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
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.
25 Responses
How can we more positively impact the wellbeing of our students at school?
I chose this as being Dean of year 10 I can directly influence this cohort in my approaches and the mentors who are in my ropu. We also have the opportunity within our health classes to deliver content directly to these students based on the needs that arise.
Students completed the W@S survey and at Year 10 we had 54 responses this is a 77% response rate (54 out of 70 students).
The area from this survey I am going to drill down into is
Prosocial student culture (data below)
Q1 – students treat each other with respect (15 responses disagree, 4 responses strongly disagree)
Q2. – students treat teachers with respect (6 disagree, 3 strongly disagree)
Q3 – students always stand up for others if someone is being mean to them ( 18 strong disagree, 5 strongly disagree)
Q4 – students include others who are being left out or ignored (10 responses strongly disagree, 3 strongly disagree)
Students obviously know what respect is and how to treat others as they are capable of doing this towards teachers but greater disrespect is shown towards each other.
Specific Goals – To improve the student culture responses in terms of the prosocial student culture questions in the W@S survey section.
Strategies –
1. Homeroom Mentor-Led Activities
Leverage homeroom time as a platform for building respectful behaviors and assertive communication skills through:
Targeted micro-lessons: Short, focused activities (5–10 mins) involving role-plays, short videos, or guided discussions on themes such as:
Respecting differences
Standing up respectfully (assertive vs. aggressive)
Listening and responding with empathy
Weekly Respect Challenge: Each homeroom sets a small goal (e.g., “No interruptions during discussions this week”).
2. Junior Assemblies: Student-Led Messaging
Year 10 homerooms take turns to lead Junior Assemblies, delivering core messages about respect through:
Skits and short drama pieces
Multimedia presentations (videos, spoken word, animation)
Real-life examples or storytelling from students
Focus: Respect in action — toward peers, staff, the environment, and school property.
3. Deans Time: ‘Respect Awards’
Transition existing student recognition system to a Respect Award, presented weekly or fortnightly.
Homeroom mentors nominate students, with a short written description of how the student demonstrated respect — to:
Peers (e.g., inclusive behavior, kindness)
Staff (e.g., polite communication, cooperation)
Environment or equipment (e.g., tidiness, responsibility)
4. Health Lessons
Design and deliver direct teaching lessons on the concept of respect, emphasizing its definition, significance, and practical application in daily interactions with peers and teachers. These lessons should help students understand why respect matters and how to demonstrate it effectively in school and beyond.
Interesting survey Annette. I’ve sometimes gone further with classes and asked them to sit as if for an exam and then questioned them on who isn’t getting a fair go and who is responsible for that. It’s really powerful when you can say to a student that more than half the girls in the class have identified you as a bully. What would you say about that? There are obviously at least 3 girls who are quite unhappy. It would be helpful to know who they are.
Are We Making an Impact on Student Achievement in the Junior School?
Context
Over the past few years, we have implemented several changes in how we teach and measure student achievement in the junior school. The question is: Are these changes making a difference?
Data Analysis (EOY Exam Grades – 4-Year Trend, Y9 to Y10)
• Trend 1: There is a significant drop in grades from Year 9 to Year 10.
• Trend 2: Māori students are overrepresented in the NA–A grade range (lower achievement band).
Desired Outcomes
1. Maintain achievement levels from Year 9 to Year 10 (minimise the drop in grades).
2. Improve Māori student performance so their achievement is in line with the overall cohort.
Plan
Stage 1 – Strengthen Monitoring & Feedback
• Continue using e-asTTle to provide regular, specific feedback to students and whānau on performance and progress.
• Begin using e-asTTle data proactively to identify and target key learning areas.
• Collect and analyse student voice to understand learning needs, barriers, and motivation.
Stage 2 – Targeted Teaching & Intervention
• Use data to pinpoint areas of weakness in literacy, numeracy, and subject-specific skills.
• Implement targeted interventions, including small group support, differentiated resources, and culturally responsive strategies for Māori learners.
Measuring Impact
• Compare Y9–Y10 grade progression year-on-year.
