Category Archives: C. Embodied Learning

Embodied learning and gestures

Embodied learning revolves around the notion that it is not just the brain participating in learning activities, rather the whole body participates, interacts, and manufactures new concepts (Winn, 2003). Winn works to defeat the notion that the mind and body are separate entities, rather the interactions we have with our environment is key to the learning process. Removal of environmental learning experiences works to limit student’s ability to adapt with the environment and therefore form a more complete learning experience. Winn (2003) encourages educators to create a “framework that integrates three concepts, embodiment, embeddedness and adaptation.” This embodied learning can be extended to artificial environments where students actively engage and become “coupled” (Winn, 2003). A person in a coupled learning environment actively engages and interacts through problem solving and discovery learning.


The use of mobile technology can allow students to become coupled learners with artificial environments. “They also have the potential to establish participatory narratives that can aid learners in developing a contextual understanding of what are all too often presented as decontextualized scientific facts, concepts, or principles” (Barab & Dede, 2007). Mobile technologies can allow students to become fully immersed in virtual scenarios where they must participate in scientific processes, or partially immersed where they use mobile technology to aid a real-life investigation. Barab & Dede (2007) highlight the use of game-based technologies to target academic content learning in more embodied and integrated formats.


Zurina & Williams (2011) helped my understanding of embodied learning in the classroom by analyzing how children may gesticulate to solve problems they are working on. Gesticulation is required by these children as they work integrated with their bodies and environment in the learning process. Without realizing it, as I explored this topic I realise that I model embodied behaviour to my students through instruction. For example, when analysing linear and polynomial graphs, I teach students to use the left arm rule to determine if the leading coefficient is positive or negative (one holds their left arm up to recreate slope of the graph; if it is easy the slope is positive, if it is difficult to contort your arm in such a way the slope must be negative).


Are there other embodied actions which help teachers reach their students?

Are you performing gestures which aid in learning without knowing it?


Barab, S., & Dede, C. (2007). Games and immersive participatory simulations for science education: an emerging type of curricula. Journal of Science Education and Technology, 16(1), 1-3.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114. Full-text document retrieved on January 17, 2013. Retreived from:

Zurina, H., & Williams, J. (2011). Gesturing for oneself. Educational Studies in Mathematics, 77(2-3), 175-188.

Embodied Learning Vs Coupled Learning

What resonated with me after reading Winn’s (2003) article, was that students can learn the same way in artificial environments just as they would in natural environments. He prefers to say students to be coupled with the environment as opposed to embedded in it.  Zeltzer (1991) states the correct term to use when a student is being coupled with the environment is “presence” (as cited in Winn, 2003). That you are in an artificial environment, not in a classroom interacting with a computer. What does he mean by this? When a student is using a computer to immerse him/herself by learning various math or science concepts, it’s then not considered embodied learning? I beg to differ. What about Minecraft? I personally don’t have experience with this game but have heard from many colleagues and friends that this game is perfect to learn math concepts such as problem solving, ratios and proportions. Isn’t this considered to be a student interacting with a computer? This is an artificial environment after all, perhaps to create a true “presence” the student could wear a virtual reality helmet? In any case, Minecraft could be considered embodied learning and is already being implemented in classes around the world.

A question I have has been lingering throughout this lesson, “What about the shy, reluctant  learners?” Dede (1995) has answered this question perfectly. He states that these type of learners, will actually benefit more through a virtual reality setting since it’s more in their comfort zone.  They have valuable contributions to share with others, but prefer it to be in written form as opposed to spoken. Looking back at my previous students, I can see how some of them would prefer this type of learning style. Then comes the question of funding for these types of technologies. How are schools to implement virtual technologies with the lack of funding?


Dede, C. (1995). The evolution of constructivist learning environments: Immersion in distributed, virtual worlds. Educational technology35(5), 46-52.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness and dynamic adaptation. Technology, Instruction, Cognition and Learning1(1), 87-114.

