Affordances is a complex term. Originally coined by Gibson, and popularized by Norman, it’s been largely used in terms of designing interfaces. Yet, it’s easy to misinterpret. I may have been guilty myself! In the past, I used it as a way to characterize technologies. Which isn’t really the intent, as it’s about sensory perception and action. So maybe I should explain what I mean, so you don’t think I’m misusing affordances.
To be clear, in interface design, it’s about the affordances you can perceive. If something looks like it can slide (e.g. a scrollbar), it lets you know you might be able to move the target of a related window in a field. Similarly a button affords pushing. One of the complaints about touch screens is that as people work to overload more functions on gestures. There might be affordances you can’t perceive: does a two-fingered swipe do anything differently than a single-finger swipe?
In my case, I’m talking more about what a technology supports. In my analysis of virtual worlds and mobile devices, I was looking to see what their core capabilities are, and so what we might naturally do with them. Similarly with media, what are their core natures?
So, for instance, an LMS’s core affordance is managing courses. Video captures dynamic context. You might be able to do course management with a spreadsheet and some elbow grease, or you can mimic video with a series of static shots (think: Ken Burns) and narration, but the purpose-designed tool is likely going to be better. There are tradeoffs. You can graft on capabilities to a core, still an LMS won’t naturally serve as a resource repository or social media platform.
It’s an analytical tool, in my mind. You should end up asking: what’s the DNA? For example, you can match the time affordance of different mobile devices to the task. You can determine whether you need a virtual world or VR based upon whether you truly need visual or sensory immersion, action, and social (versus the tradeoffs of cost and cognitive overhead).
With an affordance perspective, you can make inferences about technologies. For instance, LXPs are really (sometimes smart) portals. AI (artificial intelligence)’s best application is IA (intelligence augmentation). AR’s natural niche, like mobile, is performance support. This isn’t to say that each can’t be repurposed in useful ways. AR has the potential to annotate the world. LXPs can be learning guides for those beyond novice stage. AI can serve in particular ways like auto-content parsing (more an automation than an augmentation). Etc.
My intent is that this way of thinking helps us short-circuit that age-old problem that we use new technologies first in ways that mimic old technologies (the old cliche of tv starting out by broadcasting radio shows). It’s a way to generate your own hype curve for technologies: over-enthusiasm leading to overuse, disappointment, and rebirth leveraging the core affordances. Maybe there’s a better word, and I’ve been misusing affordances, but I think the concept is useful. I welcome your thoughts.
Prompted by prep for the advanced seminar on instructional tech for the upcoming Learning & Development Conference.