• Track Māori student achievement and progress against overall cohort trends.
• Monitor student engagement and confidence through regular voice surveys.
• Also keep monitoring sizes and number of students taking Mathematics in the senior school
• Numeracy % pass results.
I found this quite a hard task. Started with something different which just lead me down a rabbit
hole with no real perpose. We had started using easstle a few years ago as we like the feedback it gives us and students really respond to seeing their progress rather than just been labelled eg with a stanine.
You have developed an excellent action plan. You should have a real soft spot for data as a Maths specialist Catherine. Without data everything is assumptions and anecdotes. I feel this is something you could really bet your teeth into for the benefit of all of your students.
Yes I have learned to enjoy statistics was not my favourite part of the curriculum. I do know it has to play a big role in how we deliver our courses. And I do report on out department statistics every year in my Annual report but have felt it just a tickbox excercise and with this will now put some more effort into what I am looking at. At our department meeting I was good to hear that those teaching Y9 and y10 are using the data to highlight areas they need to concentrate on. It will be shame to see e assttle go at the end of the year so hopefully whatever comes into replace it will be just has helpful.
NCEA Literacy and Numeracy at CHBC
The arrival of the NCEA Literacy and Numeracy assessments in 2023 was full of unknowns.
I recall my first class to take the numeracy assessment – I was adamant that at least half of the class would have passed the standard, but this was a huge overestimate.
When we drilled down into the feedback provided by NCEA it became apparent that many were close to achieving but were falling on the ability to reason their responses.
In the last year, we have made more use of data from these assessments, using Kāhui Ako time to analyse numeracy and literacy results as well as to provide targeted intervention for groups of students in Year 11. Choosing who to target for these groups has been a challenge – we currently select students who are close to achieving but just need that extra nudge to get over the line. However, this does cause an equity issue for those who are far off achieving the standards. Unfortunately, we are limited due to staff availability but we would like to explore other options for offering targeted intervention, particularly with the end of Kāhui Ako next year (I had to get that in!).
Data driven improvement plan
This year we have produced a plan to help raise the pass rate of literacy and numeracy results. This is quite a detailed document but here are the headlines:
1) Improve teaching so that Kaiako use universal strategies which help ALL students to understand the work they are learning. This includes embedding recall activities in their practice as well as techniques that raise engagement (eg. relevant contexts, use of mini-whiteboards)
2) Provide PLD to all staff so that they are more knowledgeable to of the requirements for literacy and numeracy and so that Kaiako are equipped with simple strategies to raise the progress of literacy and numeracy across the curriculum
3) Use e-asTTle data as well as Kaiako observations and conversations with parents to determine who is ‘ready’ to sit the literacy and numeracy assessments in Year 10 – this is a result of some case studies/student voice which revealed that some students have given up after not achieving 3 times in a row
4) Use NCEA feedback data as well as Kaiako observations to determine who would benefit from targeted intervention
5) Provide specific classes for Year 12 and 13 students who have yet to achieve numeracy and literacy
6) Provide ‘booster classes’ for students sitting the assessments – these are 1 hour intensive workshops delivered immediately before the assessments. These are larger classes than the targeted intervention groups but are also selected using the NCEA feedback data
I am a firm believer in celebrating progress over attainment. Naturally, the students see if they have achieved the literacy and numeracy standards or not and make judgments on their abilities based on these results. It has been worthwhile sharing the feedback from NCEA to see how students have faired compared to their previous attempt – many are pleased to know that they have improved even though they haven’t achieved…yet.
I am aware that there are multiple reasons which determine how well ākonga achieve. Our most recent set of NCEA literacy and numeracy results for the current Year 10s was the highest we have had out of the three years. Whether this is attributed to the hard work of the college Kaiako, the effects of structured literacy and improved maths teaching in recent years at the local primary schools, the fact that the cohort of students have better metacognition and self-efficacy, a combination of all the above…who knows?! Either way, I do believe that a data driven model can be a positive approach providing that Kaiako are aware of the multiple underlying issues that determine progress.