The importance of gestural congruency in embodied learning

For lesson 1, I decided to look more closely at augmented reality and its use in embodied learning. I really enjoyed reading the paper by Lindgren and Johnson-Glenberg (2013). This paper really emphasized the importance of “gestural congruency”, which is what sets embodied learning apart from hands-on activities or physical movement in general (Lindren & Johnson-Glenberg, 2013). Basically, the more congruent a physical gesture is to the learning concept, the higher the embodiment interaction. For example, when learning about centripetal force, a student who physically spins a trackable object overhead is participating in a high embodiment interaction in comparison to a student who just clicks on a mouse to initiate spinning in a computer based simulation. They argue that mixed reality (MR) environments are well suited for embodied learning because MR technology can create an immersive environment that situates students inside the to-be-learned environment. Additionally, it provides an environments with an interface that is responsive to students’ movements and physicality. They go on to state that educational researchers in the area of MR technology and embodied learning should focus on the following 6 strategies:

1) Ascribe to the benefits of body-based learning to everyone

  • though there is learning variation to consider, they believe that this type of learning is beneficial to all types of learners, not just kinaesthetic ones. As such, the design of MR for education should be sensitive to cultural, physical and other types of differences among students

2) Assert action-concept congruencies

  • They argue that to achieve educational goals, MR learning enivornmenst should be built upon substantiated links between physical actions that students perform and construction of new concepts (high embodiment interactions)

3) Augmentation should augur well

  • I think their point on this one is that the MR tech should be used wisely and purposefully. Examples would be to overlay representational supports onto real-world experience, include unobservable phenomena, point out salient information, conduct multiple experiments in a short amount of time etc.

4) Introduce opportunities for collaborative implementation

5) Pair lab studies with real-world implementation

  • They state that initial approach to studying a particular MR and learning would be to begin with controlled studies that examines specific affordances of MR tech from building conceptual knowledge before testing it in a authentic context (such as a school)

6) Re-envision assessment

  • MR and embodied learning environments may have effects of learners’ intuition and understanding, perceptual acuity and their willingness to explore the domain but this may not be detected in traditional assessment methods. Thus the authors advocate for designing assessments that are more fit for this type of learning environment. They also state that delayed assessment is important

Now, keeping all of this in mind, I read a paper by Kamarainen et al (2013). This article explored utility of augmented reality paired with a handheld environmental probe to deliver an enhanced situated learning experience to students during a middle school ecosystem science field trip. The augmented reality portion was delivered through a mobile wireless device, which helped students navigate the pond environment and delivered virtual media and information overlaid on the physical pond. AR was also used to direct students to “hotspots” where they were instructed to use the environmental probe to collect measurements. There were several benefits of using AR with the environmental probe, which include:

  •  ability to provide contextualized, just-in-time instruction
  • self-directed collection of real-world data and images
  • social interactivity
  • facilitate cognition distributed among people tools and context
  • and provide individualized scaffolding

Their study found that students learned the intended content well according to a pre-and post activity content assessment, reported a high degree of self-efficacy and had a positive experience during the field trip.

However, I had several questions regarding this research study. For example, it’s hard to assess how much AR actually contributed to student content learning. Could something as low tech as a map and some written information been sufficient to produce the same effect? Is the content knowledge gained sustained? After the novelty of the new technology wears off, will engagement still be high? Was there some other way that AR could be used in a more high embodiment interaction? What is the cost (both for the program and time required to create the AR environment for a particular location) of creating such a program? Is it worth the cost? And could a teacher run a similar field trip without the tech support that was needed (2 researchers provided tech support during each field trip in addition to the one teacher and field trip coordinator that was also present)? I think AR provides a really intriguing field in education with a lot of potential but further research is needed.


1. Have you come across any VR or AR environments for education that use high embodiment interactions?
2. In your opinion, are the costs of these VR or AR environments prohibitory to their use in education?



Kamarainen, A. M., Metcalf, S., Grotzer, T., Browne, A., Mazzuca, D., Tutwiler, M. S., & Dede, C. (2013). EcoMOBILE: Integrating augmented reality and probeware with environmental education field trips. Computers & Education, 68, 545–556.

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.

Movement and Understanding

While reading the Winn article (2003)I couldn’t help but think about the term symbiotic relationship to describe how learning occurs. Over the course of the term, we have inquired into the various frameworks to describe how educators can better prepare themselves to understand how students learn best. As Winn suggests, learning does not occur exclusively in the brain, but rather is the process of engaging the whole body (2003). I love this thought because it reminds me of how learning has evolved over the course of history, no longer must students sit in lecture style  seating to understanding what the expert at the front of the room has to say. Learning involves inquiring with our body and minds to understand connections and applications into real-world instances. According to Winn, “A student’s Umwelt is the environment as the student sees and knows it–a limited view of the real world, ever changing as the student explores it and comes to understand it” (p.12) When educators provide students with opportunities to experience with all their senses new experiences students are more engaged and motivated to involve themselves in learning what they are curious about. Therefore, educators may study various frameworks independently but when we understand learning to be a consolidation of how the brain and body are involved in learning do we come to understand this framework.

