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“If you don’t lead with small data, you’ll be led by Big Data”

Posted on November 28, 2018 by Derek Wenmoth

Derek Wenmoth reflects on Pasi Sahlberg’s uLearn18 keynote address.

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The keynote address by renowned Finnish academic and author, Pasi Sahlberg on day two of the uLearn18 conference may best be summed up as providing a warning and a call to action. While many in the audience were expecting to hear stories of how progressive the Finnish education system is, Pasi took us in a different direction. In his casual, at times ‘under-stated’ manner, he made us reflect on the challenges facing our education system and education systems around the world. Pasi then explained how we mustn’t simply expect the ‘system’ to provide the solution – that it should be the work of the professionals in the system to step up and take responsibility by focusing on each child and each classroom to make the difference.  

The underpinning message throughout the keynote was the need to focus on and respect the learner, with his or her particular needs, strengths, abilities and ambitions, and to understand this as the key to a truly learner-centred approach in education. With subtle wit and humour, Pasi shared his own experience as a maths teacher who once wanted to be a mathematician. Teasing this out, he described the stereotypical view of a maths teacher that has established itself in the minds of students, and the disservice we do to the field of mathematics – or any discipline for that matter – when we allow the focus on the content or the discipline to become more important than the learner. In the learners’ experience their interests will typically traverse multiple disciplines and be more holistic, integrated and ‘linked’.  This focus on the learner and the learner’s perspective is pivotal in building a successful, future-focused approach to schooling.

The Warning

The warning Pasi gave us is simple, and is tied in made explicit in the title of the keynote: if we don’t lead with small data we’ll be led by big data.

The problem is that education policymakers around the world are now reforming their education systems through correlations based on Big Data from their own national student assessments systems and international education data bases without adequately understanding the details that make a difference in schools.

(https://pasisahlberg.com/next-big-thing-education-small-data/)

By ‘big data’ he is talking about all forms of assessment and achievement data that is currently being collected and collated at a national and international level, sifted, sorted and represented back in the form of statistics and trends upon which large scale decisions are made about curriculum, policy and resourcing. We know this well in New Zealand with the recent experience with National Standards and the pre-occupation with OECD data that appears to cause immediate swings in what is deemed to be important.

Pasi’s key warning here is “Don’t confuse correlation with causation”. Just because the data can be construed to reveal certain patterns or trends doesn’t make it true in the context of a specific student or school. To illustrate his point he used a combination of OECD data and national data on the amount of ice cream consumed to “prove” that ice cream consumption positively affects education scores!

The more concerning warning came when considering how technology may be viewed as providing a solution to meeting the demand for mass-personalisation. The argument presented is compelling – if 75% of education spending is on people, and we could reduce a third of the “people” by using artificial intelligence (AI) in education this would present an attractive proposition for budget-conscious politicians. But what will it mean for educating students as a whole person?

To illustrate that this is a very real and current concern, Pasi used the illustration of alt-school – a network of progressive schools in the USA. These schools advertise themselves as being completed learner-centred, providing learning that is self-driven, competency-based, personalised, socially embedded, and open-walled. It sounds like the ideal scenario – highly personalised pathways for individual students, powered by a sophisticated AI that is monitoring each student’s every move using a series of cameras throughout the environment, and monitoring their every keystroke and response to online content and instruction.

There’s no doubt this approach works, and produces students capable of passing exams and demonstrating their gains in learning – but what’s missing? Where are the relationships with others? Engagement in play? Interactions with people? Development of empathy and other effective qualities that may best serve the future of humanity?

A key question here is to ask, “if it is truly personalised learning by the AI, where is the child’s voice? How can it be personalised if the child does not have some form of input?”

So the challenge is around just how seriously we take this possible future – and not to simply cast it aside as a ‘pipe dream’ that is the work of science fiction, because after all, ‘technology will never replace a teacher!”

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The call to action

The call for action directs attention directly on those of us working with learners in schools – the teachers. Pasi challenged us to trust our raw instincts, to be amongst what is happening, not just observing from a distance. This will require a greater degree of professional trust is the key to the small data conundrum.

In Finland, trust is for us the full trust and freedom for our schools and teachers, believing that they can develop goals, teaching standards and content appropriate for their children. The trust is instilled deeply in our culture; it is not a single behavior in a particular situation.

