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Most Examples of Deep Learning Are Not Deep Enough

Deep Learning.jpg

Today's guest blog is written by Michael Fullan, an international expert on leadership and school systems. 

Part Two

In Part One of this pair of blogs—The Unity of the Human Race: Our Precarious FutureI made the case that we are getting perilously close to planet-destroying forces that could unwittingly and to a certain extent inevitably make humans extinct. The only thing that could save us is a global movement that increases the "unity of the human race." I don't mean that we would become homogeneous but that certain developments would favor the proliferation and strengths of small groups with the necessity of connecting more widely (globally) in order to benefit each other and the universe as a whole. I am going to call this "universal connected autonomy."

To do this, we need to transform public education.

Deep Learning of a certain nature will be required to save society. I start with a system solution for the greatest problem facing humanity: the growing and towering problem of inequity across the globe (a more fundamental problem than climate change because solving the equity problem will generate the climatological solution). The solution can be expressed in the following system proposition for education:

The fate of equity and the fate of excellence for all are inextricably bound.
Any full-on attack on inequity without attending to excellence for all will fail, or vice versa (excellence as the solution). This is a system certainty. This partly explains why equity, even though endorsed by every government in the U.K. and the U.S. since World War II (present company excluded) has increasingly lost ground to inequity. The one-sided solution of intensifying attention to equity does not and cannot work. (Admittedly, one could argue that the governments weren't serious about the agenda or that the power relations thwarted any chance of real change, but my argument is more basic: The strategy could never work because it did not address the whole system with deep learning.) We also see the self-defeating cycle when some politicians and middle/upper-class members think "we have been paying too much attention to equity, and too little to excellence; we need more programs for the gifted." And so it goes.

Like others, I see galloping inequity as the number-one problem facing society. The solution is to attack inequity with deep learning for all so that all learners benefit. The problem is that while deep learning by name is increasingly popular, most examples fail to go deep at all. In brief, the argument in this blog can be stated as:

Deep Learning of a certain kind has the potential for addressing equity and excellence in tandem, yet most forms of deep learning so far are not deep enough.
Deep learning is 'Engaging the world Changing the world' (actually, engaging the world in order to understand it—learn about it—and in the course of doing this, to change it, and yourself, for the better). My claim is that this stance is the only way to live and learn when society is as complex as we have become. Young people of all ages want to live and learn this way— a phenomenal potential that favors the possibilities. Put another way, proactive learning in relation to the world would be the way most students would want to learn even if the societal agenda was not at stake. We believe that the majority of students, if given the chance, desire the deep-learning agenda, which can be succinctly stated as: becoming good at learning and good at life.

Meanwhile, the present education system serves only a very small minority of students across all SES categories. Heather Malin, for example, finds in Teaching for purpose (2018) that only about 24% of high school seniors "have identified and are pursuing a purpose for their life" (p. 1). It is true that minority students and students of extreme poverty are much worse off than others, but: a) the majority of all students are not doing well, and b) any solution will have to make everyone better off while closing the gap.

When it comes to deep learning, we are finding: 

That deep learning helps all students including (one could say especially) the disaffected across all SES categories. 
There has been a deep learning movement in the U.S. for the past decade. Some progress has been made, but it has fallen woefully short of what is needed. First, there are the instances of downright shallow learning, despite the attempt to go deeper. In a brilliant investigation, Jal Metha and Sara Fine set out to study secondary schools that had been nominated as good examples of deep learning. After finding almost nothing, they returned to dig deeper and found a few pockets of deep learning in a handful of classrooms and on the periphery (for example, after-hours clubs and theatre). Metha and Fine titled their book, In Search of Deep Learning (2019). They and others are still searching.

Other work has been developing under the label of deep learning consisting of greater domain knowledge (science, math, literacy), standards, pedagogy derived from learning science, assessment, and interventions for continuous improvement—all with explicit commitments to both equity and excellence. We see this in the recent policy developments in California. This is doing some good, including generating some improvement with those students who are poor and minority. But these gains are modest and fragile and will never, can never, be good enough unless the system changes to benefit all. 

The core of the recent deep-learning developments referred to in the previous paragraph essentially focuses on "critical thinking, collaboration, and communication." While these new developments can help those students who are most troubled, they don't help them very much! That is, what they learn is not "sticky" (is not lasting) and does not help them in complex life scenarios. Academic equity gains will not be strong enough to withstand the increasingly unequal society that we live in. At the same time, I am suggesting that this form of deep learning does not even address the needs of students who traditionally do well. In short, the old system, even if buttressed by some aspects of deep learning, does not work to produce students who are good at learning and good at life. 