Roschelle et al. article “Handheld tools that ‘Informate’ assessment of student learning in Science” article they bring up a good discussion about the inconsistency of how assessment is defined across educators. The importance of providing current formative feedback for students is critical in the cycle of learning, so that students can revisit misconceptions and re-learn concepts. By using handheld technology students will be better able to access and incorporate feedback into their learning rather than waiting until the summative assignment is returned, only to find out it is too late to demonstrate their understanding authentically.

Finally, Novack’s article “From action to abstraction:Using the hands to learn math” we learn that utilizing gestures in learning outperforms actions in the classroom. By involving the body into the learning process we see that students are better able to retain information and make valuable connections that provide longevity in their understanding. The connection between Winn’s article and Novack’s research are exciting and hopefully more teachers are aware of the research in the benefits to get kids moving in order to understand better.


My questions for this week include the following:

  • Assessment: How do educators ensure that through the use of handheld devices students are actually reading feedback in a timely manner that is user friendly?
  • What support systems exist for educators to collaborate with physical education teachers to teach mathematical and science concepts for students in a K-12 system?




  • Roschelle, J; Penuel, W.; Yarnall, L; Shechtman, N; Tatar, D. (2005). Handheld tools that ‘Informate’ assessment of student learning in science: A requirements analysis. Journal of Computer Assisted Learning, 21(3), pp. 190-203.
  • Novack, M. A., Congdon, E. L., Hemani-Lopez, N., & Goldin-Meadow, S. (2014). From action to abstraction: Using the hands to learn math. Psychological Science, 25(4), 903-910.
  • Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.

Embodiment and gestures

There were so many interesting articles this week that peeked my interest in Embodied learning. As the context for my teaching practice is in younger elementary students, I wanted to focus my efforts more into what embodied learning is like in how it shapes younger children’s learning, with regards to gestures and easy-to-access participatory technology.

Winn’s article introduced me to a new framework that takes constructivism and cognitive psychology to a new level, incorporating embodiment as the “physical dimension of cognition”, embeddedness, as the “interdependence of cognition and the environment”, and adaptation (Winn, W., 2002). When discussing embodiment, Winn refers to the physical realm, using our bodies as our tools in solving problems with our minds. In the elementary classroom, sensory experience largely drives the type of learning that occurs most often in science and math units. However, as Winn points out, our sensory experience of the world is quite limited, in terms of how we perceive sound, light and time, therefore, we can use artificial environments to create metaphorical representations that allow students to better grasp concepts “outside the reach of direct experience” (Winn, W, 2002). Metaphors can be tricky as they have the potential to mislead if the abstraction of the metaphor is not intuitive or pre-taught. When working with younger children who have not yet learned scientific abstractions or mathematical equations, greater scaffolding would be needed. Furthermore, Winn discussed the importance of 3D spatial sense through virtual environments. With affective strategies of engagement, immersion and enjoyment, students’ learning can be “coupled” with the environment, providing opportunities for deeper learning. Along with regularly challenging a student’s misconception through unexpected events, artificial environments can enhance a student’s learning, redefining what learning looks like.

Barab and Dede elaborated on the impact of games as an artificial learning environment for students. They stated that in order for any science curriculum to be meaningful, the learning context should be a “participatory act”, where the context shapes the understanding (Barab, S. & Dede, C., 2007). They emphasize the importance of a social nature for games and the ability to “do science” rather than simply observe and memorize, as they are incorporated into the learning process. In considering my own context, in introducing any games or artificial environments, such as Minecraft to examine landforms with my students, I need to be aware that it is the process of inquiry and collaboration with others, through their environment, that will help deepen their learning and solidify their conceptual understanding.

