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Module 7

Data Driven Leadership

“He aha te take? He aha te pūtake?”

“What is the cause? What is the root cause?”

Module Objectives:

  • Understand the importance of data-driven decision-making in education.
  • Identify and collect relevant data to inform school improvement initiatives.
  • Analyse and interpret data effectively to identify trends, patterns, and areas for improvement.
  • Use data to inform and evaluate school programmes and initiatives.
  • Communicate data effectively to stakeholders, including teachers, students, parents, and the wider community.
  • Develop a data-driven improvement plan for a specific area of school focus.

 

Data are crucial for improving student achievement. By revealing gaps in student learning and instructional practices, they guide teachers and leaders in identifying areas for improvement and tailoring instruction to meet individual student needs.

However, data alone do not provide solutions. They serve as a valuable tool for understanding student learning and informing decision-making. Interpreting data is paramount; it involves uncovering the ‘story behind the numbers’ by identifying patterns and relationships. This process requires ongoing analysis, reflection, and the collection of further evidence to refine understanding and inform continuous improvement.

Types of Data:

  • Demographic data: Information about students, staff, and the school community.
  • Student achievement data: Standardised tests, classroom assessments, and student work samples.
  • Perceptions data: Information gathered through surveys, questionnaires, observations, and student voice.
  • School processes data: Information about programs, classroom practices, and assessment strategies.

When gathering data, focus on relevant information that serves a specific purpose. Avoid collecting excessive data, which can be time-consuming and difficult to analyse.

While student achievement data provides valuable information about outcomes, it doesn’t explain the underlying causes. To understand these, utilise formative assessment data, classroom observations, student voice, and other relevant sources.

Analysing Data:

Start by posing a specific question about your data, focusing on differences, gaps, or the impact of teaching practices. Look for unexpected findings and identify patterns, trends, and categories.

Avoid jumping to conclusions; explore the data deeply, considering multiple perspectives and questioning your assumptions.

Evaluate data quality using the 4 Cs: Completeness, Consistency, Comparison, and Concealed information.

Create a concise data overview and share it with colleagues to gain diverse perspectives.

Generate inferences and potential explanations, remembering that correlation does not equal causation.

Develop a range of data stories to identify areas for further investigation.

Recognise that data may not tell the whole story and that further data collection may be necessary to confirm findings.

 

Resources:

https://research.acer.edu.au/cgi/viewcontent.cgi?article=1317&context=research_conference

https://education.nsw.gov.au/about-us/education-data-and-research/cese/publications/research-reports/5-essentials-for-effective-evaluation 

https://cdn.auckland.ac.nz/assets/education/about/schools/tchldv/docs/Using%20Evidence%20in%20the%20Classroom%20for%20Professional%20Learning.pdf 

 

Task: Developing a Data-Driven Improvement Plan

  • Select a specific area of school focus (e.g., literacy, numeracy, student well-being).
  • Identify relevant data sources and collect the necessary data.
  • Analyse the data and identify key trends, patterns, and areas for improvement.
  • Develop a data-driven improvement plan that outlines specific goals, strategies, and action steps.
  • Post your data-driven improvement plan on the online forum for peer feedback and discussion.

 

Assessment:

  • Completion of all readings.
  • Participation in the online forum discussion.
  • Development and submission of a data-driven improvement plan.
  • Demonstration of the ability to analyse and interpret data effectively.

 

53 Responses

  3. Data-driven decision making is not a new concept for me. As a timetabler in a large (1850+) boys’ school, I use data frequently to make recommendations about enrolments, option choices, staffing, class sizes, rooming and the many other variables that determine whether a timetable will actually function. I also teach Year 13 Statistics, so the ideas around patterns, inference, correlation and questioning assumptions are already familiar territory.

    What I did take from this module was a useful reminder that data is only the starting point. The numbers can tell us what is happening, but they do not necessarily tell us why. That requires us to look for the story behind the data and, importantly, be prepared to have our assumptions challenged.

    A few years ago, I tracked the numbers in a particular subject over three years and noticed something counter-intuitive. Year 11 had strong numbers, enough to comfortably run a class, but Year 12 numbers had dropped to a level where the class arguably should not run. Then, in Year 13, the numbers picked back up to roughly the Year 11 level. This was unusual, as the normal pattern is for a subject to start high in Year 11 and gradually taper off through to Year 13. The data told me what was happening, but not why. Further investigation revealed that there was an unresolved issue between Mr ZZ and the HOD, relating to Mr ZZ being passed over for a TiC role. The resulting tension was influencing student subject choices in a way that was completely invisible in the data.

    There is certainly comfort in data and numbers, but they are only a tool, not a reason. The trick for a good leader is to use the data as a catalyst for conversation and investigation, which in turn paints a better picture of the situation. Effective leadership with data is therefore less about collecting more of it and more about asking better questions of the data we already have.

