Keynote – Jamie Casap

EdTechTeam has expanded it’s offering of Summits to three in NZ. This is the third one I have attended in Auckland and they are also great value and a good chance to connect with other like minded educators.

Here’s my notes from the opening Keynote.

Link to photo’s from the Summit

Link to summit website

Keynote notes

Link to Jamie’s twitter profile

“Education disrupts poverty”

“The impact of a teacher goes on for generations”

We’ve had tech for generations (movies in 1900s, TV in 1950s, Computers in 1970s). Now we understand the science of learning AND technology is part of our daily lives.

Generation Z – just go and learn, we needed some one to teach us.

Tech is changing our world

Skills needed in a technology world.

Source – The Economist

What problem do you want to solve?

Iteration is the result of critical thinking. Don’t assess with a grade, that is feedback that doesn’t help the student get to the next level.

Collaboration is how problems are solved.

Convert information into intelligence.

Original Google servers


Google Servers now

Current latest technology is the worst technology a 5 year old will ever now.

Remember these? My first type of phone.

Posted in teaching | Leave a comment

David Hopkins and Powerful Learning

Professor David Hopkins was hosted by ACEL for a one day workshop in Auckland that I was fortunate enough to attend. David has had a range of educational experience as both a researcher, civil servant involved in policy and as a practitioner. It is this combination of experiences and the work he has done as in the English educational system with the Blair government that makes him an authoritative voice on education.

Key learnings from the Workshop:

Session 1 – School and System Reform

Under pinning all efforts in education is a moral purpose. Every student can reach their potential (Equity)

Under the right conditions, every student can achieve. Those conditions include having the task the student is presented with being in their Zone of Proximal Development:

School development happens in Phases. Each phase needs different ‘ingredients’ and management styles. Successful strategies include:

  • Bottom up target setting
  • Inside out change

Session 2 – Teaching and Learning

Use instructional rounds (see a description by Robert Marzano) to deprivatise the classroom. Use these to identify ‘theories of action’. Over many of these instructional rounds, Hopkins identified 10 common ones:

Theories of action for the whole school
1. Prioritising high expectations and authentic relationships
2. Emphasising enquiry focused teaching
3. Adopting consistent teaching protocols
4. Adopting consistent learning protocols
Theories of action for the teacher
5. Harnessing learning intentions, narrative and pace
6. Setting challenging learning tasks
7. Framing higher order questions
8. Connecting feedback to data
9. Committing to assessment for learning
10. Implementing co-operative groups

What good teachers do – assign students appropriate and engaging learnings tasks within their ZPD (in an average class this may be 4 different tasks.

Think like a doctor – diagnose the problem, apply a suitable treatment.

Improving outcomes for students is linked with shifting teachers to increase their ‘circle of competence’. The driver is intrinsic motivation which is made up (according to Dan Pink) of autonomy, mastery and purpose.

Five conditions for building intrinsic motivation among teachers

  1. Maintain structures for scaffolding teacher development
  2. Make peer coaching ubiquitous
  3. Create protocols for both teacher and learning
  4. Incentivise teacher teams
  5. Ensure classroom observation focuses on learning

Peer coaching:

  • In triads rotating around turns at doing the observations
  • Theory-> Demonstrate->Practice->Feedback->Coaching
  • Example of Pat Cash coaching Shane Warne:

Session 3 – Leadership

The playbook for success:

  • get early wins
  • decide on non-negotiables (related to the moral purpose) and secure resources
  • install capable and like minded people
  • deeply engage with stakeholders

The narrative + a credible plan + moral purpose = action

Strategic acumen: the actions you take tomorrow as a leader contribute to where you want to be next year

Leadership style – varies with what stage the organisation is at.

Posted in teaching | Leave a comment

What does ethnicity tell us about a learner?

So this article about the ‘only brown kid in the room‘ has been shared by a couple of staff  at my school and, combined with this article about how a NZ student still experiences racism got me thinking about that whole thorny issue of ethnicity in education.  I don’t claim to be an expert and be able to offer the silver bullet, but I do know that my understanding on these types of issues gets deeper when I hear other peoples views. And often views that are contradictory to my own deepen my understanding the most.

If I look at data from my school, we can produce evidence that support the view argued in the NZ Herald article that there is an achievement gap if we disaggregate by ethnicity.

Graph shows Ethnicity vs Total credits gained with the red line being that magic figure of 80 credits.

