Cheery Friday greetings! No, this isn’t an email from Barbara Oakley, but we did talk to her this week and we’re so inspired by her work. She has new books coming out this summer -- stay tuned for more! We’ll be sure to talk about them here.
This week, we’re focusing on the following questions:
- When can workplace gamification backfire?
- What are the best practices in mobile microlearning?
- Does spaced practice enhance learning?
Let's get started!
The Dark Side
Given the title of this article, we couldn’t resist a Star Wars joke or two. In an upcoming issue of the Journal of Business Research, authors asked: is there a dark side to gamifying employees’ performance? The answer, in short, is yes. In a study that examined the effects of gamifying front-line employees’ behavior, researchers found that, when imposed, gamification may actually increase stress and disengagement. Why? “The goals and rewards associated with gamified systems may be perceived by employees as a carrot-and-stick approach from managers to control their performance” (Hammedi et al., 2021). Researchers recommend that “making participation voluntary is highly recommended to increase receptivity to gamification, which does not seem fun to everyone” (Hammedi et al., 2021).
Key Takeaway: If you’re implementing a gamification system to motivate employees, be transparent about your objectives and obtain staff’s permission before enrolling them in a gamification program. That way, it won’t be seen as some kind of mind trick.
Read More (paywall): Hammedi, W., Leclercq, T., Poncin, I., & Akire, L. (2021). Uncovering the dark side of gamification at work: impacts on engagement and well-being. Journal of Business Research, 122, 256-269.
On the Go
When it comes to microlearning, we have more questions than answers: how long should micro-content be? What should these miniature lessons cover? How should they be structured to optimize learning? Researchers at the University of Missouri sought to answer some of these pervasive questions using a multi-phase study that resulted in a set of 15 principles for creating microlearning content for mobile devices. While their findings haven’t been empirically proven to increase learners’ knowledge retention, the principles outlined represent a solid foundation for future research. We’ve summarized the full list of principles here.
Key Takeaway: Mobile microlearning, if designed correctly, can contribute to individual employee learning. Note, though, that even the authors admit that deep learning isn’t what we’re striving for with microlearning.
Read More (paywall): Jahnke, I., Lee, YM., Pham, M., He, H., & Austin, L. (2020). Unpacking the inherent design principles of mobile microlearning. Technology, Knowledge and Learning, 25 , 585-619.
In an open access article published in the journal Science of Learning, researchers Luke Eglington (who has been previously featured in LSW) and Philip Pavlik, Jr. sought to clarify exactly how far apart spaced practice sessions should be. (For a primer on spaced practice, check out Understanding How We Learn: A Visual Guide by Weinstein & Sumeracki.) Eglington and Pavlik developed a model for predicting how frequently learners should practice based on a number of variables, and their experiment showed that “adaptively scheduling practice using model predictions and difficulty thresholds is both possible and can benefit memory.” We’re highlighting this article mainly because most workplace learning and customer education opportunities are one-and-done and don’t leverage the benefits of spaced practice. This model can be introduced into edtech platforms to enhance the learning experience.
Key Takeaway: The days of cramming information into your brain should be over -- spaced practice is a much more efficient way to learn. If the material is difficult, try shorter study sessions with plenty of breaks.
Read More (open access): Eglington, L.G. & Pavlik Jr, P.I. (2020). Optimizing practice scheduling requires quantitative tracking of individual item performance. npj Science of Learning, 5(15).
Featured Student: Steven C. Dang
Everyone, say hello to Steven, who's a PhD student at Carnegie Mellon University’s Human Computer Interaction Institute. His research is focused on how to measure student motivations through unobtrusive observations of behavior as well as how to support the creation of product specific measurement models of student motivation. His work draws on theories of attention, decision-making, and motivation as well as cognition and learning to inform the development of unobtrusive measurement models.
In one of his recent projects, “Building Better Behavior-based Measurement Models of Motivation,” he is investigating how to leverage artificial intelligence to support model developers in incorporating product-specific student behaviors that can help infer student’s motivations.
Pets of Learning Science Weekly
Reader Yvonne W. shared a picture of her adorable cocker spaniel, Sabrina, with us. She is super-cute!! Thanks for sharing and for reading, Yvonne!
Send us your pet pics at firstname.lastname@example.org.
Wondering why we’re including animal photos in a learning science newsletter? It may seem weird, we admit. But we’re banking on the baby schema effect and the “power of Kawaii.” So, send us your cute pet pics -- you’re helping us all learn better!
The LSW Crew
Have something to share? Want to see something in next week's issue? Send your suggestions: email@example.com