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    1. Heya Kelly, I really enjoyed reading your plan. Super relevant!
      Working in the NHS, I can see real value in creating a system to better understand the connection between student engagement and wellbeing in our setting. I agree with your other factors also potentially having an impact.
      We all work in different contexts, but having the same kind of framework for examining student engagement, wellbeing, transition outcomes, and programme effectiveness would help Units to make stronger decisions and learn from one another’s successes. It could also support more meaningful conversations about what is making the biggest difference for our young people.
      Your Data-Driven Improvement Plan feels like a practical and purposeful improvement plan that keeps the focus on using evidence to improve outcomes for students and creating opportunities for Units to continuously reflect, adapt, and grow.

    1. Kia ora Edward, this looks really interesting and well considered and will be incredibly valuable data to unpack. It would be interesting to collect this information across the school also to explore any regional differences and patterns.

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    2. Super interesting! I’d be keen to do something similar in our unit. Agree with Tas that it would be cool to apply across the school and see what similarities/differences came out – and how that informed our practice moving forward.

  15. Kia ora everyone,
    As a team (Lizzie, Naomi and I) we analysed our Numeracy Data (PAT, SMART) collected for the BOD mid-year report. We noticed several interesting trends and patterns that back up research around gender and overall learning gaps. We found the process of analysing collaboratively really beneficial and insightful as we all brought different strengths and viewpoints to the activity. Here is our Data Driven Improvement Plan:

    2. Hi Annami, I chose to do a similar inquiry. Being the first time we have used SMART testing across all three core curriculum areas it was really interesting to see what our biggest needs were. Funnily enough it was not what we were expecting prior to the analysing of the data. It has been a really helpful process to go through. Being able to share my data analysis with my team of colleagues was a highlight as it meant that we could collectively work out our next steps and action plan to help move those target students identified. Hopefully like us you will retest in Term 4 and be pleasantly surprised with the progress.

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    1. Hi Leonie
      Your action steps and strategic initiatives are considered and well planned.
      You short/medium/long term evaluations will allow for continuous evaluative monitoring.
      I hope your theory of change does encourage, develop and build teacher capacity.

  17. This inquiry started because I had a nagging feeling, as well as some annecdotal proof, that some ākonga who were struggling with attendance and engagement weren’t always on our radar quick enough. Sometimes we’d get to the end of a term, or later, and realise, “Hang on, I think I’ve only seen this ākonga a couple of times all term or not at all.” So at the start of the year I introduced a simple attendance tracking system.

    Looking at two terms’ worth of data, notes, and our team discussions, I’ve realised the most useful thing about the system isn’t actually the attendance data itself. It’s that it helps make ākonga visible. It gives us a reason to stop, notice when someone isn’t engaging with their plan, and have a conversation about what might help.The data also challenged one of my own assumptions. I found myself wondering whether a genuine health condition sometimes led us to accept low attendance more readily. But every ākonga at NHS has a genuine health reason for being here. The question isn’t whether the barrier is real. The question is whether we’re still looking for ways to help that ākonga experience success despite the barrier.

    One of my biggest learnings has been the difference between equality and equity. Success isn’t about everyone attending the same number of hours or following the same pathway back to school. Some ākonga might be increasing their attendance at NHS, some might be spending more time at their enrolling school, and others might be engaging in different ways altogether. What matters is that each ākonga has the opportunity to experience success in their own plan and make progress towards their next steps.

    This process helps us kaiako to notice concerns earlier, ask better questions, and keep a shared responsibility for supporting every ākonga.
    Here is the link to my plan: https://docs.google.com/presentation/d/1WH6nClCYW-pbqCPPUzA_wvxGxNeGba818IbZxSQXM3U/edit?usp=sharing

    1. Hi Maaike
      So important to listen to those nagging feelings – often leads us to unexpected discoveries.
      Your plan was comprehensive and informative.
      I know that NHS have done a lot of work on engagement and attendance, and I love your learnings around opportunity to experience success for our ākonga in their own plan.

    2. Kia ora Maaike – Your plan and what sits behind it aligns with is the genuine commitment to ensuring that the ākonga’s needs are being met, at the heart of what we do at NHS and so important to understand. I also appreciate the notion of the collective responsibility of the team to act as challenges arise, rather than waiting till we review ILP.

    1. Hi Duane,
      I had a look at your report because I was interested in your use of Dibbles, how did you find the Dibbles testing time-wise? I know each individual test can be reasonably quick but when you have several tests to get through do you find the time it takes comparable to giving a PROBE? I teach Year 4 – 5 and find PROBES help identify gaps in critical analysis such as inferencing, and other deeper thinking skills which most structured literacy programs do not address – what are your thoughts?