What an excellent use of data Sam. Even more exciting is the fact that you have seen such improvements. I don’t blame you for slipping in the reference to the demise of Kāhui Ako as that has obviously been a significant factor in the improvements you have seen. I was also impressed when I spoke to you last about the Maths PD you were providing to the teachers in your cluster which will also produce dividends.
Thanks Geraldine, it is nice to see hard work paying off even though it might be small gains sometimes. Interesting with the news this week that the CAAs are likely to be phased out within a few years anyway!
Maths at Mangapapa
For a while now, Mathematics has been on the back burner at our school. Structured literacy has been the focus of our PD for 5 years now and across that time, our mathematics data has become of increasing concern.
With the new curriculum refresh and the signalling of specific mathematics resources from the MoE, we made the decision to bring mathematics back to the forefront. Our Year 4-6 classes trialled the use of PR1ME Mathematics (knowing it had come from Singapore where maths results are the best in the world). We also desperately needed a clear scope and sequence for our maths teaching. Our Year 1-3’s began using a scope and sequence maths resource called Structured Maths Approach, developed by NZ Teacher, Jordan Priestley. Our Year 4-6 teachers found the use of PR1ME challenging, particularly with the literacy skills required to access the learning and workbooks. Our Year 1-3 teachers were enjoying the very explicit, user friendly SMA scope and sequence. Some of our Year 4-6 teachers decided to give it a go too, knowing how much our Year 1-3 teachers were enjoying it, plus the huge improvement they were seeing in their learner’s mathematical understanding (particularly number knowledge and place value).
Data Analysis: Key Trends and Patterns from 2024 Data
Overall Achievement
Only 54% of students are at the expected curriculum level.
Consistent performance across gender (Boys: 54%, Girls: 54%).
Lower achievement for Māori students overall (50%):
Māori Boys: 49%
Māori Girls: 51%
Year 4 – 50% at expected level (50% below expected level)
Year 2 – 41% at expected level (59% below expected level)
Year 1 – 48% at expected level (52% below expected level)
Trends Identified
– Significant drop in achievement from Year 3 (62%) to Year 2 (41%) and Year 1 (48%).
– Mathematics achievement for our Māori boys is a concern.
– Improvements are observed in classes trialling structured maths approaches (more so SMA rather than PR1ME).
– Teachers and leaders have noted increased explicit teaching happening in our classrooms compared to previous years.
Goal 1:
To raise student achievement in mathematics particularly in the early years.
To lift Year 1 Achievement Data from 48% to 60% in 2025 and Year 2 Data from 41% to 55% in 2025.
Strategies:
Maths to be taught daily from day one at school, following an ‘I do, we do, you do’ model with gradual release of responsibility.
Review daily based on previous learning to give more opportunity for learners to build confidence in mathematics.
Data driven learning plans based on needs across the school and giving teachers time to do this (coaching to be given where necessary).
Frequent and ongoing assessment (both formative and summative) to show a record of student progress
Introduce a digital tracking system for the new curriculum so learners requiring extra support can be picked up faster.
Goal 2:
To build teacher capacity in Mathematics.
To get 100% of teachers following the SMA Scope and Sequence and using assessment to drive learning.
Strategies:
Full staff PD for new curriculum.
Full staff PD with Jordan Priestley (SMA)
Build a better understanding of how the CPA (Concrete, Pictorial/Abstract) process allows for a deeper understanding of mathematical concepts – with lots of modelling.
Coaching and mentoring for all staff – with regular check-ins.
Observations with feedback/feedforward to allow teachers to enhance their personal practice.
Goal 3:
To implement Mathematics Support/Intervention for Tier 2/3 learners.
To have specific learning plans for Tier 2 and Tier 3 learners (with a focus on Year 1/Year 2 and Year 4.)
Strategies:
ALiM intervention with a particular focus on building number knowledge.
Use maths leaders as intervention teachers for 1-2 learning blocks a day.
Extra coaching/mentoring for teachers to ensure they are using lots of materials to support Tier 2/Tier 3 learners.
