Introduction
Introduction
Generating and then triangulating the data to answer these questions will help professionals make well-informed decisions about the next steps required for each pupil to maximise their potential.
Assessment data helps educational, and other, professionals make informed decisions. The whole pupil view proposition is that the ‘whole’ is greater than the sum of the parts. That is, having as complete a view as possible for each pupil will enable informed decisions to be made to help that pupil progress. The whole pupil view helps provide a methodology to investigate and analyse the data held on pupils to inform decision-making and maximise the outcomes for each pupil. The fundamental questions behind the whole pupil view are:
- What is the pupil’s potential?
- What is the pupil achieving?
- Are there any barriers to learning?
Generating and then triangulating the data to answer these questions will help professionals make well-informed decisions about the next steps required for each pupil to maximise their potential. This model is then scalable to a whole class, year group, school or grouping of schools.
The whole pupil view
The whole pupil view philosophy is based on the premise that each pupil has the ability to achieve, and that by triangulating various data types (ability, attainment and barriers to learning) across the data available, it is possible to maximise student attainment. However, it should be noted that the opposite is also true: if there are gaps in the data sets, how is it possible to triangulate data to make fully informed decisions to maximise the outcomes for each pupil?
The whole pupil view has four distinct elements:
- teacher judgement, which is informed by a wide range of contextual information and data. This is held by the school.
There are then three data sets that support teacher judgement to provide the whole pupil view:
- ability;
- attainment;
- barriers to learning.
The whole pupil view philosophy is based on the premise that each pupil has the ability to achieve ...
“All models are wrong, but some are useful.”
(George E.P. Box 1976)
We firmly believe that the whole pupil view model has the potential to be incredibly useful in providing a methodology to guide analysis and exploitation of data for the benefit of each pupil. This GL Assessment philosophy seeks to provide a model to enable educational professionals to exploit the data available in an optimal way to improve the teaching of, and learning for each and every pupil.
... a model to enable educational professionals to exploit the data available in an optimal way ...
Triangulation of data
A critical aspect of the whole pupil view is how the different data types (ability, attainment and barriers to learning) relate to each other, i.e. how the data are triangulated and, in turn, what questions this raises.
... how the different data types [...] are triangulated and, in turn, what questions this raises.
Figure 1. This figure provides a visual overview of the four key intersections.
The data ‘sets’ become whole at their intersections where these are compared/contrasted and analysed. Figure 1 on the previous page outlines the various data intersections:
- where attainment and ability data intersect;
- where ability and barriers to learning data intersect;
- where attainment and barriers to learning data intersect;
- where all three – attainment, ability and barriers to learning – intersect.
The whole pupil view hypothesis is that optimal decision-making is based upon being fully informed.
The guiding principle is that the power within data has its use in generating questions. With this approach there is the potential of combining data. This model can support the community of experts through framing smart questions around the data combinations/ intersections provided to help schools define and develop processes that will best support their pupil outcomes.
“Our model of assessment allows us to target our teaching to meet the needs of the children, whether they are SEND, EAL or academically able. We are able to deliver a personalised programme of learning for each child based on the information derived from GL Assessment’s tests to ensure that every child is a learner every lesson.”
(Jill Wilson CBE, Headmistress, The Gleddings)
The whole pupil view hypothesis is that optimal decision-making is based upon being fully informed. As we work towards exploring this further in this document, we will first explore single data set analysis, then triangulation across two data sets and ultimately, triangulation across the all three data sets, the whole pupil view.
Teacher judgement
The guiding principle is that the power within data has its use in generating questions.
Schools, by no means, work with GL Assessment data in isolation which means that the whole pupil view always takes account of teacher judgement, which is informed by school/local data, as the starting point. Schools may already have data that support their view on ability, attainment and barriers to learning derived from other in-school assessment.
The school typically knows a great amount about each individual pupil including:
- demographics – attendance, ethnicity, gender, first language, socioeconomic mix of the school’s catchment;
- attainment – prior and current from a range of non-GL sources e.g. teacher assessment, national tests, etc.;
- expectations (targets) – projections based upon prior attainment e.g. RAISE online, Fisher Family Trust;
- attitudes – behaviour, effort, attendance etc.
Schools value this data, and monitoring and inspection regimes examine this data to inform judgements. The whole pupil view narrative begins here; it acknowledges the wealth of data that schools possess and the wider system recognises, prior to linking to benefits that can be derived from triangulating this with GL Assessment data.
Single data set analysis
A critical theme here is having data to analyse only one aspect/set of performance can, by definition, only ever provide a partial view.
This stage reflects the analysis from one set of GL Assessment data. A ‘set’ of data is defined here as either ‘ability’, ‘attainment’ or ‘barriers to learning’. It should be noted that schools, in all likelihood, will be using data from a single GL Assessment and triangulating this with a variety of ‘local’ data, for example, teachers’ assessment, attainment, effort, prior attainment, attendance, etc. In this way, schools will be using GL Assessment data in the least complex triangulation.