Finally, Novak et al.’s article opened my eyes to a new way of looking at embodied learning, through gestures. In a study of a grade 3 class, the authors found gestures, both concrete and abstract, to have a more powerful impact on the generalization of children’s learning in math compared to physical actions that directly manipulated the physical world. The results of the study found gestures to lead to “deeper and more flexible learning” (Novak, M.A. et al., 2014). Yet, how does this change if an artificial environment is considered? Interestingly enough, physical manipulatives, such as base ten blocks, actually detract from the learning process and generalization of a concept due to the physical irrelevance of moving the blocks in a physical space. I am curious to know if this would be rectified in an artificial environment. Finally, gestures allow for students to develop ideas about the relationships between problems, that actions alone do not allow. I found this particularly relevant, as I often find students struggling to make connections between concepts when they are lacking in abstract understanding.


Some questions I still have:

  • What might gestures look like in an artificial environment? Are they replaced by symbols? Does this give the same effect as a physical gesture?
  • In a participatory immersive environment, how might the teacher help target misconceptions when the environments can have so many elements? If the elements are limited, is it still immersive and just as effective?



Barab, S., & Dede, C. (2007). Games and immersive participatory simulations for science education: an emerging type of curricula. Journal of Science Education and Technology, 16(1), 1-3.

Novack, M. A., Congdon, E. L., Hemani-Lopez, N., & Goldin-Meadow, S. (2014). From action to abstraction: Using the hands to learn math. Psychological Science, 25(4), 903-910.

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

Embodied learning, and the teaching of quadratic functions and equations

Through this week’s reading, I was able to conjure a new way to teach quadratic functions, and equations going forward that incorporates technology. In the following sequence of planned activities, I plan to incorporate a greater amount of collaboration between students, more opportunities to physically manipulate the information they are working with, and focus more on an inquiry based approach.

Learning about the graph of the quadratic function using graphing software

One of the major concepts students taking Pre-Calculus 11 need to learn is the quadratic function, and how the equation of the function determines various attributes of the graph such as its direction of opening, the location of the vertex, and its width. In teaching these different concepts, I plan to utilize Desmos Calculator to have students play around with different forms of the quadratic formula using the calculator’s slider function:

This Desmos activity I plan to create will leverage the “interdependence” of embodiment, embededness, and adaptation” (Winn, 2002) as students will physically manipulate the artificial environment I have created to learn, and adapt their knowledge about parabolas.  An activity of this sort would also promote collaboration between students on a “mixed reality” type software where students have the opportunity for “physical interplay” of the graphs, which, according to Lingren (2013).

As for the choice of using Desmos Calculator over a standard graphing calculator, I am making the choice partly due to my own experiences with Desmos, but also to avoid many of the misconceptions related to quadratic functions pointed out by Powell (2015), many of which involves incorrect equation entry, and may be due to the standard graphing calculator’s small screen.

Relating quadratic equations and the graph of the quadratic function

Students frequently have difficulties connecting the concept of solving quadratic equations and working with the graphs due to their failure of seeing the relationship between the two concepts. To solve this problem, I would utilize another activity using the Desmos calculator. This time, I wish to use the calculator to showcase the relationship between the solution to the equation of the form 0=(x-a)(x-b), and the graph of an equation in factored form: y=(x-a)(x-b), vertex form: y=a(x-p)^2+q, and standard form: y=ax^2+bx+c. I would again utilize an activity involving sliders. I believe an activity utilizing sliders again in this instance, would allow me to reap the same benefits as the previous activity.

My questions for colleagues:

1. What are your experiences learning Mathematics with technology? How has graphing technology (whether it is on a graphing calculator or not) helped you with learning?

2. What do you see to the biggest problem in Mathematics education today?

3. What misconceptions in Mathematics have you struggled with in the past? How did you manage to get past them?

  • 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.
  • Powell, J. (2015). Solve the Following Equation: The Role of the Graphing Calculator in the Three Worlds of Mathematics. Interpreting Tall’s Three Worlds of Mathematics, 52(2), 11. Read one of the chapters from:,5
  • Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114. Full-text document retrieved on January 17, 2013, from:

Embodied leraning & Virtual/Augmented Reality

From the three papers I read, I found the ideas of Embodiment, Embeddedness, and Dynamic Adaptation most intriguing (Winn, 2003). At first, they appeared to be very theoretical. However, it became clear these ideas had practical applications in teaching fundamental principles and concepts that can help computer science students deal effectively with learning algorithms and data structure. Let’s start with Embodiment. Being cognizant of algorithms and data structure are insufficient for triggering effective learning. Combining physical actions such as applying algorithms in real-life problem sets utilizing augmented reality can help students truly understand the subject. Then there is Embeddedness. Viewing a learning environment and the learner as one entity in which learning emerges as a property of the whole is immensely important for understanding how to design controlled scenarios that will enable effective learning. This latter process ties into Dynamic Adaptation that can influence the environment, the learner, or both. For example, a set of carefully designed learning scenarios that change the state of the environment in ways that require learners to adapt their knowledge in order to understand how algorithms (bubble sort, binary search, etc.) work and can also be used to prompt learners to introduce changes to the environment that will satisfy new sets of requirements that emerged from learners’ initial adaptation.