(https://pasisahlberg.com/interview-teachers-need-a-sense-of-mission-empathy-and-leadership/)

Small data is what we gather when noticing the small stuff that is occurring in the specific context of the classroom or individual student learning, and will make a difference to the big picture when we combine what we observe with our professional wisdom. Pasi’s point is that this ‘small data’ reveals patterns and insights that the ‘big data’ with its statistical trends and correlations can never do.

Pasi’s call for action is that we discover together, at the local level, the power of collective professional wisdom. It is the little things we do as teachers with our learners that makes the difference.  This resonates well in the NZ context where we’ve valued Overall Teacher Judgements (OTJs) as a part of the assessment process – but if we’re to be serious about taking up Pasi’s challenge, we will need to become even more serious about ensuring we build our professional capacity even further – and deeper – so that we can be even more secure and confident about the ‘professional wisdom’ that we are able to bring to bear on the observations we make.

To achieve this Pasi recognised the need for educators and government agencies to work collectively to find ways to reduce teacher workload, support special education, fund public schools better and use student voice to design learning.

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The three key take-aways that Pasi left us with were:

  1. Build a trust-based professionalism – trust colleagues, bosses, children – and this will include building trust within the community too, with parents and future employers etc.
  2. Build professional wisdom as evidence – we need to give greater priority again to our professional reading, participation in professional associations and in-school professional learning groups (PLGs) where our professional knowledge can be challenged and honed.
  3. Lead with Small Data – if you don’t make this a priority, you WILL be led by big data!

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Preparing the next generation for the algorithmic age

Posted on November 28, 2018 by James Hopkins

James Hopkins summarises Mike Walsh’s uLearn18 keynote address and interviews Mike.

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What does the future mean to the education industry? Futurists tend to get a bad wrap because they often make technological predictions. Mike Walsh argues that successfully predicting the future is more about paying attention to people, not the technology in their lives.

While in Japan, Walsh shared his thinking around Masayoshi Son’s ability to raise hundreds of millions of dollars by starting his thinking 15-20 yrs into the future, and simply working backwards to see what support and infrastructure would be needed to make that future a reality. He then goes to find those companies to invest in and if they don’t exist, he creates them! You see, what Masayoshi does differently is that he looks at the people needed to create a distant dream, not the technology. And so, Walsh surmises, we needn’t be looking at the current crop of Millennials to make predictions about education in the next 12-15 years, as by then, they will be “as old and as miserable as the rest of us!” The people we should be looking, Walsh describes as the most terrifying generation we’ve ever encountered, are eight year olds!

Why are eight year olds so different?

The way our current crop of primary aged students interact with technology is vastly different to the generation previous to them. Walsh points out that this digitally native group of users develops an almost intrinsic understanding of the algorithmic framework that drives interactions from an impossibly young age. It’s this genuine difference in the way they interact with technology that Walsh believes will lead to a very different way of thinking around the way we connect with and explore knowledge.

It’s not the screen that’s interesting, it’s the experiences and the way technology has interacted with it. YouTube has changed the way an entire generation watches TV. Every experience children have now has been customised and hyper-individualised by the data collected by social media. Children now are at the beginning of a true algorithmic society, a social credit score based society. Terrified yet? The currency and fabric of daily life is fast becoming driven by data, artificial intelligence, algorithms and machine learning. Computers themselves are constantly adapting, writing their own code and programming, no longer reliant on the dinosaurs of the MS-DOS prompt generation.

“The minute you joined Facebook, your kids left!” 

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Adaptive Learning

The reason adaptive and machine learning has so much potential is because it allows us to truly take the world’s knowledge, understand an individual’s needs and to personalise and tailor it to algorithmic, logical perfection. Students of tomorrow have the opportunity to be taken along their own learning journey, at their own pace and scale, for vastly reduced sums of money. As Walsh points out, this is not to say human teachers are not important, just that we are entering an age whereby content and opportunity can be delivered in a scaled way, that has previously been inconceivable. And we’re going to need it! Walsh continues on to share that the skills, knowledge and understandings required to function successfully in an algorithmic age are not being taught in today’s schools. As we stand at a precipice, faced with the landscape of tomorrow’s society, how can teaching knowledge and skills of yesterday, prepare leaders and learners of tomorrow? We need to start by articulating what those skills might be…

