Embodied learning – just costly solutions looking for problems?

According to Winn (2003), brain, body and the world cannot be separated, and that consequently cognition involves the whole body, not only the brain. Cognitive activity is thus connected to the environment through physical action, which means according to Winn (2003, p. 87): Cognition is embodied in physical activity; activity is embedded in a learning environment; and learning is the result of adaptation of the learner to the environment and vice versa.

Lindgren (2013) defines embodiment somewhat differently as “the enactment of knowledge and concepts through the activity of our bodies” (Lindgren, 2013, p. 445). This definition does not include the learning environment. Yet the idea is the same: learning can be fostered when the body is used to incorporate new concepts. Recent studies have shown that embodied learning leads to “greater chance of retrieval and retention” (Lindgren, 2013, p. 446). Lindgren (2013) then presents two examples of embodiment as “mixed reality”: A MEteor simulation where a student learns about how objects move in space, and a SMALLab chemistry simulation where up to four students immerge in simulations. Both environments include real-time audio and video feedback.

I then chose the topic “mobile apps” and read the review of (Zydney, 2016). He summarizes around 30 mobile apps for science learning. I will select one of those apps for the resource sharing forum.

Regarding applying these approached in my teaching, I was first a bit reluctant: I teach university students, and they probably would hesitate if I told them to use their bodies to learn about computer science J. But I did a quick look at EBSCO and found some paper on embodied learning at university settings, such as: Using pointing and tracing gestures for learning anatomy (Macken, 2014), or using embodied haptic feedback to understand electric force (Magana, 2017). So when thinking about it a bit closer, I could use VR technology to immerge students in a simulated reality where they can see – and maybe feel – the different application systems and their connections in a hospital information systems. Students could follow, for example, the flow of information between the systems. This would be quite interesting to develop – but quite expensive.

When thinking about my idea and after reading all these papers, I couldn’t stop thinking whether augmented reality/virtual reality/mixed reality environments are really worth the effort. Lindgren (2013, p. 449) also discussed this topic without giving a clear answer. Yes, the examples presented in Lindgren (2013) look enjoyable and motivating, and will probably lead to effective learning on the presented concept. Yet, e.g. in MEteor, only a limited set of physical concepts is dealt with. Thus, for other scientific concepts, you would need new or at least adapted environments. This costs a lot of money and time to develop. Also, technology advances fast at the moment, thus MEteor and other examples may be outdates quite soon.

So my question to you:

Q1. Are complex technical environments that support embodied learning only “solutions looking for problems”? Are they worth the effort, compared to other TELE that we have discussed before?

Q2. How can we reduce the costs of developing and maintaining (!) environments for embodied learning?

References

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.

Lindgren, R., & Johnson-Glenberg, M. (2013). Emboldened by embodiment: Six precepts for research on embodied learning and mixed reality. Educational Researcher, 42(8), 445-452

Magana, A.J. & Balachandran, S. (2017). Unpacking students’ conceptualizations through haptic feedback. Journal of Computer Assisted Learning, 33(5), 513-531.

Macken, L. & Ginns, P. (2014). Pointing and tracing gestures may enhance anatomy and physiology learning. Medical Teacher, Vol 36(7), 596-601.

Zydney, J. M., & Warner, Z. (2016). Mobile apps for science learning: Review of research. Computers & Education, 94, 1-17.