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“We are moving to the world of the sons of Socrates, where dialogue and guidance are key competencies… a world where the capability to find information and turn it into knowledge at the point-of-need provides the key competitive advantage, where knowing the right people to ask the right questions of is more likely to lead to success than any amount of internally-held knowledge and skill.”
If Charles Jennings of the Internet Time Alliance is correct in this analysis, the learning revolution will not be delivered through formal courses or assessed through traditional evaluations. Rather, it will emerge from the “semantic web”: the web of meaning implicit in the language and logic of our digital lives. Web1.0 gave us connectivity and Web2.0 unleashed the content/connection avalanche. The evolution of Web3.0 promises context that moves us from clutter to meaning, from search to discovery, from hard taxonomies to more organic synthesis. Technology can now, perhaps ironically, humanize big data.
Towards deeper learning: Escaping EdTech's incrementalist trap
This move toward context and meaning may liberate us from EdTech’s incrementalist trap, which has tended to create the educational equivalent of Henry Ford’s “faster horse”: replicating, rather than transforming, old ways of learning.
Learning experts tell us that only a small percentage of what we know is learned through formal educational courses and assessments or professional training and development. The majority of our knowledge, they argue, comes from applied, informal, and social learning-by-doing (see here, here, and here). Imitation, curation, collaboration, real-world application, and community validation of whether we’ve learned what we think we’ve learned, are all at the heart of effective learning.
The big data revolution allows us, finally, to surface hard data from soft interactions such as search, collaboration, annotation, and curation. We can now measure informal social learning. Ironically, advances outside of education will likely most deeply revolutionize the classroom. The very same technology that improved ad targeting and search will allow us to watch for outcomes, then analyze the behaviors that preceded these outcomes. This new capacity for pattern detection in learning is positioning us well for an EdTech Revolution.
Collective capacity trumps individual achievements
Most importantly, Web3.0 is opening paths to collaborative intelligence. Isolated individual learning is increasingly irrelevant to organizational health, which is measured largely through group metrics. Today, public and private institutions live or die based on the efficiency, innovation, and impact of corporate efforts. In a context of accelerated change, increased competition, and scrum-based agile development processes, collective adaptive capacity becomes more important than individual achievement.
The development of this type of collective capacity is supported by content curation first-movers like Flipboard, Delicious, Pinterest and Polyvore; social knowledge education networks like Kifi, Bloomfire, Yellowdig and Edcast, and by products like SuccessFactors, SkillsSoft, and Curatr within enterprise. These represent a new breed of content-rich social learning networks that move beyond the institutional logic of the LMS, toward a more organic learning flow. Within this new genre of collaborative curation for learning, we at Declara are paying particular attention to the power of a dynamic and portable learner identity, which we surface through our CognitiveGraph. Declara analyzes content that is either hand-curated by users or automatically ingested by bots to create portfolios for collaboration. Through advances like natural language processing and machine learning, the CognitiveGraph analyzes how we collect, annotate in-text/in-video, discuss, and share. As we do this, data analytic engines can measure the variables that matter most in learning: whether or not we’re engaged in our learning and whether or not our learning has an impact on those interested in the same things we are. By understanding this, we can also understand how to recommend content and connections that can accelerate and deepen learning, inside or outside formal institutions.
Data generated through collaborative curation allows tools like Declara to link the learning process to meaningful performance outcomes. This will unleash a kind of personalization not possible with more structured, institutionally-rooted adaptive learning approaches.
For example, Davidson College, as part of its work with the Formation by Design consortium, is using collaborative curation to develop a Holistic Advisory capacity that will move students’ broader academic, professional, and personal aspirations to the center of advising. Faculty mentors collaborate with students on these portfolios to foster a deeper, more integral understanding of students’ journeys--both academic and developmental--through college and beyond.
Education Services Australia and Mexico’s SNTE, the second largest teachers’ union in the Western Hemisphere, have both developed national social learning platforms to support a collaborative model of teacher professional development. Collectively, more than 5,000 professional learning communities have developed hundreds of thousands of content objects on digitized national content stores. These are supporting teachers to create innovations in their practice that are relevant to their daily classroom realities. This form of applied, social learning-based professional development is building collective adaptive capacity and resilience at the grassroots, in what are large and diverse systems undergoing radical economic, political, and demographic transformation.
In each of these cases, the learner is given maximum control and the role of the instructor is reframed as a guide and mentor. This fosters a) curiosity and discovery through search and curation of open content, b) self-directed learning toward a shared goal, and c) networked learning communities where experts are validated by, rather than imposed on, the community.
Social semantic learning for open subject-matter networks
We at Declara, together with similarly-minded product developers at sister companies, are learning how semantic--meaning generating--approaches to big data analytics can create what Mark Oehlert, head of Networked Learning at Amazon, has called “subject matter networks” (in contrast to “subject matter experts”).
Through collaborative curation, these subject matter networks can generate the collective intelligence necessary to move institutions, and indeed society, forward. The communal and collaborative nature of this form of learning can demonstrate the power of new data analytic technologies and help us move beyond the formal logic of institutional curricula and political accountability goals. Rather than “manage” the learning process, we need to uncover it. In so doing, we can move toward more cognitively nuanced and empathic approaches to learning and link these more closely to performance tasks across a variety of settings.
Web 3.0 is unleashing a kind of “back to the future” innovation, the digital democratization of what élites have always practiced: deep learning through imitative apprenticeship, humanistic personalization via real-time observation, and mastery through crowdsourced validation. Silicon Valley is thus enabling us all to become the sons and daughters of Socrates.
