Using evidence for learning

This resource supports schools in examining the way they use the evidence they collect from their learners, such as assessment information and anecdotal notes, to inform learning.

About this resource

This resource guides schools through the various ways they might gather, work with, read, analyse, and report data. There are useful examples that explain how to make sense of the existing data you have and use it to set targets and/or goals at the individual, group, or school level.

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Using evidence for learning

Evidence is information that can be used to judge whether something is true. It is generated through the purposeful collection, organisation, and analysis of data.

Trustworthy evidence provides the information we need to engage in assessment practices that are consistent with the National Education and Learning Priorities and ensure that all ākonga:

are at the centre of decision-making about learning
have barrier-free access to great education opportunities
have access to inclusive and quality teaching.

For evidence to be trustworthy, it is essential to collect and analyse high-quality assessment information and to work with that data in ways that ensure the conclusions drawn are fair and valid. We use evidence to learn, set targets, plan next steps, and monitor progress as we seek to ensure the success and wellbeing of all ākonga.

This section supports teachers and school leaders in gathering, managing, and analysing quantitative assessment information so that it becomes evidence to inform improvement. As you engage with it, we encourage you to consider the assessment literacy and capabilities of ākonga, their whānau, and all those with whom you have learning relationships, centred on educational improvement. How might some of the information here help inform learning conversations with them?

Process for gathering and using data for learning
Gathering data
Working with data
Reading and analysing data
Using evidence for target or goal setting

Process for gathering and using data for learning

The production and use of high-quality evidence requires teachers to know and understand their students’ language, culture, identity, strengths, and learning needs. This knowledge provides a strong foundation as you work through iterative cycles of learning that incorporate the following processes:

gather student assessment information: Draw from qualitative and quantitative data sources to assemble dependable information about the progression of learning.
work with data: Accurately aggregate and present information for it to be easily understood.
read and analyse data: Interpret and evaluate information to decide on next steps to support progress.
set targets and goals: Set challenging but achievable targets for improved student progress and achievement.

At every phase of the cycle, there are opportunities to share information and build partnerships for learning.

Gathering data

Qualitative and quantitative methods are complementary. Together, they can present a clearer picture of the situation than either would alone. Often, the most accurate information is obtained when several varieties of each method are used.

Quantitative methods of assessment express data results in numbers.

Assessment tools and resources use these as you gather quantitative evidence; guidance for use is included.

Qualitative methods of assessment gather information that can’t easily be measured or translated into numbers. They may involve observations and interviews.

To make the most of achievement data to improve learning, teachers need to be aware of many other factors, including evidence that describes students’ wider learning environment and describes a school and its students, staff, and community, much of which is collected at enrolment and systematically every day. This evidence tends not to be quantifiable and should be used in conjunction with achievement and possibly other quantitative data to support teacher enquiry.

Working with data

We use the phrase "working with data" to describe what we do after we have gathered our data and need to aggregate and present it in ways that it can be easily understood.

Important considerations

Use a range of approaches

A single assessment result is a snapshot in time, so a range of approaches is necessary for a comprehensive picture of a student’s unique progress. While a result can be indicative of levels of achievement and progression, it can be affected by variables to do with timing, administration, validity, student health and well-being, and other factors.

Disaggregate data

Disaggregate data to get to the core of what is needed in the classroom. A useful expression is "mining the data". The deeper and more personalised the analysis, the better the teaching and learning goals that can be set from it.

Use data to ask and answer questions

Data becomes evidence when it is used to answer worthwhile questions. You need to be thinking about these questions throughout the process, from when you decide upon the information to gather, to when you organise it, and on to when you seek to interpret it.

Share assessment results with ākonga and whānau

Always share assessment information and results with ākonga. They are the prime owners of their assessment results and need the information to determine where they are with their learning and to set goals. Achievement data needs to be shared, discussed, and ‘owned’ by principals, middle leaders, and classroom teachers alike.

Privacy and data sharing and collection

Your school will have policies and systems for the collection and sharing of data to ensure information is available to enhance teaching and learning without compromising students’ privacy.

Names, pronouns, and gender markers

Schools are increasingly aware of the importance of respecting students’ wishes, and understand that, as far as possible, the names, pronouns, and gender recorded in their student management systems should align with student preferences. Two issues may arise, and need to be dealt with:

The process of recording an individual’s personal information may threaten their privacy, which, in turn, may threaten their safety and wellbeing. It is important to understand the legal issues around this and to have plan for situations where ākonga do not wish their prefered names, pronouns, or gender to be disclosed to their parents or whanau.
Currently, many student management systems and assessment tools lack the functionality required to meet the needs of rainbow people.

