#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.

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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.

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Google Education Roadshow in Tauranga

Cyclone hosted an afterschool Google Education Roadshow in Tauranga today. Always useful to go along to these type of events, not only to try and win a spot prize (a Chrome cast was given away today), but you also usually meet someone interesting or pick up some tips. Here’s my takeaways:

  • In the Admin console, you can configure a bookable resource calendar eg school van, theatre etc
  • Use a Google Form as interactive newsletter. You can know add quite a bit of content (video, images, text etc) and if you add in a few questions related to this content you will be able to get some feedback from your readers/parents.
  • Google Apps for Education Core services are ad free and user data is not collected.  Additional services (including Blogger and Google+) have ads and data is collected. Privacy/user agreements should be signed for these services.
  • Use Blogger for teacher portfolio using tags for each of the PTCs. There is also a Blogger App for mobile devices to capture evidence in class.
  • Chrome apps for learning Maori language :Nga Tapuae Tuatahi, Ngā Tapuae Tuarua
  • Use Screencastify to give feedback to a student about their assignment. Rather than write long and detailed comments, record a short screencast (which will automatically save to Drive), shorten the link (using Goo.gl) and add this link as comment on student document. Here’s a link to a more detail description.
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Growth Mindset workshop with Carol Dweck

IMG_4370Negative stereotypes, deficient thinking, ‘Can’t do this coz I’m dumb’… This type of attitude is not new in schooling but I discovered a new term for it a few years again when I first came across the concept of Growth mindset in Jo Boaler’s on line course ‘How to Learn Math‘ in 2014 (I get my Y9’s to complete the 6 part student course at the beginning of each year). In this course I learnt more about brain-based learning and the concept of Growth mindset and have been interested ever since. I even bought Carol Dweck’s book, Mindset, and eventually got round to reading it. So when the Learning Network NZ advertised they were bringing her over for a series of workshops, I thought I’d register and expand what I had already learnt.

It was obvious that Dweck’s work is quite well regarded. As well as hearing my own kids IMG_4373 (1)talk about ‘mindset’ from their primary school, the terminology kept coming up again and again (for example see a post by Clare Amos). So I guess I wasn’t surprised when I walked in to a full Ellerslie Events Centre with over 300 participants.

It a nutshell there are two types of mindset:

  • Fixed: intelligence is fixed
  • Growth: intelligence can be developed

It is important to realise that this is not a category for people – we don’t have ‘fixed’ people and ‘growth’ people. We are on a continuum along this scale and float in and out depending on our own psychology and how we react to various triggers. Mindset can also apply to organisations as well as individuals. These can have a culture of genius (fixed) or culture of development (growth). It got me thinking if this applies in my school?

“I don’t divide the world into the weak and the strong, or the successes and the failures, those who make it or those who don’t. I divide the world into learners and non-learners.” – Benjamin R. Barber


fixed vs growth mindset brainWhile also being a psychological concept, mindset is grounded in neuroscience. Based on brain plasticity (“You need to fire to wire”). There are a number of prominent authors on how the brain adapts to learning, including Dan Siegel and Norman Doidge among others.

If you really want to hear are remarkable story about how the brain can rewire, check out Barbara Arrowsmith-Young story at this TED talk. Her story is also detailed Doidge’s book The Brain that Changes Itself. Arrowsmith-Young’s story highlights how if you change the ability of the brain’s function in one aspect can lead to changes in other related brain functioning.

Dweck mention a Chilean study that investigated all Grade 10 students in the country (over 168,000). This showed there was a link between mindset and attainment that transcended socio economic status. However, she noted that twice the number in the more affluent group had a growth mindset compared to lower group.

Growth mindset gives students self belief to go for more than what they can already do. It’s not about celebrating mistakes but about what you have learned from your mistake. Identify this as a ‘learning mistake’ to differentiate from behaviour/other mistake. It’s about students thinking of themselves as a work in progress.

Carol talked of her message to all first years students in her Stanford University course: “Your job is to use the resources you have here to become the person you want to be to go and make a difference in your community”. One task she gets her students to do is to write her a letter from 25 years in the future focusing on the setbacks, mistakes and heartaches that they learned from along the way. This could be a useful task to give to my students.

Teaching the Growth mindset

So, if we encounter a student showing signs of the fixed mindset – what can we do. Here are some strategies:

Reframing Questions:

  • What strategies have your tried?
  • What are the achievement gaps?
  • What fabulous struggles are we having?
  • What will you try next?
  • What strategies might work better?
  • Show me what you’ve done and let’s figure out what you can try next.

