Myth #6: Reusable Learning Objects (RLOs) help create effective training
A reusable learning object (RLO) is a self-contained, granular unit of instructional content that can be integrated within multiple courses. In the pursuit of developing digital training quickly and efficiently, learning professionals often over-index on the concept of RLOs without considering the risk of creating disjointed and decontextualized learning experiences.
Strong context is crucial for optimal learning and on-the-job performance improvement. Context-neutral templates, even if reusable, are generally ineffective. Is sacrificing the quality of digital training worth the trade-off for faster design and development? No. Poorly designed training offers little value, regardless of its ease of creation and maintenance.
Reality
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RLOs can be reused in different training situations.
Disadvantages:
Difficult to design training that will have the desired impact if RLOs are made generic for reuse and independent of context. Learning is a sophisticated experience that cannot be designed as simple lego blocks.
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RLOs are easier to use and more reliable when fully debugged. This translates to more rapid development and sometimes even better designs than authors might create.
Disadvantages:
Less flexible; instructional design is limited to the structure's options and capabilities.
For RLOs to work, many times instructional designers are forced to use a specific set of processes and tools, which can limit and dictate design choices. This makes creative work more difficult and tedious than the flexibility of using the most appropriate technology to build exactly what's called for.
Another issue to be aware of is when designers become more fixated on maximizing the use of RLOs than maximizing the impact of the learning experience.
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Using RLO templates and databases to store objects makes content easier to update, maintain, and translate.
Disadvantages
RLOs may have unnecessary overhead and complication compared to a simpler, custom-built function providing only what is needed.
Following are RLO characteristics and their pros and cons.
RLO characteristics
Smaller RLOs typically have low context specificity (context-neutral). Typically assembled with more RLOs and context-based elements to create the whole; resulting in a very disjointed and undesired learning experience.
Conversely, larger RLOs, especially those embracing the specific nuances of content and context, can provide effective learning activities but need considerable revision when used in alternative contexts. Larger RLOs tend to work against the fundamental purpose of RLOs across contexts, but can be beneficial within the domain for which they were created.
Smaller versus larger RLOs
What we know for sure is that contextual details and scenarios relevant to the target audience are not to be overlooked in the design of impactful and meaningful learning experience. That said, RLOs can have a place in speeding up development and maintenance issues when used selectively and judiciously for niche use cases. A balance between customization, variety, and some guidelines seems to be the sweet spot. Following are alternative mechanisms that can help accelerate the speed of development without sacrificing quality:
Gather data and insights on what good and poor training looks and feels like for your target audience. And then work backwards to find areas where you can be more efficient in your design and development processes.
Implement a Digital Asset Management (DAM) platform. A curated library of tag-based multimedia assets (with source files) for developers to customize for their specific audience, context, and learning objectives.
A content-level template feature to save a published learning activity, customize it, and reuse it for different purposes. This is currently available in the Articulate Rise tool.