You can find information and advice in Creating rainbow-inclusive school policies and procedures (InsideOUT Kōaro, 2021).

Creating a datasheet of assessment data

The following information is intended to help you load or download data to a spreadsheet for analysis. You may want to go beyond the data reports from the digital assessment tools or SMS or analyse assessments that produce only raw data.

Quantitative data (scores) can be entered into a spreadsheet in several ways:

Downloaded from a database (from a digital assessment tool like e-asTTle or SMS) in a CSV (comma separated value) file. CSV file format is used to move tabular data between computer programs that support compatible file formats.

See Materials that come with this resource to download E-asTTle Converted Tabular Report (.xls).
Entered from other assessments (for example, teacher judgements). Results can be cut and pasted from digital tables of figures. The spreadsheet should contain all the information required for close data analysis.

If your intention is to accumulate data over time to create longitudinal data (that is, data that is collected sequentially from the same subjects over time), it’s important to record information that will make it easy to identify the ākonga and match them with their results at a later date. This information will include:

date and type of assessment
student name and number
year level, gender, and ethnicity
assessment result

Downloading data from a database may result in columns of extra unneeded information for your data storage or analysis. Remove these columns consistently across all data sets.

If the assessment tools are not digital, enter information manually, either into the SMS and downloaded, or straight into a spreadsheet. An example of data that needs to be entered manually are teacher judgements (TJs).

Cleaning, sorting, and merging data

It is important that data is 'clean'. This means that it is consistent, accurate, and complete. Ensuring the data is clean before working with it will prevent misinterpretations or having to repeat the process if you discover problems later.

Consistency

If you are downloading information from more than one source, it must be in exactly the same format. Be sure to check that the format is the same before you combine data (for example, records for more than one class). Data that shows progress over time should always be matched so that the same students are represented in both sets of figures.

Accuracy

Be alert to any anomalies, either in the numerical data or the attached demographic information. For example, a student number may be missing, a cell may be blank, or a student’s year level may be incorrect or missing.

Scan the data. Are you sure you have the right student records and the right test results? If the data is entered manually, double-check your data entry, perhaps with a partner.

Complete

Make sure that each data record is complete and that there are results for all ākonga. Where results are missing, it’s important to find out why. However, the records for these ākonga should be deleted for the analysis. This is particularly important when calculating medians and means, as empty records can skew the data.

This tutorial shows you how to clean, sort, and merge data using Excel. Making sure that data is clean before you start to work with it helps prevent misinterpretations or having to go through the process again if you discover problems further down the track.

When working with data to analyse results and draw conclusions, it is essential that the data with which you are working is "clean". This means that it is:

consistent - is all the data in the same format, and does matched data refer to the same students?
accurate - are these the right records, the right results? Does anything look out of place?
complete - are there students missing? Are there NSN numbers missing?

Be alert to any potential problems, either within the numerical data itself or the demographic information attached to it. For example, a student number may be missing, a cell may have no information, or a year level for one student may be incorrect.

Creating your own simple graphs

Graphs (also called ‘charts’) play an important role in data analysis. They are commonly used to represent relationships between achievement results for groups (for example, cohorts, classes, gender, or ethnicity) or to show progress over time. A graphic representation can make it easier to understand the relationship between sets of data.

When your data is clean, you can use it to create your own graphs. First, ensure the data is in the correct format. Then decide what you want to show on a graph.

This tutorial shows you how you can create a simple bar or line graph using Excel.

Three examples of data sets are used:

the test results for a class
the average test score for all classes within a cohort
the average attendance data for a school over one academic year.

To practise making graphs, download the data sheet above as an Excel spreadsheet.

See Materials that come with this resource to download e-asTTle converted tabular report (.xls)

Disaggregating data

Disaggregating data means looking at results or judgements by subgroups of students.

Student achievement data is often reported for whole populations (for example, cohorts, year levels, and whole classes). This is aggregate data.

It is not until the data is disaggregated that patterns, trends, and other important information are uncovered. School leaders and trustees will look at disaggregated assessment data to determine overall trends, sharing their findings with others to set school-wide goals or targets.