Ensure cohesion between intervention teaching and classroom teaching.
Great work Steph. Yes the focus has been so much on literacy that it would be a temptation to concentrate on this to the detriment of other areas. The year 3 results show quite clearly that the teaching is working! If you could plot that in a graph you would see quite a steep angle. Your data gathering means you now know where to aim that extra support and without that you’d be flying blind.
It’s all so interesting isn’t it.
Data-Driven Improvement Plan – Literacy Focus
At Mangapapa School, we’ve chosen literacy as our key area of focus. Over the past four years, we’ve worked hard to implement a school-wide Structured Literacy approach. Our professional learning has focused on understanding how children learn to read and spell, and using this knowledge to improve our teaching practice.
To help guide our next steps, we’ve looked closely at a range of data. This includes DIBELS assessments, writing samples, reading and writing achievement against curriculum levels, and what we’ve noticed as teachers in the classroom. We’ve also looked at data about students who need extra learning support and what kind of help they are getting.
From this data, we noticed a few important patterns. Firstly, while teacher capability has grown a lot and students are engaged in their learning, overall literacy achievement is still a concern—especially for our Māori students and Māori boys. This continues to be a challenge we are determined to improve. On a more positive note, we’ve started to see some good shifts in our younger learners. In 2023, only 9% of our Year 1 students were reading at the expected curriculum level. In 2024, after introducing a structured Year 1 Scope and Sequence and new teaching supports, that number rose to 34%. Year 2 achievement also jumped from 14% in 2023 to 48% in 2024. These improvements give us hope that we are on the right track.
However, we also noticed a gap. For example, 56% of our Year 1s were “on track” according to DIBELS, but only 34% were meeting curriculum level expectations. This shows us that while foundational skills are improving, students may still need more time and support with reading actual texts. It also tells us we need to make sure we’re using both types of data to guide our teaching decisions.
We’ve also turned our attention to writing. For a long time, our writing programme focused mainly on handwriting and spelling. In the second half of 2024, we started working with Dr Helen Walls and Dr Chrissie Braid to strengthen our writing teaching—especially around helping students come up with their own ideas and write in full sentences. We’ve already seen some promising results in our writing moderation and want to build on this.
We know that many of our students are also being identified as needing extra help through Tier 2 or Tier 3 intervention. This has made it even more important to strengthen our everyday classroom teaching (Tier 1), so that all students are supported to succeed, and our intervention efforts are well targeted.
Below is my data-driven improvement plan that outlines 4 specific goals to shift Student Achievement, strategies and action steps to achieve these goals and make improvements to our literacy data.
Goal 1: Accelerate Early Literacy Progress
Targets:Increase Year 1 students at or above expected level from 34% (2024) to 55% (2025) and Year 2 students from 48% (2024) to 65% (2025) using revised curriculum expectations.
Strategies:
Continue implementation of Year 1 Scope and Sequence Pacing Guide for The Code
Strengthen in-class support through targeted coaching and mentoring (ongoing)
Increase access to authentic texts for Year 1 and 2 students
Monitor progress using DIBELS and running records twice per term
Track and analyse composite scores vs curriculum levels for clearer intervention triggers
Goal 2: Strengthen Tier 1 Practice & Data Use
Target: Ensure 100% of teachers use DIBELS, writing moderation, and running records to drive weekly planning.
Strategies:
Build teacher capability to analyse and respond to DIBELS and writing data (PLD)
Implement a consistent learning conversation process fortnightly
Develop and roll out a responsive planning template for use school-wide
Use walkthroughs and coaching cycles to monitor responsive teaching practice
Goal 3: Improve Writing Achievement
Target: Increase % of students writing at expected level by 15% school-wide by end of 2025.
