Why We Teach

John G Corrigan

• Publisher: Castleflag Pty Ltd
• Year: 2020
• eBook ISBN: 9780994604477
• Topic: Education
• Subtopic: Education General

This book builds the case for systemic change in education through recognising that humans have two further innate drives beyond the drive for competence. Autonomy has been largely curbed in modern schooling and the drive towards meaning, although supported by school structures, is invisible to participants and the support provided is no longer adequate to current needs.

A systemic change in education means a change in the behaviours of educators to fully support all three innate drives. What these changes are is fully described and involve two distinct shifts in the educator-student relationship.

CHAPTER ONE

Introducing the case for change

Some teachers we remember for our whole lives because of the profound, positive impact that they have had on us. Elizabeth was teaching at a Sydney high school when I spoke with her in 2001. Close to retirement, Elizabeth was always surrounded by a gaggle of students who clearly wanted to spend time with her, and she clearly enjoyed spending time with them. When the subject of discipline arose during our conversation, Elizabeth observed, “In my thirty years of teaching I have never had a discipline problem, yet in this school there are two or three teachers whose sole objective on entering the classroom is to survive to the end of the lesson.¹”

Two questions immediately came to mind: what is Elizabeth doing that makes her so effective? And why aren’t the others doing it as well?

My conversation with Elizabeth initiated a long journey of discovery. After several months of research, the answer to the first question emerged: teachers like Elizabeth pay full attention to their students and, no matter what their students say or do, respond with kindness and compassion.

Through focus groups with students, surveys of students and teachers, and direct observation, I found that students respond to such behaviours with a desire not to disappoint or let down their teacher and to willingly do their best work.²

This research and analysis begged another question: why do these behaviours matter to students? Answering this was, on the surface, straightforward: they were meeting some need such that the students wanted the experience with the teacher to continue. But what was that need? Clearly, being listened to mattered to students, but why?

Why we teach addresses this question in service of a higher goal: to provide a compelling case for “the others to do it as well” and thereby progressively improve student learning and outcomes to such an extent that the very foundations of our education systems will be transformed to better match the needs of the twenty-first century.

A flagging system

For many years, our education systems have been perceived to be failing to deliver to their potential – not only in Australia but in many other countries³. Student learning and outcomes are stagnating (or declining relative to some other countries); and student mental health issues are measurably higher than in previous generations. Much effort has gone into resolving this situation; and, while it may have slowed these trends, it had certainly not reversed them.

For years, traditional practices have been stretched and systematised: curriculum has been standardised and broadened, pedagogy has become more inventive and inclusive. The burden on teachers has continually increased; although the horse is by no means deceased, we continue to flog it.

How did we get to this situation?

The underlying driver is a change in the work that is available in advanced societies.

Societies advance by solving problems. At one end of the spectrum are simple problems: those that can be readily resolved by performing a sequential set of steps. For example, if I have a dripping water tap, I can do a google search to find any number of YouTube videos that will show me a step-by-step process to resolve the problem. I just need to follow the steps and my problem is resolved. The steps to resolve simple problems are sequential, can be determined in advance, and can be completed by someone without high levels of skill.

By contrast, solving complicated problems requires prior experience, knowledge and some effort. Even so, each strand of effort also happens in sequential steps. An example of a complicated problem was articulated by President John F Kennedy when he said in 1961, “I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to the Earth.” Just over eight years later NASA landed the first humans on the moon (and a few days later brought them back safely to earth). The solution to a complicated problem is imaginable and a pathway to resolving it can be planned out. Yes, there will be setbacks, but no-one doubts that, with enough effort and application of knowledge, a solution will be found in a step-by-step manner.

For most of human history the work available has been algorithmic in nature – that is, sequential, step-by-step – with a great deal of it not requiring much, if any, significant knowledge or decision-making. Planting and gathering of crops, standing on a factory production line and working in a call centre are all examples. This type of work has relied on our physical strength and motor skills, low-level cognitive skills (for example following a script in a call centre), or low-level social skills (for example serving in a fast food restaurant).

Since the beginning of the Industrial Revolution and especially since the end of World War Two, more work has needed high levels of knowledge. A massive increase in the knowledge base has allowed more complicated problems to be defined and then solved. The productivity growth that led to rising standards of living in the postwar boom years continues today. Computers have massively amplified our ability to handle and share information, especially with the advent of the internet (first imagined in the 1960s), the launch of the World Wide Web in the 1990s and the phenomenal rise of the search engine in the 2000s.