Lindgren and Johnson (2013) further reinforced the concepts of Embodiment, Embeddedness, and Dynamic Adaptation. In particular, number 5 and 6 – recommend Pair Lab Studies With Real-World Implementations and Reenvision Assessment – gave me valuable insights into how to achieve Embodiment and Embeddedness in the course of teaching students to visualize complex algorithms and data structure.

Dunleavy et al. ( 2009 ) informed my understanding of the affordances and limitations of AR environments. Such environments afford excellent collaboration and pattern matching but pose significant limitations, like the nascent stage of the software development and the inherent pedagogical and managerial complexity of an AR implementation (Dunleavy et al., 2009 ). Such factors need to be taken into consideration to achieve optimal Embodiment, Embeddedness, and Dynamic Adaptation.



What are some examples of current educational technologies that support embodied learning in computer science or math?

Could virtual and augmented learning environments harm social interactions in STEM education?



Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7-22.

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.

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


Embodied Learning and Metaphors

Embodied Learning is a new topic for me and one that took a lot of reading and re-reading to get a grasp of.  Winn (2003) describes a framework of learning consisting of three concepts: embodiment, embeddedness and adaption.  Embodiment can be described as “how our physical bodies serve to externalize the activities of our physical brains in order to connect cognitive activity to the environment” (Winn, 2003, p.7) while the interdependence of this cognition and the environment is referred to as embeddedness.  Winn (2003) goes on to describe some of the neuroscience that is known and should not be ignored by educators.  Further, we read that our interactions with the world are limited and thus our understanding of it is too; We cannot hear certain sounds, nor can we see certain light and we experience in the world in a certain space and time.  Winn (2003) states that artificial environments can create representations that can allow us to understand concepts that would otherwise lie outside of our experience.

One of the challenges that arises with artificial environments is determining an effective way to represent certain concepts; students could easily misread or misunderstand metaphorical representations.  Niebert (2012) argues that “not only teaching but also thinking about and understanding science without metaphors and analogies is not possible” (para. 1). An example that Winn (2003) provides is representing current flow of the ocean using vectors and using longer vectors to show faster current (something that was misread by the student to mean the opposite).  Niebert (2012) presents a very impressive paper where 199 metaphors were analysed for their effectiveness in students learning.  An interesting finding was that one reason that a metaphor can go wrong is if it is constructed and not embodied.  What is meant here is that many metaphors are used in the classroom but “students do not have an embodied experience with the metaphor’s source domain but need imaginative skills to understand it” (Niebert, 2012, para. 29).  This really stood out for me because I find that I often use metaphors in my lessons and lectures without really considering how familiar students are with the source domain; even though it may be something students are able to relate to very well, I haven’t considered if is embodied.   

Finally, I read an article by Barab and Dede (2007) where they explore the potential that video games can have to create immersive learning environments for science education.  They found that game-based simulations were able to promote collaboration and self reflection while engaging students in professional roles and scaffolding learning through multimodal representations (Barab & Dede, 2007).


Questions for further discussion:

  1. How would you compare and contrast embodied learning with a constructivists view of learning and do you believe we have moved too far away from traditional cognitive theory as Winn (2003) would suggest?
  2. Niebert (2012) states that science cannot be taught without metaphors.  Do you agree?  Also how can we ensure the metaphors we are using are embedded and not constructed?


Barab, S., & Dede, C. (2007). Games and Immersive Participatory Simulations for Science Education: An Emerging Type of Curricula. Journal of Science Education and Technology, 16(1), 1-3. Retrieved from

Niebert, K. (09/01/2012). Science education (salem, mass.): Understanding needs embodiment: A theory‐guided reanalysis of the role of metaphors and analogies in understanding science John Wiley & Sons Inc.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114. Full-text document retrieved on January 17, 2013, from:

Embodied Learning

Embodied Learning, to learn math and science through movement and senses throughout the body, a very unique way of learning through the creation of a connection between the mental mind and physical body to aid learning. I guess in some ways, we don’t even realise that we are already doing some form of embodied learning without realising it. Like counting down a mental list, and your fingers start to gesture.  It’s also a great constructivist approach to learning.  From the readings, I agree with the authors that there is an importance in cognitive learning as well that we need to explore.