Strategies:
Implement structured writing teaching sequences across Year 1-6
Use PLD with Dr Helen Walls & Dr Chrissie Braid to develop teacher practice (ongoing)
Introduce moderation checkpoints each term using shared success criteria
Increase student talk and idea generation in writing blocks (monitored via walkthroughs)
Goal 4: Strengthen Tier 2 and Tier 3 Support Systems
Target: All identified students to have personalised intervention plans
Strategies:
Complete formal assessments for all identified students early
Allocate staffing for small group/1:1 intervention blocks each day
Align intervention content with classroom instruction for coherence
Monitor progress fortnightly and adapt as needed
Great analysis of the literacy issue Kylee. Given that all your PD has been focused on this area I should think you will get ‘cut through’ because even as students transition through the school they will be encountering the same approach. It is great that you already have measurable success which is very encouraging and provides strong evidence that you are on the right track.
Crazy when you see it written like this you realise how far we have actually come as a school with our literacy pedagogy and practice – still a long way to go though. Also, it’s a big yes to small group intervention for me!
Instead of re-inventing the wheel, I am sharing a data-driven plan that has been in action since I became HOD of Languages at my school in 2022. When I took on the HOD role there had been decline in students opting to continue their language option into the senior years. So, one of my key focuses in my first year was to identify what changes needed to be made to increase senior student retention rates.
Specific area of focus
Student engagement and retention into senior Language options (Japanese, French & German)
Relevant data sources
– Internal and External Assessment participation, completion and grades gained (N A M or E)
– Student voice surveys
– Enrolment/retention tracking
Analysis of data, key trends and areas for improvement
Internal assessment data
– Showed higher rates of Merit and Excellence grades when compared to external assessment results
– Assessed writing and speaking skills only
External assessment data
– Showed lower rates of achievement – more Not Achieved or Not Attempted results than internal assessment results
– Majority of grades fell within Achieved then a few Merits and very little Excellence
Student voice surveys
Students were asked to rank their own:
– Enjoyment of the course
– Effort
– Contribution
– Achievement
– Ability
To rank the efficacy of tasks related to the four key skills areas:
– Listening
– Reading
– Writing
– Speaking
To note what aspects of the course they find:
– most enjoyable and why
– most difficult and why
To rank which of the four skill areas (listening, reading, writing and speaking) they have had effective teaching in for development
And lastly, why or why not, they will be continuing in their language option.
Senior class enrolment numbers from previous and current years
– There had been a noticeable drop in in senior language option enrolments between 2019 and 2022
Develop a data-driven improvement plan
– Changes were made to both junior and senior programmes. In particular, a reduction in the number of formal assessments in the junior programmes (over-assessment was a trend in student feedback)
– More ‘fun’ was incorporated into programmes – e.g. projects, inquiries, peer feedback tasks, cultural experiences
– More speaking in target language opportunities were introduced into units of content in the senior programmes (another strong trend from student surveys)
– Use of digital and online resources to give variety and modernise the delivery of content e.g. online speaking tools, online language programmes such as Education Perfect
– Changes were made to the internal and external assessments students were entered into in the senior school. Students could also choose which internal assessment they completed to assess their spoken language skills.
Results
2023
– An extra Yr 10 class for x2 of our language options due to more students selecting the subject for the following year
– Increased number of students choosing to take their language option into a senior NCEA level for x2 of our language options
– Improved NCEA results across the board
2024
– Maintained x2 Yr 10 classes for the same x2 language options
– Increased retention of students taking their language option into a senior NCEA level. x1 of the language options had enough students so that they could have separate NCEA L2 & L3 classes (up to this point they had always been composite classes)
– Improved NCEA results across the board
2025
– Maintained for 3rd year running x2 Yr 10 classes for the same x2 language options
– x 2 of the language options had enough students so they could have separate NCEA L2 and L3 classes – no composite classes anymore
– Improved NCEA results across the board
What a hugely satisfying result. Your efforts were rewarded by hugely increased retention which has been maintained which really validates your conclusions. This would be hugely interesting research for other language teachers and of course has application to other disciplines. Well done!