We can see that the median for Maori is less than for other ethnic groups (and yes, I’m quite happy to be challenged on the use of total credits as a measure of academic achievement – there are holes in the data used but I think it shows a trend). But what about if we combine this data with socio-economic factors?:

So, when we look at the data this way we see that there is quite a spread of achievement across both ethnicity and decile (socio-economic rating). It is not only ‘rich’ kids that achieve, and it’s not only ‘brown’ kids that fail. So what am I trying to say? Ethnicity and socio economic status don’t really tell us that much about a student. I’ll try and explain this point in a different way. Below is an excerpt from an email a tutor sent about a new student joining one of my classes:

“XXXXXXX will be starting in the Inquiry class at the beginning of Term 4.  His family have just moved up from Christchurch.  He has sat our Entrance Test and tested as well as any student that has sat the test, admittedly a year later than most of them.  He was also very methodical in his approach doing the test.

He knows absolutely no-one at the college, so might take a while to settle.  Please help him to do so.  He says he did not enjoy the ‘open-plan’ environment of his last school in Christchurch.  This may have been due to new schools, teachers, systems as an aftermath of the earthquake.  He was initially concerned that the Inquiry class would be the same.  However, he is prepared to give it go.”

All very useful information in getting to know this student before I taught him. I think most of you would agree that this is very appropriate information sharing, and the tutor was communicating the important information that will be useful to a teacher. But notice what’s missing. No mention of ethnicity or socio economic status. Imagine if all this tutor had passed on to me was like ‘he’s a poor European’, or ‘he’s a rich Maori student’. This information is really of no use to me to getting to know the student as a learner. Yet we hold these categories (ethnicity and socio-economic status) so dearly when we are trying to measure student achievement.

I reckon we should stop categorising students by ethnicity. It is not a great predictor for success at school and can lead us to making assumptions about a student that are not accurate. Let’s focus more on indicators that are more meaningful (achievement data from previous school, diagnostic testing, learning preferences, topics the student is curious about etc.) and getting to know our students as individuals.

Posted in teaching | Leave a comment

#NotAtULearn 2016

Another October holiday rolls around and I haven’t got to attend the great ULearn conference. But, as in previous years, I’ve lurked on social media and have found the following take aways:

  1. A post by Mind kits (who must have been a vendor) about lesson plans for teaching 3D printing concepts.

2) A great sketch note about levels of engagement.

3) A link to a heap of presentations taking place at the conference. Great to have the community of educators so willing to share.

4) An interesting presentation by Shaun Brooker about how PL fits in with TPACK. Often I find I am just presenting about a particular technology tool (T) and how it can be used in class (P) without relating it to content of the S of Special Character.

5) Chinese character for learning – have seen this before and have tried to use it in my class as a way of improving student ‘mindfulness’. I start drawing the symbol on the board when waiting for quiet. If I complete the whole symbol with out the class focusing, their is a whole class consequence. Not sure if the character is actually correct (see Google Translate) but I like the idea.

6) Re-imagine the one size fits all approach of standard school PL with the Pineapple Chart (why Pineapple – apparently it is a symbol for hospitality)

7) I just like this picture….

8) Game of the week (relates to social conformity but I like the Family guy clip)

9) Like this image  – comes from a report (p 2)on ‘Global competency for an inclusive World‘ published by the OECD.

10) Virtual reality in the classroom – something I want to have more of a crack at (there just doesn’t seem to be enough time….) Maybe I’ll get my classes to make a Google Cardboard at the end of the year.

Posted in teaching | Leave a comment

Data based decision making – what the literature is saying

The following was submitted as an assignment for a Waikato University paper on Using Evidence for Effective Practise.

Education, like many other modern endeavours, is rich with sources of data. But often educators are too busy in the work of educating to take the time to reflect on the usefulness of the data collected and then the analysis of this data. This essay investigates what the current literature is saying about the selection of data and it’s purpose in the education setting. In beginning this investigation, we must first look at defining data.

What is data?  At its most basic definition data is a piece of information. In the educational setting it is a representation of a measurement or quality. For example, a test score, the reading age of a student, the number of behavioural referrals a student has. So something that can be collected and organised. Madinach (2009) proposes that educators should go beyond this simplistic definition of data and use data to make meaning, then translate this into action.

Further, Schildkamp (2012, p. 11) describes four different types of data: output, context, input and process. This is a useful framework for understanding data in the educational setting as we often focus on output data. That is a test score, the result of some assessment, something that is measured at the end of a unit of work. We often ignore input data (socio-economic status of the learner, prior knowledge etc) and very rarely look at context and process data. In my own practice, I think of the test results of my Y9 and 10 classes in Algebra (outcome) and how I used to interpret this as just ‘Algebra is difficult’ rather than look at how I taught this topic (process). As Unger (2013) promotes, teachers need to link achievement data (outcome) with teaching practice data (process) and whether patterns of one can be explained by patterns in the other.

