding: What Can we Learn from Schools where it was Judged 'Not Effective' | Judicium Education
Safer recruitment and the importance of getting it right