Introduction
What is data analysis and why do we use it?
Quality control
The main goal of school-level data analysis is to gain insights into the overall functioning of the school, identify areas of improvement, and make informed decisions. As such, data analysis is a principal quality control activity in the quality improvement cycle.
What data do we analyse?
In schools, we generate and have access to data for a variety of purposes. These include, but are not limited to:
Student achievement: analysing academic performance data to understand how well pupils are attaining and what progress they are making..
Attendance: examining attendance data to identify patterns, address chronic absenteeism, and ensure that students are regularly present in class.
Behavioural data: analysing disciplinary incidents and behaviour-related data (suspensions, exclusions etc) to identify trends, address issues, and implement interventions to maintain a positive and inclusive school culture.
Who analyses data?
Data analysis can serve many purposes at different levels in the family. For example, teachers might use data to identify where quality teaching plus is needed to help all students to reach their academic and personal potential. Senior leaders in the school might use data to monitor students’ progress and attainment, their attendance, their behaviour and other indicators of how well the school is doing in order to inform decision making. At the trust level, data provides information that builds a picture across the family and informs strategic decisions about policy making, allocation of resources and so forth.
What can data analysis do … and what can it not?
What is data analysis and why do we use it?
Data can give vital insights into different aspects of school provision. Data helps you to ask the right questions, identifying the lines of enquiry, that will help you diagnose strengths and weaknesses. However, it is important to remember that data does not give the full picture. Data can tell you that a pattern or trend exists but it cannot tell you why it does.
Principles of effective data analysis:
Right data, right people, right time
There are many data streams available to us and these can be used in many ways by many players in the system. To navigate the myriad of possibilities, it can be helpful to follow a threequestion approach:
1. What am I trying to find out - what do I need to know?
Am I trying to identify strengths and weaknesses in outcomes from high-level data?
• Am I trying to spot where something in a process needs to improve?
• Am I looking to see if previous actions are making the desired difference?
• Etc,
2. What data will help me find this out?
• Do I need high-level data or something more granular?
• Where is that data held?
3. How should I look at the data I have chosen to give me the information I need?
• Are there any filters or comparisons that will help me?
• Do I need to drill down further? In what direction? How far?
This matrix can be helpful in identifying what to look at, how and by whom
What am I trying to find out - what do I need to know?
What data will help me find this out?
How should I look at the data I have chosen?
Analyse data with the expectation that something will happen as a result
Data analysis is not an end in itself. It is a quality control activity: it compares something to a desired measure. It raises questions and gives indicators of what is working well and what is not. Therefore, data shows the end result of things that are working well and those that are not.
Data cannot be changed by focusing on the data alone. Change in data is only achieved by improving the factors that lead to the data. Year 6, Year 11 and Year 13 outcomes are not improved by
saying, ‘Improve the outcomes.’ They will improve as a result of making the quality of teaching as good as it can be. In the same way, attendance will not improve by telling students to attend more regularly. Something has to happen to motivate them to change their attitudes towards school.
The WAT mental model of data analysis supports us in applying these principles to our analysis of data of all kinds and at all levels.
Applying the principles of effective data analysis in the quality improvement cycle.
The following case studies capture how leaders used the principles of effective data analysis to identify insights and develop lines of enquiry in the quality improvement cycle.
Case Study 1: primary
Leaders’ questions linked to the QI cycle What data was looked at Who looked at the data/ when What the data showed
1: Right data, right people, right time.
Leaders’ questions/ lines of enquiry
How are we doing in terms of KS2 outcomes?
WAT SATs analysis 2023
Overview - Attainment and Progress HT and SLT last SLT meeting of July 2023
Why are we doing as we are and not better? Are any groups of pupils attaining less well than they should?
WAT SATs analysis 2023
Pupil CharacteristicsAttainment As above
• Attainment broadly in line with national for R,W and M - and for combined.
• Improving picture over the last three years. Nothing to indicate specific weaknesses in any subject overall.
• Slightly below other WAT schools.
