Placing OER (open educational resources) online without optimizing their components to signal is like expecting a single cell or group of cells to perform their role in isolation. Yet educators and subject experts put non-signaling lesson plans, courses, and curricula into the internet all the time. This was not surprising in the early days of the internet: educators were used to analog materials like textbooks, lesson plans, and and the separation of experts by geography. But the best knowledge for learning is now online, and education is far overdue in utilizing the cognitive connectivity of the internet.
What the e-Commerce world calls SEO (search engine optimization) is one way to give resources signals they can use to reach out to related stuff online. For OER, SEO is vital, but just a first step in the creation of signaling pathways. There are other very effective signal methods inherent in learning resources including: experts linking to (creating a network with) other OER they respect, landing pages that point (signal toward) excellent OER, and RSS-type signals that roll out expertise as it is published.
So would this signaling stuff work in a real network? Yes, and molecular biology is a very compelling model. The Wikipedia article on Cell Signaling (from which the above illustration is taken) explains:
Traditional work in biology has focused on studying individual parts of cell signaling pathways. Systems biology research helps us to understand the underlying structure of cell signaling networks and how changes in these networks may affect the transmission and flow of information. Such networks are complex systems in their organization and may exhibit a number of emergent properties . . . .
The following excerpt is from a current article in Molecular Systems Biology. Click on the small illustration from the article at the right to see a chart of network relationships — which are the real world way in which life itself works. Instead of bundling a course or textbook in a pdf and tossing it online, how can we instead optimize the knowledge within the OER with some of these principles in the excerpt that follows by which our cells keep us alive and keep us thinking?
Despite their value in aggregating diverse and scattered information, protein networks inferred purely from data and those assembled from the literature suffer from significant and complementary weaknesses: reverse-engineered networks ignore a wealth of existing mechanistic information about individual proteins and reaction intermediates, whereas literature-based networks are too disconnected from functional data to encode input–output relationships. Thus, even the most comprehensive interactomes do not capture the logic of cellular biochemistry and—critically—cannot predict the responses of cells to specific biological stimuli. Two nodes in a node–edge graph might have a positive effect on a downstream node, but a graph alone cannot specify whether the target is active when only one upstream node is active or whether both must be on.