Relationships: Focus on the relationship the teacher has with the relationship. Teacher’s attitude needs to be – “My life’s work is your development…”

Other Strategies

  • When students succeed, praise the process (and tie it to progress, learning). When they fail, focus on the process (fabulous struggles, the power of ‘Not Yet’)
  • Treat failures as beneficial for learning
  • Give clear feedback and a chance to resubmit
  • Role model making errors/mistakes

Assessing mindset

There are a number of online tools for students to assess their mindset (Mindsetworks, Mindsetonline, London Academy of IT). There are also non-digital methods. We were given on entry a set of cards with different statements on them and asked to put in piles of Agree and Disagree.

Growth mindset report comments: a significant way in which you can give growth mindset messages to your students and their parents is through the report comment. Hemi McDonald from HPSS in a blog post details to shift at the school to be more growth mindset focused in terms of identifying successful strategies and then next steps and why (further detail on HPSS reporting described here).

IMG_4372Other Notes

Negative stereotype = fixed mindset (e.g. Girls in math, ethnicity in educational achievement)

The journey to a growth mindset

  1. What is a growth mindset?
    1. It is not solely about effort – encouraging people to work hard vs. believing that talents and abilities can be developed
  2. What is the first step?
    1. Acknowledge we are all a mixture

Fixed mindset triggers:  These are things that set off a fixed mindset. There are four main triggers summarised in the table below:

Stepping out of your comfort zoneAvoid risk and challengeEmbrace challenges.
High effortIt should come naturallyHard work is the key (effort + strategies + input from others)
SetbacksHide mistakes and deficienciesConfront mistakes and deficiencies
FeedbackIgnore feedback or criticismSeek out feedback
SUMMARYNever look dumb, don't work hard or seek help, run from difficultyLearn, Work hard, use strategies, seek help to learn, learn from mistakes.

Related resources

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Data Based Decision making

So having recently gained my Google Trainer certification, I thought it may be a good idea to embark on some more post graduate study – let’s see how this goes over the next few years…

I have enrolled in Waikato University’s School of Education’s Master of Education (Professional Learning) course. This was 2 compulsory papers, one of which I am starting on Friday – Using Evidence for Effective Practice.

Our first assigned reading was:

Schildkamp, K., Lai, M. K., & Earl, L. M. (2013). Data-based decision making in education: Challenges and opportunities. Schildkamp, K., Lai, M. K., & Earl, L. M. (2013). Data-based decision making in education: Challenges and opportunities.

Chapter one was a good introduction to the paper and data driven decision making: “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. (p1)

We often just think of analysing data as relating to outcome (test) results so Chapter 1 gave a good explanation about the importance of other types of data (context, input and process-p11) and how schools can use this range to best improve student learning.

I also found myself reflecting on how the authors reinforced the underpinnings of the Teaching as Inquiry process (e.g “synthesis of the literature on professional learning that makes a difference to student achievement found that schools that used data to inquire into the effectiveness of their teaching and school practices made significant improvements in achievement (Timperley et al.2007) p15). In fact, Fig 2.1 on p16 is another way of illustrating the TAI process.

Fig. 2.1 Process of data use

Fig. 2.1 Process of data use

At my school I’m responsible for guiding 2nd year teachers through a TAI and so this chapter was useful and in fact I emailed one of my colleagues the flow chart  to highlight the purpose of his Inquiry. So this idea of ‘instrumental’ use of data (p19) which “involves analyzing and interpreting and data as well taking actions to improve based on the analysis and interpretation” means we need to do more than just give kids a test and record a number in a mark book.

I was also intrigued withe the section on how data can be used and abused. An example is roll based vs participation based pass rate at NCEA and how that data can be used to overstate actual student achievement.

Chapter 3 was a good description of how to analyse achievement data in context of classroom practice data – the two are obviously linked. The concept of the ‘ill-structured problem’ (p27) succinctly defines the challenge we face in the classroom where there are “no definable procedures for reaching a solution and uncertainty about the information required to solve the problem.” 

Another quote that resonated with me is how data can be used to ‘blame’ the student’s family circumstances rather than analysed to improve teaching (p32). With the multiple facets to student achievement (socio-economic, family, peer group, mindset, teacher, school…) we as teachers can often feel a bit more comfortable in explaining low achievement on other factors that we can’t control rather than looking at our own practice.

I was also interested to see how the authors acknowledge than one challenge for schools is not only staff being unfamiliar with analysing data and aspects of Pedagogical Content Knowledge (how students understand and misunderstand their subjects p 42), but also that analysis of data is “not easily available on existing school analyses software p40.” 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…

My main take away from the two chapters was how we need to link achievement data (outcome) with teaching practice data (process) and whether patterns of one can be explained by patterns in the other (p36). In my own practice, I think of the results of my Y9&10 classes with Algebra and how I used to look at this as just ‘Algebra’s tough’ rather than look at how I taught this topic.

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