The learning solution has to be—in a word, deeper. Our version of this is the 6Cs—not three. We have added creativity, character, and citizenship to form an integrative set of 6. There have been some independent efforts to develop greater creativity, or character, or citizenship, but these have been typically separate endeavors, and as such, have failed to gain widespread traction. Although recently, SEL (Socio-Emotional Learning) programs have supplemented academic learning, they have not been integrated as learning for life. My point with respect to addressing the fundamental problems of the world as seen in blog one is that we need all 6Cs working in concert. The 6 provide an integrative force for learning and action that benefits the individual, the group, and potentially, society as a whole.

The 6Cs have to be mobilized and supported, and we have done so with a comprehensive model that operationalizes i) the 6Cs in action ii) four dimensions of a learning environment (pedagogical partnerships, learning partnerships, learning environments, leveraging digital), and iii) support systems at the local, regional, and system levels. We are actively pursuing this very agenda in partnership with school systems around the world (see Fullan, Quinn, & McEachen, 2018, and Quinn et al, 2020).

We are discovering two things: Nearly all students are attracted to the learning mode of "engage the world change the world." Second, and potentially a dramatic breakthrough, this same framework is the best way of addressing inequity, because deep learning is especially powerful for those students who are most alienated by the status quo. These students respond well to the passion of the deep-learning agenda that the 6Cs and the framework encompass. 

Recall from blog one David Sloan Wilson's point that "modern evolutional theory tells us that goodness can evolve, but only when special conditions are met" (italics in original). Thus goodness is not guaranteed and could go either way; to flip it, badness can thrive when other special conditions evolve. The qualification: "only when special conditions exist" is key. Our version of deep learning, or something comparable, fosters some of those "special conditions."

My argument in the two blogs is that we need focused and sustained political action, combined with the mobilization of young learners en masse across the globe—learners who are steeped in proactive deep learning via the 6Cs. This political action and deep learning will need to enable and develop scores of small communities of learners who support each other internally, while seeking global interaction that results in greater understanding and mutual commitment to building a more sustainable and creative planet. This is what is meant by the "unity of the human race"—to co-exist and develop taking full advantage of evolutionary forces and the unlimited creativity and imagination of the human spirit. 

Following Sloan Wilson, we might have the rest of this century to do this—80 years—(after all, we are talking about unifying all of humanity). Given the starting point—that we are going in the wrong direction and losing ground rapidly—we must start immediately to reverse our fortunes. The fact that young people want to live and learn this way, with high risks at stake, augurs well for our evolutionary potential.

Once again, aggressive attacks on inequity with a social-justice base by itself—as much as I agree with and help with this very agenda—will never work because it is not systemic. Dignity for all is the holy grail of system evolution (Wilson & Pickett, 2019, and Arnade, 2019). Sloan Wilson said that "neuroscientists are relentlessly bottom up" (p. 162) because they respect how things may evolve in mysterious and unpredictable ways. Deep learning at its best stimulates, liberates, and tries to capture and project bottom-up ideas and trends. 

We need political action on the very agenda that I have tried to capture across the two blogs. It will be a mixture of top-down and bottom-up forces. In essence, we need education to help us create a new social order. Right now, most examples of deep learning are neither deep enough nor widespread enough to make much of a difference. Deep learning must become the evolutionary force we so badly need at this particular moment in history. 

Michael Fullan, O.C., is the global leadership director, New Pedagogies for Deep Learning and a worldwide authority on educational reform with a mandate of helping to achieve the moral purpose of all children learning. A former dean of the Ontario Institute for Studies in Education (OISE) of the University of Toronto, Michael advises policymakers and local leaders around the world to provide leadership in education. Michael received the Order of Canada in December 2012. He holds honorary doctorates from several universities in North America and abroad. 

For more information from our team, see: Fullan, Quinn and McEachen, Deep Learning: Engage the World Change the World (Corwin  2018), and Dive Into Deep Learning: Tools for Engagement. Quinn, McEachen, Fullan, Gardner & Drummy, Corwin, in press).

Arnade, C. (2019) Dignity New York: Sentinel, Penguin.

Fullan, M., Quinn, J. & McEachen (2018). Deep learning: Engage the world Change the world. Thousand Oaks, CA: Corwin Press.

Malin, H.(2018). Teaching for purpose: Preparing students for lives of meaning. Cambridge, Mass.: Harvard Education Press. 

Metha, J & Fine, S. (2019) In search of deep learning. Cambridge: Harvard University Press.

Quinn, J. McEachen, J. Fullan, M. Gardner, M. & Drummy, M. (2020) Dive into deep learning: Tools for engagement. Thousand Oaks, CA: Corwin Press.  

Wilson, R. & Pickett, K. (2019). The inner level: How more equal societies reduce stress, restore sanity, and improve everyone's well-being. UK: Penguin Press.

Photo courtesy of Shutterstock. 

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