Embodied Learning in the time of COVID 

I’m writing this post hoping to help frame a conversation about education in this time of isolation. 

My hope is that we can share ideas for promoting increased learning and engagement through embodied cognition. 

I teach computer science. Learning computer science can be difficult to learn – for many reasons. This is no different than many other subjects e.g. math, foreign languages, reading, problem solving. 

One of the greatest difficulties with learning anything new is making the content accessible. 

When describing learning, it is important to step away from pseudoscience and wishful thinking and look at what we know that we really know about human behavior. In short we are social, physical creatures who are hardwired to respond accordingly. 

How can we take advantage of physical responding in a time of isolation? 

We learn to count with our fingers for a reason. In the social sciences, “embodied cognition*” is the term used to explain that our learning and understanding is informed by our human bodies and their interaction with the world. I briefly describe the related research below – but first: 

How can we leverage embodied cognition during the time of COVID?!

If anyone were to ask me if my class incorporates enough embodied activities – most of the time I would say ‘no.’  Whatever the case may be – given the quarantine situation and students’ isolation, now more than ever, students’ daily access to physical activities that support learning and socialization have been significantly impeded.  

I teach computer science – in a classroom setting we might whiteboard problems or explain solutions to partners. In math, students might act out a function or stand so as to express the slope of a function, or dozens of other activities.

While we might still encourage such activities (and we should) – there are technical solutions that we can use to support learning. Solutions that allow students to model and create knowledge in a virtual space. 

A classic example from computer science is the Logo turtle.  Designed by Seymour papert, designer of Lego Mindstorms, the robot turtle allowed students to learn logic and algorithms by programming art.

Photo by Wallace Feurzeig, BBN

There has been a lot of research done on how physically problem solving and acting out concepts can aid in concept formation, retention, and engagement.   

I have adapted things in my computer science class to take advantage of embodied cognition when programming using familiar metaphors. 

There are multiple, simultaneous project choices for students whereby they can their learning supported by virtual physicality. The two primary alternatives are 1) programming (in) Minetest and 2) programming in Unity.

Using Minetest to leverage virtual embodied cognition

• Program virtual turtles to interact with the environment in Minetest.  Minetest is an open-source alternative to Minecraft. I am advocating that students use Minetest because it is open source and allows students more easily to create an implement mods as well as setting up their own servers. 
  There are a couple of CS related educational mods that we have been looking at. 
  Foremost among those mods are visual bots – programmable robot turtles that can interact with the Minetest world. 
  Here a vbot is adding a wood block:The robots are programmed via a drag and drop interface: 

Because this does involve programming, students are able to algorithmically generate art and artifacts in this interactive sandbox environment – much like real life physicality – except better through programming.

I don’t teach math or CS – why should I care?

Regardless of the subject or teacher, students’ learning will be influenced by the fact that the are social, physical creatures. More specifically, I mention Minetest – because it is a very familiar metaphor/ environment for our students and because it can be easily adapted to model many different content areas. 
What other environment could be adapted so easily by students:
to model their own school? 
to model the functions of cells? e.g. t-cells? mitochondria?
to design logical circuits?
as a model UN environment where students can interact / simulate the workings of a real economy? 
recreate historical events?
create an manage a virtual business?
simulate a network? 

These questions are not completely hypothetical. If you have answers – please do share them.  
I am seeing a lot of distance learning tools in online discourse – but I am underwhelmed by the numbers mentioning participatory simulations or embodied cognition. Below I elaborate a little more on the research / history behind embodied cognition.

What is embodied cognition and why should I care about it? 

Embodied cognition or “grounded cognition” is simply the finding that our learning is influenced by our own physicality. 

When we learn to count – we learn to count on our fingers. This is not silly – it simply recognizing how we as organic creatures are setup to interact with the environment. Our physicality even influences our understanding of complex mathematical concepts such as wave functions (Núñez et al., 1999).  

If you would like to read an more in depth analysis of embodied cognition – there are tons of resources. As far as peer reviewed research articles, I’ll mention some of my favorite and list those in the discussion.

As far as a book – Chemero’s Radical Embodied Cognitive Science is a great introductory text that goes from the basics to complex implications while explaining how embodied cognition is situated in the broader fields of human behavior and psychology*. 

Chemero, A. (2009). Radical embodied cognitive science. Cambridge,  MA: The MIT Press.

What are examples of embodied cognition in teaching and learning? 

