Module 7: Te Whakamahi Pūrongo (Data Driven Leadership): This module focuses on using data effectively to inform decision-making, assess progress, and drive continuous improvement.
“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
http://www.edu.gov.on.ca/eng/policyfunding/leadership/IdeasIntoActionBulletin5.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.