  20. Kia ora everyone,
    A focus for my team has been around improving student confidence to attempt new learning without the ‘This is too hard, I quit!’ we were getting.
    I used this Module as an opportunity to write up what we are doing as a more formalised Data-Driven plan, as a change from our more usual curriculum data-driven focus. Here is the link to the plan;
    https://docs.google.com/document/d/1kJMrH0MBFQ8hh8URpNaXnK540bUt6i3-5BzpdzZR3X0/edit?usp=sharing

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    2. Hi Murray,
      I really liked your topic for exploration and analysis. Motivation is a tricky one, especially for learners with needs. A student survey is a great qualitative data gathering tool – I had students in my Year 4 – 5 class, last year, form small groups and make a poster about the type of learning that they would like more of in the classroom to engage them further in their learning journey. I was surprised that some wrote that they would like to do more speed writing, maths worksheets with a buddy, more addition using outdoor games – a big theme was movement when learning – I studied Montessori Early Years and that particular request really resonated with me. Cheers Leonie

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    1. A very thorough analysis and detailed plan. Clear to see a lot of time and thought has gone into compiling it. My team has also been frustrated by the heavy impact of reading and writing skills on math assessment outcomes.

    1. Thank you, Jess, for sharing your Data-Driven Improvement Plan. I really liked how you differentiated between mathematics learning needs and ESOL language needs, and then linked those findings to practical support that could make a real difference for your learners 🙂

  26. Kia ora koutou
    Please follow the link to my data-driven improvement plan:
    https://docs.google.com/document/d/1HMRrsVVx8s0oDk8ZK8vQjPMYP6m1F-tGwq3rJ-f61Ms/edit?usp=sharing
    My improvement plan centers around my team of kaiako, rather than ākonga. My aim was to work on a plan that was relevant within my leadership position. Relating to our module 7 reading ‘5 essentials for effective evaluation’ I am aiming to embed evaluative thinking into an area of concern I see in my team, making a plan to solve it, and measure the success of the plan. The theory is that this improvement plan will have a trickle-down effect to improve student achievement.
    My data will be gathered over the next 10-week period, so data has not been evaluated as yet.
    I may have deviated off the set task – if so my kaiārahi will be able to let me know

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    1. Nic I am impressed looking at your improvent plan, very relationship focused within your team. Clear indicators on what the challenge is, and I can hear (read?) so much as the kaupapa of ‘relational trust’ we have been working on within the NHS the last few years – it is really an amalgamation of learning (I have) been done in the past years, with a strong focus on whanaungatanga, manaakitanga and creating the conditions for people to feel heard, valued and connected.When you suggest the ‘temperature question’ – I wonder if this could become useful baseline data at the beginning of the process. While it sits within a conversation rather than a formal survey, it could still provide a measurable indicator that you revisit at the end of the 10 weeks to see whether there has been any shift in how people perceive connection, belonging, trust or feeling heard.

      What I like about this approach is that it creates space to listen first. It will be interesting to see what emerges from those conversations and how that might shape your thinking as the term progresses.

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      1. Thanks, Maaike for your thoughtful feedback. A great suggestion re the baseline data gathered from the ‘temperature’ question – this is exactly what I had planned. So far, I have held 4 learning conversations, and it is fascinating what individual team members have shared.

    2. Nic, thank you so much for sharing your document. It made for really interesting reading and immediately prompted me to think about an aspect within my own team that I am keen to collect data on, rather than relying solely on my observations.

      I am interested in exploring staff contributions within our weekly staff hui. My hunch is that it is often the same voices that are heard, but I don’t have any actual data to confirm whether this is the case. This feels like a perfect opportunity to gather some baseline data over time and develop a clearer picture of participation across the team.

      Once I have collected and analysed the data, I am hoping it will help inform my thinking about how we structure and facilitate staff hui going forward to ensure they reflect our NHS values and create opportunities for all voices to be heard.

      Thank you again for sharing your inquiry. It has definitely sparked some thinking about how I can use a data-driven approach to explore team culture and psychological safety within my own leadership context.

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      1. Thanks, Robyn, for your thoughts and feedback.
        This is a great opportunity to gather some data, and drive data-informed decision making within our teams.
        I love the focus for your mahi, and the use of data to confirm or change your perspective.
        Our team culture is pivotal, good luck with your research.

    3. Thats a great theme for qualitative data collection Nic – I like how you have recognised the whispers of a potential problem within your team and are proactively trying to solve them. I especially like the coffee catch – up idea so much better than a formal meeting or an impersonal survey! Cheers Leonie.

    1. Tēnā koe Arima, Thank you for sharing this data. It made me reflect on our learners at NHS across Years 1-13. I wonder if we would find similar results if we asked our students what feedback is and how it helps their learning. I’m also thinking about this in relation to our explicit teaching around the CAAs. Students receive feedback from their NHS teachers, but they also receive a form of feedback through CAA marking once they have completed an assessment. It would be interesting to explore whether students recognise that information from the CAA markers as feedback and whether they can explain how they use both sources of feedback to improve their learning and achievement. This could provide some valuable insights into student understanding of feedback and learner agency.

      Ngā mihi nui

      Robyn

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