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3 Evidence-Based Training Methods From Ruth Clark’s Updated Meta-Analysis

Monday, April 20, 2020

Ruth Colvin Clark is a specialist in instructional design and technical training who focuses on bridging academic research and practitioner application in instructional methods. She holds a doctorate in the field and is president of Clark Training & Consulting. I reviewed her third edition of Evidence-Based Training Methods: A Guide for Training Professionals.

Here’s the latest research on a few trending instructional methods and evidence-based tips you can implement into your program design.

Animations That Don’t Overload

With evolving content authoring tools and software with graphic capabilities, designers can implement animations into their learning solutions more than ever. While animations may seem like a superior option, Clark reveals that they’re not always the best learning solution. In studies with lessons on toilet-flushing, lightning formation, automotive braking, and wave formation, still visuals led to learning that was just as good or better than with animations. Here are a few things to consider when incorporating animations into your instruction:

Know When to Use Still Visuals Versus Animations: Still visuals can be reviewed and processed at a learner’s preferred pace, whereas the rapid visual information of an animation can quickly overload memory capacity. Clark advises designers use animations for procedures (such as solving a 3D puzzle) and dynamic changes (such as acceleration). Still visuals can be efficient tools when the learning goal teaches a process.

Perspective Matters: Training content that teaches how to perform complex tasks should prioritize animations with an over-the-shoulder view. Imagine learning how to tie a knot. While instructive animations are often designed with a third-person perspective, learning results are higher when using this perspective.

Chunk the Content: Clark cites research that found benefits when learning content with animation was broken up into smaller segments, to be accessed at a learner’s individual pace. This approach helps reduce the cognitive load for learners who have little to no prior knowledge.

Present Visual Cues: Include visual cues to reduce cognitive load. For example, in a process with sequential steps, you can incorporate different colors as cues to show each task. Highlighting can be used as a cue as well. Studies showed this strategy works successfully in diagrams to differentiate specific elements within a larger whole.


Designing Effective Games

Gamification continues to trend in popularity, and many organizations have already implemented digital games into their workplace learning goals. Clark defines a learning game as entertaining enough to motivate play and educational enough to promote learning. Consider these recommendations when incorporating games into your learning content:

Align Engagement to the Learning Objective: Game design should ensure that goals and associated activity are connected to the learning objective. Core features such as high interactivity and responsiveness, specific challenging goals, and rules and constraints must be driven by the learning objective for successful outcomes.

Offer Repeated Playtime: When a game is only played once, learning content has about the same effectiveness as a standard tutorial. Ensure your design allows for multiple playing sessions, particularly if a goal in to ensure automaticity.

Simple Is Better: This is another one of those counterintuitive design methods Clark reveals to be more valuable. Simple visuals, such as schematic or cartoon graphics, are more effective for learning than games with highly realistic visuals, such as photographs or high-fidelity computer-generated graphics.

Conversational Audio: A personalized approach performs better than a formalized tone. Use first-person and second-person constructions (such as I, you, and we). Explanations are more effective in audio format rather than text versions. An audio format enables learners to leverage the limited capacity of their working memory by sending instructions to the auditory center instead of the already occupied visual center.


Optimizing Feedback

Feedback is a powerful tool facilitators, managers, and peers can use to improve learning outcomes and performance. Not all feedback is created equal, however, and the level of effectiveness depends on the type provided—how it directs the learner’s attention and how it focuses on the learner’s goal. Clark provides an entire new chapter dedicated to insights on feedback to optimize learning and motivation. Here are a few key takeaways for successful feedback:

Include Explanations: When giving feedback, incorporate an explanation. For example, if you’re giving feedback on the accuracy of a multiple-choice question, learners benefit from a brief explanation about why their response was correct. While this may add more time, Clark points out the learner’s outcomes are worth the investment.

Offer Strategies for Improvement: When a learner’s response is incorrect or not entirely correct, one of the most valuable ways to provide feedback is to provide guidance about how to improve for the next time. Share the criteria for what a successful outcome looks like and provide advice for progress.

Avoid Generic Praise: Providing feedback such as “Good job!” or “Wow, that was awesome!” has not been shown to improve learning. It is far more effective to pair praise with specific aspects of the task the learner has completed.

Include Peer Feedback: You can leverage peer feedback by providing guidelines about what feedback and success should look like. By offering criteria for success and conditions for successful feedback, you align expectations for you and your learners.

Affordability and Accessibility

One of the greatest takeaways from Clark’s findings is that research often validates simpler design, interface, and animations as more effective learning tools. Going simple can create a more accessible price point to implement training solutions too. Many pages of the book illustrate relatively inexpensive methods that are simple yet effective. Bottom line: Professionals interested in applying the latest and greatest in learning science don’t have to go over budget to produce effective, evidence-informed training that will yield successful learning outcomes.

About the Author

Alexandria Clapp is the content manager for Learning Technologies and Sciences at ATD. She’s responsible for creating and curating content including articles, webinars, books, conferences, and more for talent development professionals. She also hosts the ATD Accidental Trainer podcast.

Prior to joining ATD, Alexandria worked at a research and development firm that applied cognitive science to improve learning outcomes. She trained partner organizations to deliver cognitive enhancement programs and specializes in leadership development, learning and memory, instructional design, and virtual coaching. Alexandria is a credentialed ATD Master Trainer and Expert Coach. 

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Hi John, The main literary influence for this article was Ruth Colvin Clark's Evidence Based Training Methods. She refers to almost 300 different peer reviewed research papers in her book.
Headwinds for the "simplicity approach? This may require auditing and adjusting current digital games to remove or change less effective visuals that cause irrelevant cognitive load.
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Dear Dr Clapp,
What are your main literary influences on this outlook? What are the headwinds to the simplicity approach?
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Great article! Very interesting insights, especially the point about simple visuals being more effective for learning than games with highly realistic visuals.
I would've assumed highly realistic visuals were more effective. We use VR to train users and we've seen remarkable results from simple learning visuals as well. However, it's important that mechanics and actions should reflect reality.
Evidenced-based training methods are important for us to prove the effectiveness of VR.
Thank you! And thanks for sharing your experience using simple visuals within your VR training, Albert!
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