Many of the authors promote the ideal to use data to inform learning but also provide evidence that the use of data makes a difference (Chick & Pierce, 2013; Mandinach, 2016; Schildkamp, 2011; Unger,  2013) . Indeed the consistent theme is data based decision making. As Schildkamp  (2012) explains:

By data-based decision making, we mean that schools make decisions about students, about instruction, and about school and system functioning based on a broad range of evidence, such as scores on students’ assessments and observations of classroom teaching.” (p. 1)

One of the barriers to more effective data-based decision making is the lack of data literacy amongst educators. Educators need to be upskilled in data literacy (Schildkamp, 2011; Mandinach, 2013; Mandinach, 2016; Chick & Pierce, 2013).  Mandinach (2013) describes this as the ability to understand and use data effectively to inform decisions.  Many teachers are too caught up in the busyness of teaching to step back and reflect on what the results of that recent test actually mean. As Unger (2013, p. 51) explains; “I sometimes feel that we are all working very, very hard, but we are not always sure of what we are doing and why.”

Further, they may not actually have the understanding of statistical tools such as correlations, effect size, sample populations and the like to make informed decisions from the data they are looking at. And it is not just a simple fix. As Mandinach (2013, p. 31) states: “Educators need multiple experiences to develop data literacy across their careers,… “. A further way to improve this is to change the culture of school and have teachers supported by data coaches (Unger, 2013).

Implied in this need for more data literacy is the shift of use of data from compliance and accountability purposes to continued learning and use for improving student outcomes. A good example of this is Hipkins (2011) study of Albany Senior School.  While based on a student survey as the main data set, it was interesting to note that whenever the authors made conclusions from the results, they were always supported by qualitative observations or other explanations. This helped to enhance the usefulness for moving forward rather than just the processed numerical survey data.

A reason why educators may not engage in these multiple experience to develop their data literacy is expressed by the Scottish Teaching Union. They, in a paper on the use of pupil performance data, note that “These tasks [keeping and filing records] not only risk undermining the work/life balance of teachers but also distract teachers and school leaders from their core responsibilities for teaching and leading teaching and learning (p. 6)”. Again, this notion of the busy-ness of the teacher to step back and interrogate what all this data we collect as teachers actually means and how it can improve outcomes for students.

Another theme was the use of technology to analyse data and that this analysis of data is not easily available on existing school analyses software” (Schildkamp 2011, p. 40).  I think of the behemoth that is KAMAR (the software that my school uses) that is very powerful in terms of how you can analyse data – if you are an expert in relational databases.

Mandinach (2013) details investment by States in the US into data systems to handle the data ($610 million) but not in an equivalent amount in developing the human capacity to use this. So this may be similar in a New Zealand context – we have expensive systems (think the online PAT testing costing $2700 for 800 students and 2 subjects) but not the same investment in helping teachers become more data literate to make the most out of the data.

In conclusion, the literature appears consistent in the message that the collection of data without action is a waste of time and resources. Action not based on data is ill informed. The combination of data and action leads to informed decisions more likely to have a positive impact on student outcomes. In my own teaching, I want to be more selective about when I formally assess and make sure I analyse the results, inform students of this analysis and make sure they can interpret their result in the context of the data.


Boyd, S., & McDowall, S. (2001). Techno magic, whizz or fizz?: The relationship between writing mode, editing process, and writing product. Wellington: New Zealand Council for Educational Research.

Chick, Helen, and Robyn Pierce. “The Statistical Literacy Needed to Interpret School Assessment Data.” Mathematics Teacher Education and Development 15.2 (2013): 19. ERIC. Web. 23 July 2016.

Hipkins, R., Hodgen, E., & Dingle, R. (2011). Students’ experiences of their first two years at Albany Senior High. Wellinton: NZCER.

Mandinach, E. B., & Gummer, E. S. (2013, 01). A Systemic View of Implementing Data Literacy in Educator Preparation. Educational Researcher, 42(1), 30-37. doi:10.3102/0013189×12459803

Mandinach, E. B., & Gummer, E. S. (2016). Every teacher should succeed with data literacy. Phi Delta Kappan, 97(8), 43-46.

NASUWT-The Teachers’ Union. (n.d.). The use of pupil performance data in target setting and in the evaluation of the effectiveness and capability of teachers (Scotland) (Rep.). Edinburgh: NASUWT Scotland.

Schildkamp, K. (2012). Data-Based Decision Making in Education: Challenges and Opportunities.

Unger, J. (2013, August). Flex your school’s data muscles: Leadership strategies strengthen data’s impact. JSD, 34(4), 50-54.

Posted in teaching | Leave a comment