#nonlinear-learning

#nonlinear-learning

The first event is a roundtable on "Zhuangzi: Fate, Desires, Transformation" on April 6th at 9:00am Beijing time.

Real change rarely happens through debate or persuasion. Instead, transformation grows out of relationships, shared struggle, cognitive dissonance, and practice. Together, Kelly and Lewis explore what organizers can learn from the science of neuroplasticity, the role of rupture and confrontation, and why movements need to focus less on 'changing minds' and more on creating conditions where people can unlearn harmful beliefs and step into collective action.

Being thrown into a group of new strangers each and every year, as is typical in so many American public school systems, is deeply evolutionarily unnatural. Under ancestral conditions, humans did not encounter strangers with nearly the same frequency that we experience now. And guess what? Humans have an entirely different way of interacting with strangers (including appropriate levels of hesitation and skepticism) than we have when interacting with others whom we know well.

I'll be talking about holistic engineering or the practice of factoring in your technical decisions, designs, strategies, all the non-technical factors that are actually forces that influence your organic socio-technical problem space. As much as you can see in this canyon how natural forces have influenced the shape of the earth, so you can see the color. You can see all the different layers.

At its core, the curve of learning represents how quickly proficiency increases through experience. The learning curve theory shows that improvement is not linear. At first, people might feel confused and make mistakes, which can slow progress. After some time, though, they start to improve faster. Eventually, as they approach mastery, progress may slow again.

Collective learning is how a group or system creates, improves, and keeps knowledge. This knowledge lasts beyond any one person or cohort. That is the most practical collective learning definition, because it shifts the focus away from individuals and toward the learning system itself.

For years, Learning and Development teams have been told they have a technology problem. If only they implemented the right LMS. If only they added an LXP. If only they layered analytics on top. If only they automated a few more workflows. Yet despite an ever-growing learning tech stack, most L&D teams feel more overwhelmed than ever. Training requests pile up. Programs take months to launch.

Today's eLearning solutions use algorithms for many things, including recommendations for courses, tags for skills, scores for completions, heat maps, and metrics for engagement levels. Anyone interested in eLearning sees learning in new ways; all of those ways are measurable, sortable, and optimizable. We seem to have come a long way in terms of learning. Through data-driven learning, one can increase efficiency, personalize learning, and scale it up.

When we look more closely at how and why organizations actually invest in these systems, we can see that the popularity of adaptive learning has far less to do with pedagogical ambition and far more to do with operational pressure. Understanding this gap between how adaptive learning is marketed and how it is used in practice is critical for organizations trying to decide whether it is the right approach for their learning needs.