Because an Enabling environment can help people do new things, it’s tempting to design environments which aspire to help novices enter a discipline, perhaps through simplified versions of its activities. This approach is usually quite limited. Powerful enabling environments usually arise as a byproduct of projects pursuing their own intrinsically meaningful purposes. Those purposes are usually best served by enabling experts. From that position, environments may also include structures to help novices pursue those same purposes.
When environments focus on enabling simplified versions of an activity, the goal often becomes skill-building itself. This tends to subvert its own purpose (see Educational objectives often subvert themselves). Environments designed for e.g. solving important problems in a field will almost automatically avoid this trap.
Representation design is one of the most important parts of designing enabling environments ::TODO write note::. Representations designed specifically for simplified versions of an activity usually won’t also enable expert practice. That means these representations have limited power. Worse: they’ll often be discontiguous with representations used by experts. But representations designed for experts often can service novices. When apprentices are building their skills in environments of authentic practice, they may use simplified representations. But because those representations were conceived in the context of expert practice, they’re often contiguous with expert representations so they can evolve smoothly. (See also Most dynamic representations developed for communication aren’t very enabling)
Research labs can be powerful enabling environments. They may include lots of structures which help junior scholars develop: reading groups, colloquiums, writing workshops, etc. When they’re working well, these activities are all about producing better research—not simply to build skills. Many of these activities (e.g. colloquiums) may actually be more important for experienced scholars. They were created for that purpose, and then perhaps additional structures were added to make them more accessible to junior faculty. Participation in these activities is participation in the discipline, for everyone. These activities grow with their participants.
Mathematica enables high-school students to visualize and manipulate the data from simple experiments. But more importantly, it helps professional scientists do their work more effectively. A tool designed primarily for the students probably wouldn’t help the pros do better science; this tool, designed for the pros, also helps the students do better science—and it grows with them to the frontiers of knowledge.
SimCity is fun—at least skill-building is not its primary purpose—but its representations encode many assumptions so fundamentally that they can’t be smoothly evolved to the representations of experts. If your goal is to do urban planning, SimCity will not help you much.
Likewise, Logo enables children to access ideas in differential geometry. This is wonderful! But professional geometers don’t seem to find Logo’s representations relevant for their research. So the children can only be said to be doing differential geometry in a limited sense because to do math is to ask and answer original questions, and this environment doesn’t seem to help much with that. Papert wasn’t interested in helping differential geometers first and foremost, and he didn’t necessarily have the expertise to do so, but it’s interesting to imagine an alternative Logo designed around powerful computational representations for geometers?
Email with Michael Nielsen, 2019/08/23. Re: Transcending the Primer
If you create Mathematica you’ll certainly be enabling people. But it’ll be secondary to doing kick-ass mathematics / theoretical physics.
Email with Michael Nielsen, 2019/09/03. Re: ❲FYI❳ Some notes on enabling environments / anti-educationalism
Scalability and non-scalability of ideas are interesting. Rocky’s Boots is still one of the best ever games that provide profound learning experiences. The extension of this to Robot Odyssey didn’t work because the logic and wires programming didn’t scale well enough — the bang per effort dropped off precipitously