Philipp asks how we can scale learning, and discusses two axes that P2PU is pursuing: peer-based learning, and an emphasis on challenges over courses.
Imagine the learning ecosystem as a graph, where learning challenges are nodes, and the edges between nodes represent the various ways these challenges can be combined. this is essentially a break down of the monolithic course concept into smaller, atomic components. by doing so, we increase the possible ways these components can be combined, leading to a more diverse ecosystem of learning opportunities.
in peer-based learning, the more traditional constraints on who can be a so-called ‘teacher’ are removed. since we are creating a graph representing pathways to learning, these peers represent additional nodes in our graph. they also increase the combinatorial possibilities in the system of learning.
manifesting this kind of diverse, rich structure, does not happen via monolithic design (no matter how well intentioned). it happens via open standards and platforms: composable components that share interfaces, and can be combined in
arbitrary and surprising ways. just to emphasize this, if we consider our graph to be completely unstructured (remember, the nodes in our graph are made up of learners and content), the number of possible subsets of those components is the powerset of all the nodes, or 2n, where n is the number of nodes. (never mind that for each subset, one can also enumerate all possible fully connected combinations of those components… but i digress). go ahead, calculate 2n where n is the number of courses currently available on P2PU. It’s clearly a simplification, but you get the point: a lot of
possibilities. learning everywhere! hoorah.
now, traditional learning environments have clearly “scaled.” so what do we mean when we talk about scalability of peer-based learning? let me offer a working definition, that is: to reach and benefit arbitrarily many people. in traditional learning environments, the delivery mechanism has scaled, but the relevance and effectiveness have not. our giant learning graph of challenges and peers provides a notion of what it means for the experience of individually
optimized learning to scale.
can heterogeneity be standardized?
us humans thrive on recognition and feedback. the compliment (actually precursor) to P2PU’s work on challenges, has been badges. simple, atomic symbols of recognition and accomplishment. one of the big questions around badges has been, are they standardized? if not, how can they be meaningful? and if so, are we not just becoming the very thing we sought to change?
unfortunately, the diversity of standardization doesn’t scale with the diversity of learning mechanisms or individuals. standardization scales by selecting a set of activities or pathways that are recognized and meaningful to a broad group of people. we’ve seen that the less dependencies we can institute between components in the graph, the greater the heterogeneity that can be achieved. but by their nature, standardized recognition systems provide inventive to follow well-trodden pathways. these pathways impose a structure that is otherwise not present in our free-form graph. structure reduces the combinatorial possibilities.
another draw of standardization is that it provides encapsulation. it’s a way of putting a wrapper around a body of effort, and evaluating it en mass. we often assume that people seek standardization because they desire a shared or common assessment. but through encapsulation, standardization also simplifies evaluation, and makes that evaluation portable. but how does one know that a given standardized mechanism has been applied correctly? that it actually is standardized? typically, this is through oversight and enforcement mechanisms. and those mechanisms have overhead. the benefit has to outweigh the cost for that overhead to be worth the effort. which, again, keeps the tension on that system to reduce diversity (reduce overhead).
these questions do more than highlight open problems to be solved. they lay bare a fundamental tension, between developing an arbitrarily heterogeneous ecosystem of learning, educational practices, goals, and opportunities; and our
desire for meaningful recognition. if we provide infinitely many ways to recognize peoples’ efforts, the recognition loses its meaning. if we provide a few focused ones, we constrain what and how people can achieve.
many independent learner types don’t seek recognition or credentials for their learning efforts, and it’s easy to dismiss the need for credentials and recognition as valuable primarily in job-oriented environments. i think there’s a few reasons to consider the role of recognition beyond this:
- one goal of organizations like P2PU is to show that there are alternatives to “the system” for those who have not thrived in it (or perhaps who have, but who seek other ways). part of succeeding in this goal is providing a way to
recognize their approach, whatever it may be.
- as humans, we thrive off of feedback and recognition.
- as the world, and its needs, become more diverse, so do the various skills and ideas we need to address those needs. we live in a post-industrial world where innovation and creativity are highly sought-after skills (at least in more economically prosperous countries). understanding how to design a system which systematically incentivizes (and, as a function of that, recognizes) disruptive ideas, is a broadly important goal. to give just one example, science funding agencies are constantly struggling with this question in terms of how to select grantees.
unfortunately, i don’t know the answer (sorry if you’ve slogged through this far in hopes I did). IMHO, such tensions do not get “solved,” but rather understood and leveraged. for P2PU’s approach to truly scale, i believe we need to put some serious brainpower behind this question: how can we recognize and reward arbitrarily diverse learning without constraining what that learning is or how one gets there? and how do we design these rewards and incentives without
pre-supposing we have any clue what the outputs will look like?