Education systems have been organised since World War Two to develop most of the population for straightforward algorithmic work and an increasing minority to be capable of resolving complicated problems through applying high levels of knowledge.

Complex problems

But there are problems that are in another class altogether: complex problems, in which a complete solution – meaning a pathway to resolution – cannot be mapped out at the outset and may not be achievable at all, no matter how much effort is put in. Partial solutions may be all that is possible, and these may improve over time through repeated iterations of the search for a solution.

Climate change is a very topical example of a complex problem. We can imagine a world with a stable climate, unaffected by human action, but the pathway to get there is not obvious and the road to any type of solution (even partial) requires levels of collaboration, critical thinking and willingness to change deep-seated beliefs and behaviours that make this, under current conditions, an unsolvable problem. Another at a similar scale concerns what our societies do when fossil fuels run out. In both these cases we can imagine what a world might look like, but we cannot imagine the pathway to get there, if there is one.

Increasingly, the way we approach problems at smaller scales is to use an iterative methodology to define the problem and find a solution. An example is the concept of the Minimum Viable Product, described by Eric Ries in his book The Lean Startup⁴, where customer feedback is an essential part of developing a product or service. Instead of fully developing a product or service and then releasing it (which risks putting effort into something people don’t want), a version is released before being fully defined so it can be developed gradually to solve an, as yet, undefined or poorly defined need. The Agile movement also takes an adaptive approach. It began with improving software development⁵, but is now being used more widely to upgrade more general processes.

Twenty-first-century challenges

Societies advance by solving and routinising simple problems; this frees time and resources that can be used to work out how to solve and routinise more complicated problems. Eventually a point is reached where complex problems become salient and the skills required to resolve them – even partially – need to be developed.

We are now at that point, meaning that we need to develop at least a minority of young people with skills to address complex problems.

As compared to algorithmic work, this type of work is heuristic in nature – characterised by the need for iterative and collaborative solutions, creativity, tolerance for ambiguity and so on, often dubbed twenty-first-century skills. The table below provides one description of the component skills as proposed by the World Economic Forum in 2015.

Foundational Literacies

How students apply core skills to everyday tasks

1. Literacy

2. Numeracy

3. Scientific literacy

4. ICT literacy

5. Financial literacy

6. Cultural and civic literacy

Competencies

How students approach complex challenges

7. Critical thinking/problem-solving

8. Creativity

9. Communication

10. Collaboration

Character Qualities

How students approach their changing environment

11. Curiosity

12. Initiative

13. Persistence/grit

14. Adaptability

15. Leadership

16. Social and cultural awareness

Source: World Economic Forum, New Vision for Education (2015)

Twenty-first century skills as formulated and published by the World Economic Forum in 2015 in New Vision for Education. These are the skills needed for heuristic work which, of course, can also enhance the search for, and resolution of, simple and complicated problems.

Three key employment trends provide further compelling reasons to prepare students for heuristic work. Firstly, as far back as 2005, the consulting firm McKinsey & Company estimated that in the United States only 30 per cent of job growth then came from algorithmic work, while 70 per cent came from heuristic work.⁶

Secondly, since the 1990s, algorithmic work that had once been central to advanced economies has shifted gradually to being done overseas in countries with less developed economies and, therefore, less expensive workforces (a process known as “offshoring”). The main areas have been in simple ...

Table of contents

• Cover
• Title Page
• Copyright Page
• Dedication
• Contents
• Chapter 1: Introducing the case for change
• Chapter 2: How we grow
• Chapter 3: How schools support growth
• Chapter 4: Another way: Case study 1
• Chapter 5: Lifting the limits on growth
• Chapter 6: Counting the cost
• Chapter 7: Preparing young people for a future that’s here
• Chapter 8: The value of teachers
• Chapter 9: A student’s perspective: Case study 2
• Chapter 10: First, do no harm
• Chapter 11: Second, love is a better teacher
• Chapter 12: No downsides to being Enlightened
• Appendix: Descriptions of developmental levels in the STAGES model
• About the author
• Acknowledgements