There was the question of what @downes and @gsiemens can do working together, and I don’t want to miss this opportunity for an early Christmas wish list 🙂
Over the years, both thinkers have emphasized different important things but without explicitly disagreeing with each other:
- Siemens still points to the conceptual level of Connectivism, creating coherence, and sensemaking;
- Downes has deep thoughts about how human recognition actually works;
- Siemens has ideas of how technology can support knowledge as an “outboard brain”, not just as logistics (storage and communication of information), and also about a learning analytics that is not patronizing the learner towards prescribed outcomes but keeps a human face;
- Downes emphasizes assessment as human recognition, and independent/ autonomous navigation within the subject matter rather than memorizing it.
What is needed is a learning solution that leverages all of the above aspects.
- A demonstration of how human recognition works differently than the AI competitor who is catching up rapidly.
- An illustration of how learning works when there is no predefined true or false outcome but a real understanding of complex conceptual networks is needed.
- A demonstration of how connectivist principles apply to concrete subject matter from sample knowledge domains,
- … to drill down to which structures lend themselves to non-linear, networked coverage,
- … and to study which kinds of learner preferences influence their interaction with the sample subject matter.
Personally, I am particularly curious about how the “outboard brain” can help to offload parts of the emerging conceptual network, to free precious working memory (of course because I work on a think tool). Or what non-patronizing analytics will find out about different learner and teacher bias towards speed and (a)synchronicity. But there is much more in the field of machine-supported human recognition.