Imagine a trainer at the front of the room responding to a participant’s comment by saying nothing more than “You’re right!” or “Incorrect.” Imagine this happening over and over again.
Even though it seems futile, this is one of the most common types of feedback we use in e-learning courses to respond to user actions
and answers. In fact, many authoring tools come with these vacuous statements as their default response.
If we’re going after higher-order thinking and maximum learning transfer, then we’re giving up a golden opportunity when we forgo real feedback and instead resort to “correct” and “incorrect.” We need to find ways to close the feedback loop.
There are many strategies for providing feedback, depending on the context and type of instruction, the objectives of the learning activity, and the audience’s level of expertise. Let’s look at some of your options for providing feedback that is sufficiently informative and moves the learner forward.
Analog World: Some of the most ideal feedback replicates what happens in the analog world. In simulations and virtual worlds, learners are given a chance to explore, manipulate and practice so they can learn in a safe environment.
In a reasonably accurate simulation, feedback occurs naturally as the result of an action. For example, in a driving simulation, turning a simulated steering wheel to the right appears to turn the car to the right. That’s feedback.
Digital World: Then again, sometimes our simulations replicate the digital world, as in a software simulation. Too often, software simulations are completely canned, so that users can only take one action. If possible, allow for more interactivity so learners can try out the simulated software a bit to better understand how to perform a task. With more flexibility, the feedback simulates the real (digital) world. For example, when the learner clicks a menu item, the menu displays. That’s real world feedback.
We often don’t have the budget for highly robust or complex simulations, so let’s look at some other options.
During interactions, learners might require several tries in order to clarify a learning point or fine-tune their discriminatory skills. If this is the type of interaction you’re designing, then valuable hints and cues are a good way to assist learners without completely taking away the benefit of making errors.
When using stories and scenarios in e-learning, it’s natural to take learners down a different path depending on their response. That’s a type of feedback. For example, in an emergency medical training course, the choice of one drug results in stabilizing a patient whereas the choice of another drug results in dangerously high blood-pressure levels.
The difficult part from a design and development perspective is to determine how many paths to design and implement. A simpler approach is to design short forks in the road and then merge the two or three paths back together. It’s one way to avoid a huge design and development effort.
If you don’t want to build many paths in a story or scenario, simply provide unique feedback specific to each response. In the medical scenario above, an incorrect choice would produce feedback about the danger of the selected drug and request that the learner select another one. Even though context-sensitive feedback is not as compelling as branching, it is more engaging than a facts-only exercise and probably more beneficial to learning.
I think that context-sensitive feedback should be the minimal type of feedback we provide as learning experience designers. This means that we should always provide unique feedback that is specific to each response or action the learner takes. It works in multiple-choice tests as well as games, stories and virtual environments.
Although there is no one type of feedback in learning games, gaining points or completing a challenge is motivating. This type of feedback acts as an incentive to continue playing the game and learning.
As Karl Kapp writes in The Gamification of Learning and Instruction, “A player gets caught up in playing a game because the instant feedback and constant interaction are related to the challenge of the game, which is defined by the rules, which all work within the system to provoke an emotional reaction and, finally, result in a quantifiable outcome within an abstract version of a larger system.”
In a training context, you can use collaborative and social media tools to give and receive peer feedback from colleagues. For example, if you were designing a course for new coaches, you could set up a Facebook Group page for discussion. Then request that participants write about how they would handle a specific coaching situation.
The participants would comment on how each coach managed the fictitious problem and everyone would learn in the process. The added bonus here is that the act of critiquing and commenting can help reviewers themselves, according to one study where undergraduate students critiqued each others writing. (Cho and Cho, 2011)
See Social Media for Trainers by Jane Bozarth for more ways to use social media for learning.
You can apply explanatory feedback to any learning experience in which errors are caused by misconceptions or a lack of knowledge. If your design has frequent opportunities for learners to respond, then you can catch and remediate misconceptions as the learner is constructing meaning.
In particular, explanatory feedback, rather than corrective feedback, is a good choice for discovery learning as it helps learners build accurate mental models. In addition, there is evidence that explanatory feedback reduces cognitive load. (Moreno, 2004)
Motivated or mature learners can benefit from self-directed feedback. As the learning designer, you can present thoughtful questions that encourage learner reflection, self-evaluation and self-assessment.
For example, after requesting that learners write a short essay response to a question, provide an ideal response or specific criteria as feedback. Then let learners evaluate their own essay and compare it to the ideal. There are many types of self-evaluation questions that can encourage higher-order thinking and reflection.
Worked-out examples are step-by-step demonstrations of how to solve a problem. They are thought to be effective with learners who have limited prerequisite knowledge because these examples reduce cognitive load. (Sweller, et al, 1998)
If the focus of your instruction is problem solving, then you can provide worked-out examples for learners to study and then again as feedback after they solve a problem. Note that worked examples are not effective for learners who are skilled at a task as it interferes with their ability to solve problems like an expert.
Connie Malamed is an expert on e-learning, information and visual design, and author of Visual Language for Designers. This post first appeared on her blog, The E-Learning Coach. Re-posted with permission.
Image used under Creative Commons by Flickr user jepoirrier.
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