Every subject has a plethora of examples highlighting the use of embodied cognition. In math, students often act out the slope of a curve, or physically split up pizza into fractions. Students commonly act out new vocabulary in foreign language classrooms. 

Why did you mention teaching first? 

Of course learning is more important than teaching – it’s the desired result. I mention teaching first because more “conscious” design decisions go into teaching than learning. We teachers are the ones who organize the environment and contingencies to influence learning. 

I also mention teaching because I would like to see more discussion of embodied cognition in teaching and learning related discussions. I love flashy, slick software – but I also acknowledge it’s important to remember how we learn and how we as teachers can facilitate learning. 

 Does embodied cognition really influence learning?

A few years ago, I conducted a meta-analysis examining all the empirical research that I could find examining the affordances of embodied cognition for teaching and learning.  Examining ACM’s database, using the search term “embodied cognition” within the ACM digital library, I identified 1813 potentially relevant results. These were further filtered using the search terms (“embodied cognition” and “learning”) and (“embodied cognition” and “education”) to 513 potentially relevant articles that were identified and skimmed. Of those, I went through 183 articles for closer analysis. Among those, 31 were empirical in nature. These were further narrowed to 11 studies with quantified findings (and significance values). The 11 quality experimental studies, i.e. having control groups and well defined measures, used many different  measure’s so they had to be converted to Cohen’s d. I did this with variance and confidence intervals using the metafor (Viechtbauer, 2010) and MBESS (Kelley & Lai, 2012) packages for R (Team, 2013). 

The subset of (11) studies containing sufficient data for a comparison of effect sizes was evaluated. This additional examination also indicated positive effects for embodied cognition approaches (z = 5.0592, p < .0001). Closer examination using a vote count method (as per Cooper et al., 2009, p. 158 ), tallying significant findings, I identified a significant positive effect for interventions leveraging embodied cognition (z = 3.15, p <.005). {The studies are listed at the end.]

Some background 

My background is in computer science education. The research team I worked with found that students were better able to learn to program by actually acting out the programs when they were planning. Basically, by physically understanding what the robot (program) was doing, the students could better program the robots. Looking at the PDF here, it’s possible to see students acting out their programs. I still have one of the hexagonal mats and use it in introductory programming activities. 

Although, Mr. Loomis, the other CS teacher, and I aren’t teaching programming with the iPro app – we are both big fans of embodied cognition and using it to support learning CS. [CS is a lot harder for students to learn than many people suspect.]  We have adapted the large hexagonal map for our game – hivemind vs. haxors – where valiant hackers are tasked to program autonomous agents to defeat a rapidly spreading malware bot known as the hivemind. If you feel inclined to print out the cards – they are here. 

 

 

 

 

Parting Thoughts 

I really would love to hear from those of you leveraging embodied cognition or with ideas on how to do so.

For example, in the Linux community, Marcel Gagné recently wrote an article about Fighting Isolation in Virtual Reality. 

Thanks to my colleague Mr. Loomis, i know that there is an entire community of people who recreate (I mean really reenact – not just program) historical battles in War of Rights. 

 

References / Works Cited

Abrahamson, D., & Trninic, D. (2011). Toward an embodied-interaction design framework for mathematical concepts. In Proceedings of the 10th International Conference on Interaction Design and Children (pp. 1–10). New York, NY, USA: ACM. doi:10.1145/1999030.1999031

Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149(1), 91–130. doi:10.1016/S0004-3702(03)00054-7

Anderson, M. L., Richardson, M. J., & Chemero, A. (2012). Eroding the boundaries of cognition: implications of embodiment. Topics in Cognitive Science, 4(4), 717–730.

Antle, A. N., Droumeva, M., & Corness, G. (2008). Playing with the sound maker: do embodied metaphors help children learn? In Proceedings of the 7th international conference on Interaction design and children (pp. 178–185). New York, NY, USA: ACM. doi:10.1145/1463689.1463754

Baum, W. M. (2005). Understanding behaviorism: Behavior, culture, and evolution (Second Edition.). Malden, MA: Blackwell Publishing Ltd.

Berland, M. (2008). VBOT: Motivating computational and complex systems fluencies with constructionist virtual/physical robotics. Northwestern University.

Berland, M., Martin, T., Benton, T., & Petrick, C. (2011). Programming on the move: Design lessons from IPRO. In Proceedings of the ACM SIGCHI 2011 (pp. 2149–2154). Vancouver, BC.

Chemero, A. (2009). Radical embodied cognitive science. Cambridge,  MA: The MIT Press.