Data is often disaggregated by gender and ethnicity. However, for goal-setting, there is value in investigating the results of individuals or more specific groups of ākonga. Do you have questions about trends for certain individuals or groups? Do some appear to be progressing at a faster or slower rate than others? Decisions such as these will determine the most helpful way to disaggregate your data.

This short tutorial shows you how to create a graph in Excel to illustrate test results for a class, broken down by ethnicity and gender. Use the Excel spreadsheet below to practise disaggregating data using pivot tables.

See Materials that come with this resource to download Cleaned and matched writing data BOY and EOY and combined (.xls)

Reading and analysing data

We read and analyse, or ‘interpret’, data to turn it into evidence that tells us where students are in their learning and what their next steps might be. The information can then be used to answer questions, set goals, and plan for optimum teaching and learning.

For data to be transformed into useful evidence, it is essential that it be read and interpreted accurately. Preferably, this will happen in a collective process that fosters learning partnerships.

Any test is a snapshot, and the results should be considered along with a range of other evidence. It is important that test scores not be used in isolation to calculate student achievement.

The process of reading and analysing data will often throw up anomalies that need to be investigated. The investigative process can often reveal insights that suggest solutions or alternative pathways.

It is useful to have ākonga analyse their tests and graph their results over time. Their participation in creating a record of progress provides strong motivation for ākonga to set and work towards ambitious learning goals that are meaningful for them, their whānau and their community. It provides another very valuable site for engaging in ongoing learning conversations.

Using evidence for target or goal setting

Target setting is effective when it drives strategic planning across a school, class, cohort, or for an individual. It is not a compliance process.

By using the following guidelines, you can ensure target setting is directed towards improved performance.

Use qualitative and quantitative evidence about what is working well and what needs attention to inform targets.
Keep targets realistic but challenging.
Negotiate and agree on targets rather than imposing them.
Set SMART targets: specific, measurable, achievable, relevant, and time-bound.
Regularly check progress by monitoring actual performance against set targets.
Take targets seriously, but ensure that monitoring is experienced as a positive process.
Failure to meet targeted performance can prompt learning and problem-solving to improve performance.
If targets are not met, focus on what has been achieved, make a rich assessment of that, and share the learning where possible.
Ensure targets and goals align with the appropriate progressions of curricula.

Planning for improvement

Plan for improvement when you set targets. Ask these three broad questions:

What do we know that we can already do to achieve these targets?
What do we need to learn to do so that we can achieve these targets?
What will we do differently to ensure targeted improvements are made?

Lasting change comes from investment in professional learning and improved leadership and classroom practices.

Ākonga goal setting process

For most ākonga, goal setting is generally more effective in the context of next steps in learning than broad, long-term goals. Goal setting, based on evidence, is the logical next step following teacher, peer, or self-assessment.

It’s important for ākonga to develop self-assessment skills so that they can set their own goals. Goals set by the teacher do not have the same level of student ownership.

It’s also important that ākonga, along with their whānau and others with an interest in their learning, have regular opportunities to set long-term goals and monitor progress towards their achievement. Discussion on smaller steps can then be contextualised within shared understandings of the learners’ long-term learning pathway and include reflection upon how smaller steps help move ākonga towards achieving their aspirations.

Sharing assessment data with students

In these videos, teachers from Mangere Bridge School talk about sharing assessment data to set goals with students.

Elizabeth Crisp and Rosina Prasad talk about ākonga in years 1 and 2. Don Biltcliffe talks about ākonga in years 5 and 6.

Target setting at the school and classroom level

Follow these steps so that staff members at every level of the school have ownership of the targets and take responsibility for them.

Begin school-wide target setting at the classroom level, where teachers can identify individuals or groups of ākonga needing a particular focus.
Establish targets at the syndicate or department level.
Incorporate targets into school-wide targets at the leadership and board of trustees levels.

At all levels, and as appropriate, partner with whānau, Māori, and others in your community to check that targets are aligned with their priorities and aspirations.

Co-construct and establish processes to monitor against targets. For example, an expectation could be:

weekly monitoring in the classroom, using anecdotal notes, classroom observations, and learning conversations
monthly reporting to the syndicate leader or head of department
term-by-term reporting to the board of trustees.

Your school-wide assessment schedule will support the monitoring processes.

Target setting at the board level

See the following page on the Ministry of Education’s website:

Schools' planning and reporting for information on target setting
Developing your planning and reporting documents for guidelines and resources.