Automation of Industry

When farm jobs started to decline during automation, the westernised education system began to evolve. Many smart and forward thinking people realised the need to invest in new forms of education in order to prepare people for the future. Technology doesn’t destroy jobs, it simply changes them. It’s not always a straightforward process and often the realisation takes a little time. Sometimes enabling technology, even though it can be hugely disruptive, can actually increase the number of people employed in an industry. Take ATMs for example. Some bank tellers lost their jobs, however because paying the number of people who worked as tellers reduced, it meant that more branches could be opened- thus increasing the number of people working for the banks!

It’s becoming a case of looking at the type of people that will thrive in an environment that focuses on both the world of people as well as having a strong understanding of how to leverage data and apply it. Computational thinking is not about teaching children to code, it’s about how to leverage technology to break a problem down and find a strategy to automate its solution. Thinking about the future, this gives students the ability to both understand the essence of a problem as well as a knowledge of the tools and processes to combat it.

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Key skills for the next generation

As we see the rise of this hybrid approach, the understanding of the problem and the data to solve it using computer science and technology, we need to teach the next generation to be more comfortable with ambiguity. We are in danger of preparing students for a world that has become obsolete by the time they leave education. The CEO of Netflix looks for employees that can exercise “good judgement in ambiguous situations.” This is harder than it sounds. As we leave a structured education system that has exams and allocated time, hierarchy and structure, and they walk into a world that has huge unknown, how can we make sure they cope. How do we teach students to process unpredictability and handle ambiguity?

Another element we need to help learners become aware of is the power of machine thinking and artificial intelligence. Here Walsh sites Deep Blue (an AI) beating world Chess Master Gary Kasparov. Reflecting on this event, it became clear that the computer was not trying to beat the world’s greatest human chess player by a substantial margin, it was simply trying to do the very minimum to win by just one point. What this means is that we need to understand how a computer ‘sees’ the world and problem solving. A computer will conserve resources, not try to focus on the end goal and winning big. A computer will work out the simplest way to win and this way is often not an approach that a human will see, let alone take.

The final element we need to take into account is to teach students to centre themselves, find the right moral compass and make good ethical judgements. Here Walsh suggests that perhaps studying computing is not the best way forward, but the studying of philosophy in order to help build decision making capacity using a strong moral compass. This is not about following the laws of a land, it’s about following the laws of trust, set by humans. As the debates around privacy and our data continue to rage, we are entering a time where understanding the tech is important, but understanding underlying motivations and human behaviour is even more valuable.

“The algorithmic age is an opportunity to embrace new and exciting ways of thinking…”

Q&A with Mike

Are our experiences within the digital economy going to get wider and bigger?

It’s impossible to not participate in the future. It may become impossible to get a bank loan or go about daily efforts as you’ll have no transparency and digital value. With kids, we have about 9-10yrs where people shelter them from tech. If we don’t teach them how to function appropriately and effectively, then how can we expect them to function?

How can we avoid programmer bias being transferred to AI?

This is important. We need to interrogate the code that is produced. How was the data collected? Are they discriminatory? There’s a need to have well educated teachers and others so they can be part of the discussion.

Small data: The rights, the voice and the individual. How do we as teachers ensure that the rights of our children are at the forefront?

People assume it’s a binary thing. They think it’s either about human interest or corporation driven outcomes. I see it as a combination. As we scale up good education into remote communities or for larger class sizes, it should be a partnership. Everyone is at a different rate of learning and we can leverage small or big data to find what someone knows and unlock their potential.

Teachers in the future: They need to be informed, discerning, questioning and listening. So what might it actually look like?

Teachers need to be as good as the tech they use. I don’t believe classrooms will disappear. The power of humans together is incredible. People working from home is beginning to end because their best ideas come from the old school analogue way of being face to face. In 10-20 yrs we won’t have virtual classes. If anything the tech will be less visible. It’s the data that sits behind it that will really shape the system.

Are humans learning to think less for themselves therefore teaching ourselves to becoming less intelligent?

In many ways we don’t have the same memories because we have google! We live in times when we don’t even need to remember phone numbers. Tech has become an extension of our memory and perception. Does it makes us stupid? I think it’s changed us. It should allow us to extend ourselves.

