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Step 3: Explore data and draw conclusions
Step 3: Explore data and draw conclusions
After gathering data, you start to review the data you have collected. At this point it is important to keep your thoughts focused on what is in front of you. The data that needs to be transformed into information. Although it can be tempting to jump to raw conclusions about what you see in the data, try noting down what you see and what questions come up while looking at the data. It can help if you do this together with colleagues. Collaborative inquiry helps to establish openness and lets everyone get a better picture of the evidence. Make sure everyone gets a chance to express what they see in the data and to ask each other clarifying questions, when necessary, to get on the same page.
As you get ready to explore the evidence you have collected and to draw conclusions about the problem and what to do next, it is vital to consider how the stakeholders are going to work with and react to the evidence. Remind the leadership team and teachers that this journey is about looking at your work from a data-driven angle, to balance intuition-based decisions with data. This means that in some cases you will be able to confirm your intuition with data, but it could also mean that the data will reveal things about teaching and learning that go against the gut instincts you have been working with thus far. The key is to be open to what the data is telling you and to use collaborative work and discussion to transform the data into information that includes your school's context, the teachers who teach and the students you work with every day.
Our minds tend to jump to conclusions easily, as we are very good at finding patterns, inventing stories, and then confirming our beliefs. Data visualiation scholar and journalist Alberto Cairo (2016) calls these mind bugs. You can read more about how to avoid our own bias when exploring data and making conclusions about them in the section on 'Avoiding bias'.
In a job that can be in some ways very isolating for teachers (especially those who do not make it out of their own classrooms enough), coming together around data can be a new way to connect with colleagues, discuss things that are going well and where teachers feel they need help. Help them see that exploring evidence is an opportunity to reflect on their practice and to share ideas with their colleagues.
1. Explore data
1. Explore data
Begin with a broad overview of the evidence you have gathered: Instead of immediately focusing on minor details, look through the entire data set, as it will allow you to identify important patterns in the data and get a better understanding of your situation and context.
Look at the big picture: Consider the larger context of the data. Is this data source better for looking at the whole group? Are there any trends that have an overarching effect on teaching and learning?
Zoom in to explore subgroup differences: Consider the differences within the subgroups of the data. What unique characteristics of separate groups might have an underlying effect on the data you are reviewing? Are any factors impacting one group more than another? What might be the cause of that?
Together with your team discuss and answer questions provided in the worksheet.
2. COMPARE data SOURCES
2. COMPARE data SOURCES
Now it is time to think back to the work you did in Steps 1 and 2 to start aligning the different sources of information you have collected to draw conclusions. Remember, more often than not, one data source will not be enough to completely understand your situation and its root causes. That is why it is necessary to:
consider other data sources, such as, attendance, future education or career choice statistics, etc.,
compare your data source by finding similarities and contrasting information,
or explore one factor from your data source in depths.
Using multiple sources of data is a fundamental concept of data-driven decision making for school improvement. By looking at only one stakeholder's perspective or using only one source of data, you risk creating an incomplete review of the objective situation in your organisation. Only by analysing the situation from various points of view can you gain true understanding. For example, looking at low results on a maths test can be double checked with attendance records and reveal that students were absent for critical lessons that supported the focus of the test. Likewise, it helps to bring together various perspectives and triangulate the data (more on that in the section on 'Gathering data'). Moreover, this helps to remove biases we might have about a certain class or group (more on that in the section on 'Avoiding bias').
When comparing data sources, keep the following questions in mind:
'What do we see in the data?'
'What do we mean by that?'
'What data proves that?'
'What examples do we have to support that?'
To successfully understand the data, you are working with, you must be an active listener, working with the information in front of you, clarifying details if something is unclear, and keeping yourself and others accountable to focus on what the data is saying, not what your emotions or assumptions are saying.
Together with your team discuss and answer questions provided in the worksheet.
3. Define the root cause
3. Define the root cause
It is in this stage when you need to think back to your original hypothesis about the problem you've set out to solve and the hypothesis of the root cause of the problem. If the data confirms the hypothesis, then you are ready to go on to the next step of the cycle. However, if the data denies the hypothesis, indicating that there is a different reason for the problem, you will need to go back to test a new hypothesis by collecting new data.
Do not worry! Many studies have shown that teams going through the data-driven decision making cycle for the first time do not confirm their hypothesis at first. This is an experimental process that takes time and builds experience to become more efficient. Do not take it as a sign of failure if you do not confirm your hypothesis at first, it is a completely normal part of the cycle. Take a moment to reflect on what you have learned from the first hypothesis and data collected and use it as a starting point to explore a new hypothesis and data.
Together with your team discuss and answer questions provided in the worksheet.
4. draw conclusions
4. draw conclusions
Reflect back on the problem you are exploring and focus on what the data is telling you. At this point in the data-driven cycle, it is crucial to consider the factors that you can impact for change, and to shy away from deflecting ownership of the root causes of your problem. To ensure that your focus stays on the teaching and learning processes, draw conclusions about the problem by formulating it in two ways.
1. Student-centred problem: expressed in terms of what the student is having difficulty with (for example, 'Our students are having difficulty solving multi-step problems').
2. Problem of practice: expressed in terms of the teaching practice (for example, 'We don’t give our students enough practice with multi-step problems').
These two conclusions will be the key when thinking about taking action in the steps to follow because they will help you focus your strategies on the aspects of teaching and learning that actually need to change. This also helps to keep the bigger picture in mind - to ensure that the work we do is focused on our students and that teachers are the ones who can make the greatest impact day-to-day.
Together with your team discuss the question provided in the worksheet.
Recommendations for DATA COMMUNiCATION PLAN
Recommendations for DATA COMMUNiCATION PLAN
When planning to communicate the survey results and the conclusions it is crucial to think about the following questions:
Who needs to hear / see the information?
When is the best time to present it?
Where will we present it?
How will we present it?
What form will we use to present the data?
You can read more about communicating the survey results in this material 'Recommendations for communicating survey results'.
After drawing conclusions about the data you have gathered and explored, you can proceed to the next step of the cycle:
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