Clark, A. (1999). An embodied cognitive science? Trends in Cognitive Sciences, 3(9), 345–351. doi:10.1016/S1364-6613(99)01361-3

Clark, A. (2012). Embodied, embedded, and extended cognition. In K. Frankish, W. Ramsey, K. Frankish, & W. Ramsey (Eds.), The Cambridge Handbook of Cognitive Science (pp. 275–291). Cambridge: Cambridge University Press. Retrieved from http://ebooks.cambridge.org/ref/id/CBO9781139033916A026

Cooper, H., Hedges, L. V., & Valentine, J. C. (2009). The handbook of research synthesis and meta-analysis. Russell Sage Foundation.

Cress, U., Fischer, U., Moeller, K., Sauter, C., & Nuerk, H.-C. (2010). The use of a digital dance mat for training Kindergarten children in a magnitude comparison task. In Proceedings of the 9th International Conference of the Learning Sciences – Volume 1 (pp. 105–112). Chicago, Illinois: International Society of the Learning Sciences. Retrieved from http://dl.acm.org/citation.cfm?id=1854360.1854374

Esteves, A., van den Hoven, E., & Oakley, I. (2013). Physical games or digital games?: Comparing support for mental projection in tangible and virtual representations of a problem-solving task. In Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction (pp. 167–174). New York, NY, USA: ACM. doi:10.1145/2460625.2460651

Fadjo, C. L., Lu, M., & Black, J. (2009). Instructional embodiment and video game programming in an after school program. Editlib.org. Retrieved from http://www.editlib.org/d/32064/_32064.pdf

Han, I., & Black, J. B. (2011). Incorporating haptic feedback in simulation for learning physics. Comput. Educ., 57(4), 2281–2290. doi:10.1016/j.compedu.2011.06.012

Howison, M., Trninic, D., Reinholz, D., & Abrahamson, D. (2011). The mathematical imagery trainer: From embodied interaction to conceptual learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1989–1998). New York, NY, USA: ACM. doi:10.1145/1978942.1979230

Jansen, Y., Dragicevic, P., & Fekete, J.-D. (2013). Evaluating the efficiency of physical visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2593–2602). New York, NY, USA: ACM. doi:10.1145/2470654.2481359

Kelley, K., & Lai, K. (2012). MBESS: MBESS. Retrieved from http://CRAN.R-project.org/package=MBESS

Kuenzi, J. (2008). Science, technology, engineering, and mathematics (STEM) education: Background, federal policy, and legislative action. Congressional Research Service Reports. Retrieved from http://digitalcommons.unl.edu/crsdocs/35

Malinverni, L., Silva, B. L., & Parés, N. (2012). Impact of embodied interaction on learning processes: Design and analysis of an educational application based on physical activity. In Proceedings of the 11th International Conference on Interaction Design and Children (pp. 60–69). New York, NY, USA: ACM. doi:10.1145/2307096.2307104

Manches, A., O’Malley, C., & Benford, S. (2010). The role of physical representations in solving number problems: A comparison of young children’s use of physical and virtual materials. Comput. Educ., 54(3), 622–640. doi:10.1016/j.compedu.2009.09.023

Núñez, R. E., Edwards, L. D., & Matos, J. F. (1999). Embodied cognition as grounding for situatedness and context in mathematics education. Educational Studies in Mathematics, 39(1-3), 45–65. doi:10.1023/A:1003759711966

Schönborn, K. J., Bivall, P., & Tibell, L. A. E. (2011). Exploring relationships between students’ interaction and learning with a haptic virtual biomolecular model. Comput. Educ., 57(3), 2095–2105. doi:10.1016/j.compedu.2011.05.013

Singer, M., Radinsky, J., & Goldman, S. R. (2008). The role of gesture in meaning construction. Discourse Processes, 45(4-5), 365–386. doi:10.1080/01638530802145601

Skinner, B. F. (1984). An operant analysis of problem solving. Behavioral and Brain Sciences, 7(04), 583–591.

Smith, C. (2012). Every body move: Learning mathematics through embodied actions. University of Texas, Austin, Texas.

Sterne, J. A., & Harbord, R. M. (2004). Funnel plots in meta-analysis. Stata Journal, 4, 127–141.

Team, R. C. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.

Vitale, J. M., Swart, M. I., & Black, J. B. (2014). Integrating intuitive and novel grounded concepts in a dynamic geometry learning environment. Comput. Educ., 72, 231–248. doi:10.1016/j.compedu.2013.11.004