As someone who travels world as a global nomad – where do you think the patterns around where people live lie? Will travel decrease because of tech?

It feels like we’re going backwards. How did we lose Concord? Even with tech, our ability to see more digitally makes us want to see it more physically. I hope it will make people want to see more. Autonomous cars, flying cars and drones, all will change how we interact and how we design where we learn. We need to remember not to forget what it means to keep in touch and be human.

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Image Credits

Mike Walsh Keynote Photo by Becky Hare via Twitter

VR Photo by Giu Vicente on Unsplash

Chess Photo by JESHOOTS.COM on Unsplash

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Tō reo ki te raki, tō mana ki te whenua

Posted on November 28, 2018 by Hohepa Isaac-Sharland

Hohepa Isaac-Sharland reflects on Hana O’Regan’s uLearn18 keynote.

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‘Tō reo ki te raki, tō mana ki te whenua’

Let your story be heard in the heavens, and your mana be restored to the land

(2018, O’Regan)

Kia piki taku rau huia ki ngā tihi tapu o taku pae a Tararua,

e rere whakarunga ki te ūpoko o taku ika tapu,

Kia whiti atu rā i Te Moana o Raukawa ki te tauihu o te waka a Māui, ki te tauranga o Uruao,

Kia hōkai ake rā i ngā tapuwae o Rākaihautū,

tau atu rā ki ngā pākihi whakatekateka o Waitaha,

Kia hiki aku mata ki tō wehi, ki tō tapu Aoraki e tū mai rā,

otirā ki tō mana e hora iho nā e Tahu e!

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Ka mihi, ka tangi ki a koutou katoa rā kua riro rā ki ngā hawaiki. Ka tautoko ake i ngā kupu mōteatea mōu Matiaha Tiramōrehu, otirā ngā kupu mihi, ngā kupu tangi, e koro e, moe mai rā. Ko tō reo ki te rangi, ko tō mana ki te whenua! Kāti rā.

Ko Tahu, ko koe e Hana. Ka mahana te ngākau, ka pūhana mai te wairua! Ka hotuhotu te ngākau, ka maurirere te wairua! Nāu e Hana!

Ko te momo i a koe Tiramōrehu, tohunga whakairo i te kupu ki te arero, ki te pepa, pou ranga i ōna tira, pou whakatō kākano ki ōna uri whakaheke, heke, heke, heke ki ō mokopuna te hāpai ake nei i ō wawata! Erangi ka hotuhotu ki ō tini mokopuna e kōtiti nei, e kuare nei i ngā kōrero mōu, auē te mamae e! Ka huri rātou ki hea? Mā wai rātou e tauawhi? Mā te mōteatea ō kōrero e whakakanohi mai, e whakaringaringa mai i te Tahu o āpōpō! E kore tō reo e ngū, e kore hoki e ngaro!

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He hae roa, he ngau kino i te korenga o ngā kōrero o ō tātou tīpuna i kōrero i ō tātou kura. Ko ngā pānga tōnui ka taka mai ki ngā whakatipuranga e kīa ana, he tangata hākinakina, he tangata māngere, he tangata katakata, he tangata kēnge, he tangata mauhere, aha atu, aha atu. Pēnā i tāu i mea mai, ‘they know they (stereotypes) exist when they are followed around a dairy……have to process verbal abuse for speaking Māori to each other’ (2018, O’Regan).

Heoi anō ko tāu pū, kia tika mai, kia pai mai te ao, he whakapapa, he pūmanawa, he pūkenga o tēnei iwi taketake mai i ngā kāwai rangatira. ‘We can be the generation that made the change. We can reclaim our story and help our people understand it’ (2018, O’Regan). I ēnei kupu āu, ka tū te ihi, ka tū te wana, ko wai rā te tamaiti kei mua i a koe, ko Tahu, ko Te Rautāwhiri.

Ko au, ka whakataukī ake i āu kupu akiaki, i āu kupu whakatūpato, i āu kupu whakaaraara ki te ao mātauranga, otirā ki te ao e noho nei tātou kia aro pū ki te tamaiti me ōna kōrero whakapapa, arā he tāonga, he kākano.