With single data set analysis there is room to support greater benefit – guiding exploitation and questioning, through providing a set of key questions, signposting school processes via case studies (leveraging the community of experts), and guiding improved learning by utilising this data. A critical theme here is having data to analyse only one aspect/set of performance can, by definition, only ever provide a partial view. How useful is attainment data devoid of a view on a child’s potential? How useful is ability data devoid of current attainment? The answer has to be ‘somewhat useful’ and with any partial view, decisions based on this are unlikely to be optimally informed.
Having said this, there are useful questions that can be raised with one data set to inform next steps for individuals, groups or even schools. GL Assessment reports are incredibly useful in providing data on single aspects and also, in many cases, interpreting these data to inform teaching and learning possibilities.
Attainment
If only one set of data is available to support teacher judgement, it is most likely to be attainment data as many education systems have focussed upon measuring attainment across a range of subjects and skills at different points in time, for example, termly or annually. GL Assessment provides a breadth of assessment data to support specific single aspects of attainment, for example, progress attainment data in English, maths, science and reading.
How useful is ability data devoid of current attainment?
Much can be derived from attainment data alone, this is as follows.
- Tracking and monitoring progress: it is useful to see how individuals and groups are currently performing,
“New Group Reading Test (NGRT) has proved effective in the way it is an adaptive test, focused on reading ability rather than chronological age, thus extending more capable students whilst also attending to the needs of others. Despite proving more effective with the older girls (who are more familiar with using a computer), NGRT has also been regarded as a particularly effective assessment in how both sentence completion and passage comprehension are covered in determining the child’s reading skills.”
(Melanie Charles, St Hilda’s School, Bushey)
How should the school organisation be arranged based upon findings from this data?
- Measuring progress: collecting data at different points over time is also incredibly useful and allows progress to be measured between these data collection points. Progress Test reports will provide an additional statistically-derived interpretation in terms of whether the progress is average, below or above average, helping to determine the quality of the progress made.
- Benchmarking nationally: how does my school compare to the national average?
- Comparison of groupings: within school, boys’ performance compared to girls’, students with English as an additional language compared to the main student body, etc.
These sets of attainment data then raise further questions. How are any differences explained? For example, are there any specific causes that can be identified? Some may be evident from other data sources the school may be aware of, for example, poor attendance is statistically correlated with poor performance. Where there are gaps in attainment, what should be done to address these gaps?
Ability
Many schools will derive ability data from GL Assessment only in the form of Cognitive Ability Test (CAT) data. Schools that generate CAT data are interested in understanding pupil potential to help inform expectation and set targets. A typical scenario is that schools will assess ability on entry to the secondary years. Some schools will also have a regime of assessing with CAT at a number of fixed data points to track and monitor progress, and then refine targets.
Much can be derived if only ability data is available, from identifying potential, personalising learning, through to measuring value added.
Much can be derived if only ability data is available, from identifying potential, personalising learning, through to measuring value added.
“CAT4 helps us to set ambitious but realistic attainment targets for students across the whole school. This is essential to enabling teachers to ensure that every child is achieving well within the context of holistic development.”
(Dr. Derek Cassells, Principal, Maharishi Free School)
As with attainment, ability data can be used for comparison between groups and for national benchmarking.
These sets of ability data then raise further questions. How should the school organisation be arranged based upon findings from this data? For example, to support students deemed gifted and talented? To support students with lower ability? To support students with strengths or weaknesses in the various aspects of ability assessed by CAT?
It is likely that most schools will use data from the GL Assessment CAT, measure and compare and contrast this with other locally-held data, for example, prior attainment, current attainment, perhaps based on teacher judgement, etc.
Barriers to learning
Many schools will derive data to help understand barriers to learning from a single GL Assessment test. This may help diagnose whether a student has a very specific barrier, for example, dyslexia or dyscalculia, perhaps driven by a desire to better understand poor attainment in literacy, numeracy or, indeed, across the curriculum more generally.
Analysis of barriers to learning assessments will help provide data which, in turn, raise further questions ...
Another potential barrier to learning – limiting access to most academic subjects – is the reading ability of students. Assessing reading ability in the context of barriers to learning is focussed upon diagnosis of underperformance in literacy/English and curriculum subjects where reading ability is key. In a similar way poor communication skills’ development can also be a barrier to learning, assessing and better understanding pupil’s communication skills can help professionals in unlocking this.
“It can be upsetting to discover your child needs additional help, but Wellcomm provides good evidence in a friendly, straightforward manner. It doesn’t bamboozle with technical jargon.”
(Beccie Hawes, Head of Service, Rushall’s Inclusion Advisory support team)
Many schools will also seek to understand hidden or subtle barriers to learning through using the Pupil Attitude to Self and School (PASS). It is useful as a general screener to help build a picture for each pupil, or perhaps targeted at specific pupils, where there is a specific question around underperformance, poor attendance, poor behaviour, etc.
It is unlikely that any school will use single data set information in isolation ...
As with attainment and ability, barriers to learning data can be used for comparison between groups and for national benchmarking. Analysis of barriers to learning assessments will help provide data which, in turn, raise further questions:
- Are there specific trends across the school that should be addressed?
- If so, how could school organisation be arranged based upon this data?
- What interventions will best meet the needs of individual or group of students?
- How will we know if interventions have been successful?
It is unlikely that any school will use single data set information in isolation, but will be comparing and contrasting data derived from GL Assessment with other locally generated or held data.
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