The fundamental difference between the Ripples@Work (R@W) model and traditional education is the vision of the world on which they are built and, following from this, the vision of the learner.
That is to say that traditional education and the R@W model are the product of different paradigms.
A ‘paradigm’ is like a matrix, a construct people create in accordance with their knowledge of the world. The originator of the ‘paradigm shift’ concept, the scientist and philosopher Thomas Kuhn, described the work of science within a specific paradigm as ‘normal science.’ ‘A normal scientist,’ as Kuhn writes, doesn’t aim to produce major substantive novelties but becomes an expert ‘puzzle-solver’.
So ‘normal’ scientists use knowledge and skills to work within a framework that has been predetermined. They invent some new moves—but still within that worldview, the paradigm they keep in mind. They discover some new things along the way, but again to fit into that given picture.
The central nineteenth-century view of the universe was of mechanical clockwork, as suggested by Isaac Newton. This paradigm thrived on the bedrock solidity of the interrelations between space and time, as they were then understood, with predetermined interpretations and answers. Every puzzle had ‘the assured existence of a solution,’ as Kuhn explained, comparing the process of finding the solution to assembling jigsaw puzzles with one fixed picture. In the same way, contemporary ‘normal’ education rests on the principle of a standardized, ‘one size fits all’ solution and has established one ‘correct’ method for arriving at it.
This ‘enslavement to clockwork regularity and chronology,’ as David Porush describes it, continues to permeate the whole fabric of contemporary education. The orderly class bells and standardized drills and testing, benchmarked knowledge and systemic pedagogies reinforce conformity of thinking and promote the growth of the dispassionate learner. It is still a common belief that the laws of exact sciences and the well-established order of things are the most important things in education, as they continue enabling us humans to maintain control over nature, social experiences, and our well-being.
The fixed order of three-dimensional space and the measured pace of time, however, were challenged by Einstein’s theory of relativity. The new understanding of the world brought us mind-boggling alterations, going far beyond the mechanical confinement of the old paradigm. The recognition of a new understanding of our surroundings and the relations between its elements forced us to redesign our mental construct. Philosopher Zygmunt Bauman described the historical period we live in as ‘liquid times’—where ‘change is the only permanence, and uncertainty the only certainty’.
In this ‘liquid modernity,’ as Bauman suggests, walking in step with the Zeitgeist means not chasing ‘the final stage of perfection’ but ‘an infinity of improvement […] forever becoming, avoiding completion …’
So the world is streaming forward, spreading in all directions, changing its contours as it picks up the pace.
‘Normal’ education, however, continues to teach a linear strategy of putting together given pieces into a well-defined vintage vision of reality. To justify its logic, normal education tricks itself and everyone around it into believing that in the ‘liquid times’ everyone who has learned how to put together a jigsaw puzzle will manage to create a ‘piece’ of stability for themselves.
Maybe such logic has a grain of truth in it. The problem, though, is that the puzzle itself, that one fixed solution, is not fixed anymore. It is in a continuous process of rapid change. Maybe, then, as another justification says, learning an algorithm for assembling one puzzle —even an outdated one—trains you to find a solution to a new one? Such cognitive operations require the engagement of higher-order thinking marked by the ability to connect the dots from disparate patterns, re-organize the elements, and remix the properties into new beneficial compositions—in one word, to synthesize. To explain this in terms of jigsaw puzzle assembling, it means not only knowing the algorithm but also being able to extract from it useful moves and elements, recombine them with the elements from another algorithm, and create an effective solution that wasn’t expected. Such cognitive skills are not easily measured by standardized testing and therefore in most cases are considered superfluous when sticking to prescribed curricula.
Ripples@Work aims to be a useful system for developing these skills. Its underlying logic says: ‘It is great to know the algorithm, but in a rapidly changing environment like ours it is more important to know how to modify existing knowledge of the facts and algorithms in such a way that it can be effectively applied to a new life situation.’
For example, how do you convert the dimensions of a blueprint into the dimensions of a full-size model? There is a fixed mathematical algorithm for doing this, but how can this fixed operation be manipulated to invent a method for calculating, for example, the proportions of an actual space where the full-scale model is about to be installed?
In fact, thinking about algorithms in a broader philosophical way—they are behind everything we do. The simplest example of an algorithm is a cake recipe. If you’re attentive to every word in the instructions and follow the steps as closely as possible, you are almost guaranteed to have a perfect result. ‘Almost’ is ever present because there is always a possibility of having an unlucky occurrence: an imperfect ingredient or an unexpected power cut, etc. Otherwise, following the cake recipe is a good example of ‘normal’ education as teaching an algorithm for jigsaw-puzzle assembling. And this is a great skill to have to enjoy your everyday life.
However, in our world which keeps churning out new machines that are getting faster and more efficient at following algorithms, we have to find smarter ways to apply human intelligence in the unavoidable contest with technology.
Perhaps what we need to be looking at is the understanding of the algorithm itself. What do we mean, for example, when we try to explain the operational logic of social media by saying, ‘That’s something to do with the algorithm…’ —as if there is some cryptic force regulating our interactions with digital logic that are altering our own behavior beyond our ability to comprehend?
In this light, the question is: What and how should we teach and learn in order not to lose control of our thoughts and feelings? Without having to be specialist wizards in computing, and while being aware that the records of our memories, decisions, and thoughts are voluntarily submitted to the ‘Cloud’, how can we protect our human identity?
If mathematical formulae can wield magic algorithms predicting the collapse of financial institutions, the chance of winning the lottery, and the most successful candidate for a job, do we really think that teaching students how to assemble jigsaw puzzles will help them to survive—let alone succeed—in a world controlled by increasingly powerful data-processing systems?
As we start thinking seriously about reinventing education, we first have to recognize the paradigm/groundwork on which our very conception of the universe and, consequently, education is built. Previously, the conception was represented as a clockwork mechanism with knowledge being encapsulated in the expertise of using algorithms. Therefore, education meant teaching and learning algorithms.
Nowadays, after a paradigm shift, we have to replace our image of the clockwork universe with that of a fast-moving ripplework of information. To navigate such a rapidly changing and unpredictable environment, individuals have to be efficient in recognizing alterations in the surroundings, re-adjusting their existing knowledge and adapting to rapid technological progress. This works best when a person has developed a repertoire of such competencies based on the cultivation of beneficial individual tendencies and the skills to act on them.
A person’s unique competencies and ability to apply them in various life situations constitute their individual agency (IA).
In the context of this article, an interesting correlation can be noticed between two central forces that new education has to address. They are: individual agency (IA) and artificial intelligence (AI). And the question that we have in front of us is: In what ways do we have to modify our approaches to the cultivation of IA so that we can create a mutually advantageous relationship between IA and exponentially advancing AI?
We could also say that the goal of the project of reinventing education is a continuous tension between the cultivation of individual agency and the advancement of artificial intelligence— IAAI.
 Porush, D. (1991). Fiction as Dissipative Structures: Prigogine’s Theory and Postmodernism’s Roadshow. In K. N. Hayles (Ed.), Chaos and Order: Complex Dynamics in Literature and Science [Kindle Version, loc. 1550]. The University of Chicago Press.