• No consistent differences between gender/EAL/ FSM etc in terms of attainment. Varied from subject to subject
Why are we not performing as well as other WAT schools?
What might our focus for improvement need to be?
What might our focus for improvement need to be?
Principle
Getting the best insights from data analysis
Have we looked at enough data to identify any pupils who may be underperforming?
(why are we doing as we are?)
How well are our current Y6 girls performing in maths?
(How are we doing)
WAT SATs analysis
2023 Pupil Characteristics
- Progress and Scaled Scores
As above
How well are our girls performing in maths in other year groups?
(How are we doing)
Smartgrade: current Y6 most recent mocks
As above
• Notable difference found between the APS progress and average scaled score between male and female pupils:
Maths
• Girls made a lot less progress to reach a much lower average scaled score than did boys.
• Girls scored on average 4% fewer marks than boys
• Variation between classes - in the worst scenario, girls scored 17% fewer marks.
Is this an anomaly, or do our girls underperform in maths?
Smartgrade: Ark Curriculum Plus, Years 5-1, overall results
As above Female/male difference:
• Y5 - 6%
• Y4 - 18%
• Y3 - 3%
• Y2 - 6%
• Y1 - 4%
We potentially have an issue with our girls in maths. Is this to do with the quality of teaching in Year 6 or is it a more widespread concern?
Girls are performing less well than boys in all year groups for whom we have data.
Is it all girls? Some? Who? Why?
Getting the best insights from data analysis
Which girls are underperforming in maths in each year?
(How are we doing)
Why are these particular girls falling behind in maths?
(why are we doing as we are?)
(What needs to change)
Why are these particular girls falling behind in maths?
(why are we doing as we are?)
(What needs to change)
WAT-produced PPM data
Maths leader
Smartgrade: Ark
Curriculum Plus, Years
5-1, QLA
Speaking to the girls in each class who are falling behind:
• How do you find maths lessons?
• What do you find most difficult?
• How could your teacher help you to feel more confident in maths?
Maths leader
• Most girls in each year group sustaining attainment from prior scores but not moving from WTS to EXs+ or EX to GD.
• Several identified as not sustaining previous attainment.
• Weakest progress seen for girls overall in Y4 and in 1 class in Y6
Why are the particular girls in each class not sustaining or building on prior attainment?
What are the more general issues in the two classes with the weakest overall progress?
• QLA showed these girls failed to score marks due to basic calculation errors in questions that require application of knowledge. They lack fluency - they cannot automatically recall basic number facts from number bonds in Y1 to multiplication facts and the commutative relationship between number facts.
Maths leader and T&L lead Responses ranged but showed that, overall, the girls:
• don’t enjoy maths lessons
• don’t feel confident in maths
• don’t like to answer questions because they are afraid that they will get it wrong
In the weaker Y6 class, the girls said that the teacher focuses mostly on the boys because the boys always put their hands up and call out the answers,
In the weaker Y4 class, the girls said that the teachers does not explain clearly what they have to do. They are often confused and do not understand how to complete the questions.
Why has this happened?
What do we need to do:
• Immediately - how are we going to ensure these girls catch up?
• For the long termhow are we going to prevent the issue in future?
What needs to happen?
Principle 2: Something will happen as a result of data analysis.
Maths lead identified precisely which number facts each girl needs to practise. Intervention sessions were set up to provide daily practice.
Teachers were asked to make these girls a target group to check for understanding during lessons.
Maths lead delivered CPL to all teachers about how to spot lack of fluency and what strategies can give extra practice in lessons.
Maths lead gave bespoke support to Y4 teacher to strengthen their subject knowledge. Also gave deliberate practice in maths-specific modelling.
T&L lead gave instructional coaching to Y6 teacher in ensuring that the use of the engagement strategies benefits all pupils equally.
Is this making the desired difference?
What next?
Ongoing assessments from daily sessions.
Next round of mock and Ark assessments
SH, SLT, maths lead The difference reduced in most classes but was still wider than SLT wanted. They agreed to adjust lesson timings slightly to build in dedicated time at the start of each maths lesson across the school for specific focus on basic number fact retrieval.
