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.
So, why is this one of the most common types of feedback to user responses in eLearning? In fact, many authoring tools come with these vacuous statements as their default response, implying it is acceptable.
If we’re striving for higher order thinking and maximum learning transfer, then we’re giving up a golden opportunity when we forgo informative feedback and instead resort to corrective feedback. Corrective feedback informs the learner whether their response is correct or incorrect but gives no explanation. We need to close the feedback loop with something more substantial.
You Have Lots of Options
There are many strategies for providing alternatives to correct and incorrect feedback. The approach you choose should depend on the context, type of instruction, performance objectives and the audience’s level of expertise. Let’s look at some of your options for providing feedback that is meaningful because it corrects misunderstandings.
1. Provide Explanatory Feedback
As the name implies, explanatory feedback presents an explanation for why a response is incorrect. Often, there is unique feedback for each choice the learner makes. This is known as context-sensitive feedback. This should be the minimal type of feedback we provide in learning design.
You can apply explanatory feedback to any learning experience in which errors are caused by misconceptions or a lack of knowledge or skills. If your design has frequent opportunities for learners to respond, then you can catch and remediate misconceptions as the learner is constructing meaning.
According to Clark and Mayer (2016), a meta-analysis conducted in 2015 reported a substantial positive effect for explanatory feedback compared to no feedback or corrective feedback with no explanation. They write there is strong evidence that explanatory feedback increases learner satisfaction, learning efficiency and leads to better learning than corrective feedback.
2. Real World Consequences
Using scenarios, simulations and virtual worlds, you can allow learners to explore and make real world decisions while learning in a safe environment. For this type of learning, the ideal feedback replicates the consequences of the actions they choose. For example, in an emergency medicine training course, the choice of one drug results in stabilizing a patient. The choice of another drug results in dangerously high blood pressure levels. In a customer-service course, one response will satisfy a customer and another might leave a customer angry.
The difficult part from a design and development perspective depends on the number of paths you design into the scenario. One way to simplify the strategy is to merge the different paths back together, when possible, after a few decisions. This can reduce the risks of a lengthy design and development effort.
3. Offer Hints and Cues
During interactions with content, learners might require several tries at a response to get the most out of an interactive activity. Using hints and cues may help the participant clarify a concept or fine-tune their discriminatory skills without handing them the answer. Meaningful hints and cues are a good way to assist learners without completely taking away the challenge that can enhance learning.
Epistemic feedback provides a hint that moves the learner closer to the correct answer without giving it away. This is one way to accelerate learning because it “stimulates the learner to think about the ‘why’ in relation to carrying out a task” ( Kirschner & Neelen, 2018). In the case of eLearning, the hint after a user chooses a specific incorrect response might be in the form of, “Have you considered all the factors in this situation?” or “Is there another path you could take that would give better results?”
As you might expect, providing hints is most beneficial to learners who have lower prior knowledge. In one study, an intelligent tutoring system provided hints as participants learned to solve problems using the database programming language SQL. The hints were well accepted and “drastically reduced the distance to correct solution even after several steps upon using the hint.” Researchers found that after a student was stuck on the problem, the hint proposed a new approach to solving the problem, providing a way to explore alternative paths to the solution (.
Another form of a hint is a cue. Cues can be words or phrases associated with the correct response. A text-based or audio cue could act as a signal or hint for what to do next. Visual cues can focus attention on the salient information required to perform a task. In a previous article, on visual cues, I write that they do not carry the primary message. Rather, they act as perceptual signals that control where viewers look.
4. Incentivized Feedback in Games
In learning games, gaining points or completing a challenge will often increase motivation. 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, feedback in games is almost constant. “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.”
5. Peer to Peer Feedback
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 create 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. This was demonstrated in one study where undergraduate students critiqued each others’ writing. (Cho and Cho, 2011).
6. Self-directed Feedback
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.
7. Worked Examples as Feedback
Worked 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 types of examples reduce cognitive load (Sweller, et al, 1998). As learners gain skills and knowledge, you may want to scaffold the learning by thoughtfully omitting some of the steps, allowing learners to fill in the gaps.
If the focus of your instruction is problem solving, you can provide worked-out examples for learners to study and then again as feedback after they solve a problem. Note that worked examples may not be effective for learners who are skilled at a task as it interferes with their ability to solve problems like an expert.
8. Make Use of Positive Feedback
What about correct responses? Should participants receive positive feedback when they respond correctly? In the case of real world consequences (see #2 above), positive feedback comes in the form of a successful outcome: a patient recovers, customers are satisfied or the number of accidents decrease.
In explanatory feedback, there is evidence that positive feedback can reduce uncertainty if learners were unsure of the correct steps to solving a problem (Mitrovic, Ohlsson & Barrow, 2013).
- Cho, Young Hoan and Kwangsu Cho. Peer reviewers learn from giving comments. Instructional Science 39:629–643, 2011.
- Clark, R. and Mayer, R. e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning 4th Edition. Wiley, 2016.
- Recommender system for learning SQL using hints, Interactive Learning Environments, 25:8, 1048-1064, 2017.
- Kapp, M. Karl. The Gamification of Learning and Instruction. John Wiley & Sons, 2012.
- Kirschner, P. & Neelen, M. No Feedback, No Learning. 3-Star Learning Experiences, https://3starlearningexperiences.wordpress.com/tag/effective-feedback/, (2018).
- Mitrovic, A., Ohlsson, S. & Barrow, D.K. The Effect of Positive Feedback in a Constraint-Based Intelligent Tutoring System. Computers & Education, v60 n1 p264-272, 2013.
- Moreno, Roxana. Decreasing Cognitive Load for Novice Students: Effects of Explanatory versus Corrective Feedback in Discovery-Based Multimedia. Instructional Science 32: 99–113, 2004.
- Shank, Patti. Practice and Feedback for Deeper Learning. Learning Peaks Publications, 2017.
- Sweller, J., Van Merriënboer, J.J.G. & Paas, F.G.W.C. Cognitive architecture and instructional design, Educational Psychology Review 10: 251–296, 1998.