Crowd learning
Crowd learning is a term to describe collective learning at scale. Beyond collaboration and collective experience, the challenge in learning in collectives is often the issue of scale. Collective learning always works, but when the number of participants increases, different tactics and strategies must be deployed. Therefore, a scalable approach to collective and collaborative learning must be created, hence the term Crowd Learning.
Compared to collective learning, Crowd learning has the similar set of properties:
- Competition-based Learning
- Cooperation-based Learning
- Compositionality
- Scalability
In other words, the key feature of crowd learning is scalability through learning instrumentation and organizational practices.
Driven by Scalable Tools
To enable a crowd the learn together, a set of tools must be deployed to the participants. The technical architecture of the learning tools must be scalable in nature. Learning tools that are scalable must be economical in scale, and more importantly has built-in governance model, so that it can grow and evolve on its own, and lastly, it needs to allow more crowd to participate over time. This implies that learning does not only take place in human brains, but also taking place in the formation of how a crowd's social structures evolve. Defining a set of tools that enable a crowd to learn, the tool itself must be able to evolve accordingly. Recently, software engineering communities have come up with tools and organizational principles, sometimes called micro services, DevOps , and Agile development methodologies. These tools are great supportive infrastructures of highly technical organizations, but can hardly be managed by individuals or general purpose educational institutions. Crowd Learning aims at leveraging these open sourced tools and practices to enable crowds to learn.
Organized by Scalable Constitution
A critical condition for managing scalable organizations is to make rules explicit and make the verification of rule execution cheap and frequent. This requires both instrumentation, and proper design of rules. More importantly, rule should have a similar format, so that verification can be done with rigor and reuse. That is a reason why XLP focuses on the usage of Logic Model.