Instructional design

ID Models (3/5): Rapid Prototyping

A good instructional design model should help us produce output that meets the objectives of all stakeholders. As we saw in the previous post, ADDIE in its classic form too often does not achieve this because it requires the instructional designer and developer to get everything right first time, without ongoing feedback from stakeholders and the chance to change direction.

Today, with the advent of Rapid Prototyping, from very early on in the process instructional designers and developers create quick and imperfect prototypes and share them with stakeholders, including sponsors, SMEs and learners. Working fast is encouraged. For example, instead of building a complex interaction in Articulate Storyline a quick paper mock-up might be made, allowing the instructional designer to observer learners (or at least colleagues or stakeholders) using the interaction and evaluate its effectiveness, before putting days of work into development. The interaction might change (and improve) recognisably several times before it finally gets created.

In Rapid Prototyping the Design, Development and Evaluation stages of ADDIE are intermingled. The feedback that flows throughout the process means that the final output is many times more likely to meet the everyone’s needs than with classic ADDIE’s “right first time” approach. Instructional designers and developers feel able to innovate and try out new things, knowing that anything that doesn’t work can be ditched. Stakeholders understand better what is being developed when they can get their hands on it, rather than trying to imagine it from a storyboard.

On the down side though, the complexity of moving an inexperienced team back and forth through the process and trying to coordinate everyone’s schedules, work and feedback means it is challenging for inexperienced teams to use Rapid Prototyping.

To tackle this complexity, Allen Associates developed a Rapid Prototyping model that combines the flexibility of prototyping with a clear structure. It’s the next model in our series, the Successive Approximation Model (SAM).