I have used a very limited anonomised data set because many students from the original data set have moved cohorts with only some of the remaining students completing the aformentioned NMM writing assesment. I took the assesment data available and added the matching ethnicity and attendance data, then used Ai to find any patterns and corrolations as would (or should) be the norm. With such a limited data set any conclusions are speculative only, but demonstrates the ability to find these patterns and corolations, which can then be used to address the wheres and whys of shortfalls. However this excercise does demonstrate the power of data analysis and how it could be used. https://docs.google.com/spreadsheets/d/16yV9GDp-kKOlVILdDURCIJytAhE96SkCQMfPgnpDFv0/edit?gid=1369808910#gid=1369808910 will take you to the data set and https://docs.google.com/document/d/16C5WKMvW_8pE7HRD4jqfAr8sK4NzvjK8NHn6t4Xm-WA/edit?tab=t.0 will take you to the summary and conclusion doc. I have sent these to SLT and the relevant team leader to demonstrate what is possible with the appropriate records. In terms of how this data could be used and a plan developed in responce, attendance is a major issue to be addressed with a concurrent analysis of ethnicity and socio economic situations. Accessability to the internet and devices would also factor and may be a relatively easy fix. Ethnicity and gender patterns may be also be causitive and worth exploring. I have sent this to SLT and the Team Leader of the cohort to demonstrate what is possible.
Analysis and interpretation of the readings shows broadly agreed positions on the use of data. Generally, leadership support in implimenting and collaborating on data gathering, analysis and outcomes is vital. This operates at different levels, teacher to middle leaders, middle leaders to SLT and SLT to Ministry (in NZ’s case). Interpretation and integrating the various data requires a holistic, moderated approach given that collected evaluative data alone does not provide a full picture of a students learning journey. This is where OTJ’s, attendance records, effort and whanau for example all have a part to play when considering and reporting student learning outcomes. Teacher efficacy and knowledge also needs to be considered.
Adapting a data culture, ensuring data literacy, and adopting an inquiry cycle will show a more complete picture over time and provide all players with better knowldge with which to allocate resources, where to focus those resources, which become part of a “Program logic” evaluative process.
It is vital to ensure no pre-determinations exist and that data gathering is used to improve learning as its primary focus rather than labelling or grouping students. As a side note, the vexed issue of ability grouping v mixed ability grouping may result as a result of the inapropriate use of the data.
Like AO’s LI’s, WALT’s etc, for our students, the purpose and ideal outcomes of a data gathering exercise must be pre-determined. Without knowing what and why we are measuring, there is nothing to measure.
I have spoken to a middle leader and an SLT member on the requirements of this module and what data I would like access to. At this point I am keen to explore linkages between our Kura’s approach to pastoral care and student outcomes around academic, attendance and behavior data.
There are all sorts of fascinating interactions to explore aren’t there. Have you ever read Malcolm Gladwell’s book, ‘Outliers’. If you haven’t I suspect you would enjoy it. As a Principal I was constantly looking for the ‘secret sauce’ that would really positively impact achievement. Attendance, teacher engagement, expectations, resources, parental involvement, assessment appropriateness etc etc. Collecting the data to support your hypothesis is the interesting part.
I’ve discovered audiobooks! I listen while I’m driving or relaxing. I’m almost finished Legacy, I’ll read/listen to Outliers next. Thx for the recomendation.
Our Kura has adopted a tool called No More Marking. https://www.nomoremarking.com/?countryCode=NZ It provides data on reading levels across years 4, 5, 6 and compares them to moderated national averages. Its really interesting and highy endorsed by our middle school Team Leader. I’m going to get an editable spreadsheet of individual student outcomes, anonymise it, and use Ai to finds trends and patterns against attendance records, ethnicity and gender. From this create a summary and recomendations. Atleast thats the plan at this stage haha.
What is the due date for this module please?
July 28 Peter.
Reading 4 goes to an old link, but here is the link to the relevent pdf:https://drive.google.com/file/d/13plG4I-cUwgpedSttzdPul2ELWnU_h2d/view
Thanks for that but weirdly all the links take me to the right places. Hmm. Wonder what’s going on?
That is weird, when I clicked through the link, it took me to an old website that said it is no longer in use. Oh well, we found what we needed!