I think for technology to work it’s way into embodied learning, we would need to implement more physical input components in the applications we use for learning. For example:  using a floor mat that has build in sensors to detect inputs. Back in 4th year of my undergrad, my team and I created a soundscape installation as our final project, that allowed users to move around in the space and manipulate music in the space. Their speed and movements, would adjust the music’s tempo and rhythm while certain movements would manipulate the volume. The installation didn’t have specific instructions though, so users would learn as they went. It wasn’t an easy project and required a lot of programming as expected whenever programmers try to take physical inputs and translate that into data to produce something else. This type of embodied learning technology would seem like a small group or individual activity instead of a class size one.

So, for embodied learning to work with technology in math or science classrooms, I would assume that physical inputs would need to be programmed into the applications to respond in the certain ways. The successful-ness of these interactions seems more like a programming issue than whether the lesson was taught well and used effective learning approaches. Educational games occasionally found for Wii consoles get kids moving and learning at the same time. So  these new interactive learning applications definitely have potential, if researchers or programmers can find a way to bring them into the classroom.


Dede, C. (2000). Emerging influences of information technology on school curriculum. Journal of Curriculum Studies, 32(2), 281-303.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114. Full-text document retrieved on January 17, 2004, from:

Embodied Learning, Virtual Environments, and Mixed Reality

One of the key takeaways for me, throughout the selection of readings that I chose for this week’s topic, is the importance of considering the learner as being both embedded in the learning environment, while being physically active within it. In this sense, cognition can be thought of as an embodied, as well as a cerebral activity. As a means of supporting student engagement in embodied learning, educators can provide collaborative, immersive experiences that allow for the exploration of knowledge and content that extends beyond the confines of the classroom space. Winn (2003) states that artificial environments can use computer technology to create metaphorical representations in order to bring to students concepts and principles that normally lie outside the reach of direct experience. This instructional approach enhances students’ ability to apply abstract knowledge by situating education in authentic, virtual contexts similar to the environments in which learners’ skills will be used (Dede, 1992). These synthetic simulation environments center on interaction and collaboration, unlike the passive, observational behavior induced by television and presentational multimedia, and are therefore well suited for constructivist experiences (Dede, 1992).

Existing in the space between entirely virtual environments and entirely real world environments, Mixed Reality environments combine digital technology with physical activity as means of supporting the idea that physical activity can be a catalyst for generating learning (Lindgren & Johnson-Glenberg, 2013). This incorporates the continued emergence of new technologies and interfaces that accept natural physical movement, such as gestures, body positioning, and touch, as input into interactive digital environments. As such, exciting learning possibilities exist around creating personalized educational experiences grounded in the learning affordances of human perception and bodily action (Lindgren & Johnson-Glenberg, 2013). In my own practice as a Physical Education specialist, I aimed to infuse gamification into physical activity. Apps that fall within the mixed reality category can guide or instruct students in learning skills or movements and can enhance teaching and learning in Physical Education, and these can be utilized by individual students, small groups, or during whole class activities. Augmented Reality offers new possibilities in delivering engaging physical activity to students.

Winn (2003) emphasizes the importance of designing instruction with the focus on learning being no longer confined to what goes on in the brain, as cognitive activity involves the brain, through the body, and to the environment itself. If learning is considered to arise from the reciprocal interaction between external, embodied activity and internal, cerebral activity, the whole being must be considered as embedded in the environment and contexts in which it occurs.

Questions to Consider

How do educators best design embodied learning tasks in order to strengthen student knowledge through physical movement, while maintaining some level of structured and prescribed focus to the tasks?

What considerations do educators need to bear in mind with regards to student diversity in cultural, physical, and social considerations when engaging in embodied learning activities?

In what ways can virtual, immersive learning environments foster a transformation in social interactions, and what impact could this potentially have on the nature of interactions and behaviours between students?


Dede, C. (1995). The evolution of constructivist learning environments: Immersion in distributed, virtual worlds. Educational Technology, 35(5), 46-52.

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.

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