E Hana ko tō reo i rāngona ki te rangi, ko tō mana i horahia ki te whenua, e te tuahine – mauriora!

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Ngā Tohutoro

  1. O’Regan, H (2018, Oct) Te reo ki te raki, tō mana ki te whenua. Paper presented at the Aotearoa New Zealand CORE Education uLearn Conference, Auckland, New Zealand.

Ngā Whakaahua:

  1. O’Regan, H (2018, Oct) Te reo ki te raki, tō mana ki te whenua. Paper presented at the Aotearoa New Zealand CORE Education uLearn Conference, Auckland, New Zealand. – Images 1-3
  2. Tāwhiwhirangi, K (2018, Oct) ULearn18 Keynote Speaker – Image 4

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Words, words, words

Posted on November 21, 2018 by Philippa Nicoll Antipas

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Ko tōu reo, ko tōku reo,

te tuakiri tangata.

Tīhei uriuri, tīhei nakonako.

Your voice and my voice are expressions of identity.

May our descendants live on and our hopes be fulfilled.

(Learning Languages Whakataukī, NZC 2007)

We language our world and ourselves into being. We have ideas. We think thoughts. We express these things to ourselves and to others using words. The words we choose to use say something about the person we are, and the way we perceive the world to be. So we may say that language shapes our culture, and shapes our identity.

Words, to perhaps use a construction metaphor, are the building blocks of stories. Words, strung together in sentences, held together with the mortar of grammar (and punctuation, if the words are written down), create worlds and the characters who inhabit these worlds. Some of these characters become ‘larger than life’: Māui, Harry Potter, Gollum… By the words we choose to use, and the stories we choose to tell, we convey important messages and ideas about who is important, what beliefs are valued, whose perspectives we honour. In this way, words and stories, and storytellers, have infinite power. Hana O’Regan spoke about this kaupapa at uLearn this year.

Let’s consider a couple of examples.

It is reasonably commonplace these days to speak of the ‘industrial model of education’. This phrase employs factory metaphors. We can see these ideas in words like ‘classes’, the ‘timetable’, and teaching ‘units’ to make sure students know all the necessary ‘nuts and bolts’. The overarching factory metaphor suggests that we see knowledge as ‘stuff’, and that education is about putting knowledge into people’s (empty) heads. And that this is best done by breaking knowledge down into small, manageable chunks, and telling people what they need to know, because we store knowledge in our own individual heads (see Gilbert, 2005).

When we say that we’d like to embrace ‘21st century’ or ‘future-focused’ teaching and learning, alongside unpacking what this means for us, we also need to examine the words we use to imagine and conceive of school and its purposes. Often we’ll find it very hard to move away from these words, as they are the signs that show us that we still think about education in this way. Finding new words, embracing new metaphors, telling new stories until these become ingrained, is a challenge.

Or perhaps this example:

We need teachers to come on board with our new initiative or strategy, or to adopt a new practice. Some teachers seem quick to embrace this innovation. When this happens, we sometimes say that they are the ‘early adopters’. This is a reference to the popularised research by Everett Rogers in the 1960s (see also the Diffusion of Innovation theory). ‘Early adopters’ we might find to be a comfortable phrase or label, but who is at the end of the scale? The ‘laggards’.

Can we use one term in isolation from the other? Who would choose to be known as the ‘laggard’? What do these words suggest about how we think of others – of our colleagues and peers?

When we tell stories, we generally speak from our own perspective, and because of this we tend to make ourselves the hero of this story. Jennifer Garvey Berger and Keith Johnston explore this idea in their book Simple Habits for Complex Times. It can be useful to keep this in mind when a colleague does or says something that you struggle to comprehend. Garvey Berger and Johnston recommend asking yourself: “If I had just done what that person did, and I thought my actions were perfectly reasonable, what story might I be telling myself?” (p. 24). We can use language to practice respect and empathy. We can challenge ourselves to be imaginative and compassionate.

We could apply these ideas about words, language, and stories to many phrases we use in education:

  • Priority learners
  • Māori boys’ writing
  • Manaakitanga
  • Those who are ‘resistant to change’
  • Teacher aides
  • Special needs
  • ESOL

And more. What springs to mind for you?

This is an invitation to reflect on your vocabulary choices and what stories they may have to tell about you and the way you see the world around you. How do you refer to your learners? What words do you use to describe them? How do you refer to your colleagues? What words do you use to describe them?

Language is a dense and thorny thicket. Making your way through this thicket is rife with dangers. You must pick carefully your path through. Be mindful; be present – lest your words bite like thorns on the vines.

We can tell this story another way though.

Language is a seed bursting with possibilities. Plant it carefully in rich soil. Give the seed kindness, love and attention. Nurture its shoots, and protect it from harm. Be mindful; be present – so that your words may inspire.

What words do you use? What stories do these help to tell?

References

  • Garvey Berger, J., & Johnston, K. (2015). Simple Habits for Complex Times: Powerful Practices for Leaders. Stanford: Stanford University Press.
  • Gilbert, J. (2005). Catching the knowledge wave? The Knowledge Society and the future of education. Wellington: NZCER Press.
  • Title reference: 2.2.192 Hamlet

Image by Bogomil Mihaylov, CC0 

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Two free time tools that I use everyday

Posted on November 14, 2018 by Rochelle Savage

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My first job out of University was writing and performing comedy for a television show. On the whole it was as enjoyable as it sounds – mainly due to the people I worked with: Hori Ahipene, Lyndee-Jane Rutherford, Rawiri Paratene, Dave Fane, Dave Armstrong, Cal Wilson, Raybon Kan, Jemaine Clement, Oscar Kightley, Pip Hall, Paul Yates, Robbie Magasiva, Jackie Clarke etc. However, I don’t know if Rawiri Paratene forgave me for my excitement that he was the Play School presenter from my childhood when he is one of the best actors, writers and directors (amongst other impressive life achievements) in Aotearoa with an impressive resume for film, theatre and TV.

But what wasn’t fun was having to write up my invoice each week to account for my hours each day – what hours did I work on Monday? Did I leave early on Friday and come in on Saturday morning – or was that last week? I would look over my diary and try to work it out.

In my second life (post-children) I have worked as an Instructional Designer for over 15 years and like a lot of jobs I need to look at how I spend my time and how to make the best use of my time.

Below are two short videos of two free time tools I use every work day that help with accountability and productivity; and how I use these two time tools.

Yast

Kia ora – Ko Rochelle tōku ingoa. My name is Rochelle. I am going to talk you through how I use Yast. Shout out to my friend Ben who introduced me to this and which I have been using for over ten years ago. Also a disclaimer – I am going to show you how I use Yast and how I would show a friend.

This is not to say there are not other ways you could use it and this is just for the free version. You first of all need to create an account which is very simple – it just requires your email address. I won’t show you that in this video but I assure it is quick and simple.

I use Yast everyday – it is great if you are either a freelancer; or have several jobs or need to keep track of the time you spend on different aspects of your job.

You can create folders – or categories –  and have tasks within them. You can make it as detailed as you like. The best aspect of it it is it is really simple. You click on what you are working on and when you finish you click off it.

If you forget to click off it – you can adjust it. The other aspect I like is at the the end of the month – or whatever time period you choose you can select and see how you spent your time and report that to others.

So that’s how I use yast. I am sure there are other ways you can use it and as I say this is just the free version.

Pomodoro

Kia ora – Ko Rochelle tōku ingoa. My name is Rochelle. I am going to talk you through how I use Pomodoro.

I encountered the pomodoro method on Barbara Oakley’s Learning how to learn course. The science behind it is that humans tend to work best in chunks of time – 25 minutes and then to have a 5 minute break and carry on.

I work from home and it can be tragically easy to carry on working and it means you don’t allow your brain the possibility of the aha moments – you know the solutions you have when you stop and have a cup of tea or go and hang out the washing.

The other bonus is if you are feeling particularly uninspired about a task – you can say ‘only ten more minutes and I can have a break’ it helps you stay on task’.

This carries on for several hours and then it schedules a 15 minute longer break. And after this break – back to the 5 minute breaks. Now you can also pause both your working time or break time if you need to.

As you can see you can use other features – such as adding a to do list however I tend to use it in its simplest form and that is what suits me – I just like getting on with it.

Ngā mihi

Image credits:

Sunset Home Office by Viktor Hanacek on Picjumbo

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