Category Archives: C. Embodied Learning

Embodied learning acknowledges the individualism of the learner. The individual’s cognitive behaviour connects to past cognitive experiences and present interpretations in ways that are unpredictable and dependent on the the learner’s Umwelt. Umwelt is described as “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” (Winn, 2003, p.12). The learner’s interaction with the surrounding environment can be viewed as a biological interaction and a way of knowing. Metaphorically, the learner is an organism interacting with and within its environment. In effect, both the organism (the learner) and the environment evolve and are changed through the interaction (Proulx, 2013). Proulx refers to this interaction as enactivism and suggests that enactivism is the necessary cognitive theory behind problem solving, or more succinctly “problem posing”, in mental mathematics. Through problem posing, “the solver does not choose from a group of predetermined strategies to solve the task, but engages with the problem in a certain way and develops a strategy tailored to the task (both of which also evolve and are co-defined in the posing). Strategies are thus not predetermined, but continually generated for solving tasks” (Proulx, 2013, p.316).

In the brief article by Barab and Dede (2007), there is evidence of the cognitive theory of enactivism as the science learner is immersed in “narratively driven, experientially immersive, and multi-rich media” (p.1). The learner, as the organism, interacts with the immersive game-based simulated environment, bringing individualized input and then coupling {embedded interaction} with the environment. Problem posing exists as the learner poses solutions and generates strategies as interaction occurs with/in the simulated environment. In contrast to Proulx’s (2013) writing on enactivism and mental math problem posing through which students interact with an unprogrammed environment, Barab and Dede (2007) share studies of learners interacting with a programmed simulated environment. Can learner interaction with a programmed environment, even when programmed to be an adaptable environment, allow for enactivism to truly emerge? Or in other words, is the environment truly evolved by the learner, or is it an illusion? Also, what would be the best practices for teacher assessment and feedback when learners and environments are continually evolving and adapting?

In my own practice, I appreciate Proulx’s view on the individual learner and how this individualism aids the approach and walk through learning. I particularly appreciate that his focus is on mental mathematics, an area that seems to be neglected as students interact largely with workbook based curriculum and predetermined strategies. Continuing to engage students in number talks, breaks the misconception that there is one right way to find a solution, and opens the mindset to evolving possibilities. Immersive simulations that allow students to problem pose and structure solutions through interaction with the environment, and then use the adaptations to further generate strategies for solutions is ideal. I look forward to discovering simulations that encompass enactivism through the remainder of this module.

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.

Proulx, J. (2013). Mental mathematics, emergence of strategies, and the enactivist theory of cognition. Educational Studies in Mathematics, 84, 309-328.

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

My readings for this lesson revealed the following key ideas:

• The idea of coupling describes a mutually influential dynamic of interaction between learners and their environments. (Winn)
• The real power in augmented reality lies in using digital technology to enable students to see the world around them in new ways and to engage with realistic issues in a student-connected context. (Klopfer & Sheldon)
• If physical objects focus a child’s attention on irrelevant aspects of a procedure rather than on the underlying concept, the child may be unable to generalize learning to a new context. (Novack, Congdon, Hemani-Lopez, and Goldin-Meadow)
• A meta-analytical study of research articles by Wu, Wu, Chen, Kao, Lin, and Huang found that only 5% of the studied articles investigated the affective domain during mobile learning and only 5% evaluated the influence of learner characteristics in the mobile learning process.

As a math teacher who regularly recommends and models physical manipulative for math learning, I was initially saddened by Novack, Congdon, Hemani-Lopez, and Goldin-Meadow (2014) that action-based learning can actually inhibit students from applying their learning to novel contexts.  Their further explanation, however, of the concreteness fading theory was reassuring as it pointed to the way in which I strive to use physical manipulatives.  According to this theory, the most effective way to use representations for learning is to first introduce concrete representations then transition learners to more symbolic or abstract representations.  Symbolic and abstract representation is where I envision a valuable role for augmented reality and mobile apps for learning.  Students can progress from a concrete physical tool to a digitally represented tool, and ideally eventually to an abstract gesture approach that allows them to apply their learning in novel contexts without the limitations of technology availability.  One way I envision using embodied learning with my senior math students is using body and arm positioning to represent the shape of particular types of functions, such as the trigonometric functions, a cubic, etc.  By using movement to represent these forms, it is my hope that it will help them to apply the abstract rules to the physical position and movement.  Following from Novack et al’s findings, having my students orally say certain conditions and rules while performing the gestures will potentially help them better internalize the learning.

Winn (2003) explains that internal rules or procedures that specify how a student interacts with his/her environment change through adaptation primarily based on their success at producing fruitful behaviour.  Students working with physical manipulatives such as base-ten blocks will be able to use them fruitfully for particular contexts for a period of time.  Eventually, they will reach a point where they are no longer applicable or efficient.  Movement to a different method of exploration can thereby return the learning to fruitful levels.  Eventually, the development of an abstract concept will likely enable the student to use abstract strategies to produce fruitful behaviour that was not possible with other tools.  For my own STEM practice, this reinforces the idea of scaffolding learning experiences to move students from the concrete to the abstract in progressive stages that allow them to also recognize limitations and learning needs for themselves.  With the growth of mobile learning opportunities and device proliferation, this process can be further expanded into the home as students are able to engage in representative learning activities on personal devices as well.  Wu et al (2012) highlight the conclusion of Ketamo (2003) that while mobile technology can generally bring some added value to network-based learning, it cannot replace conventional computers.  As mobile devices continue to advance in their development they offer more possibilities, but there remain tasks that are far better suited to a computer, such as those that require large amounts of memory, processing power, electrical power, or certain forms of tactile interaction such as a full-size keyboard.  Thus, I still recognize connected but different roles for both mobile technologies and computer-based technologies as components of the learning process.

Questions Arising:

If educational philosophy is increasingly focusing on student engagement through personal connection and the affective component of holistic development, how can we reconcile a push for these personalized approaches with a seeming lack of sufficient research on the affective and learner-centered influence of mobile learning opportunities?

Winn explains Umwelt as the environment as seen and understood by different individuals.  Recognizing that understanding a student’s Umwelt is essential to engaging them in meaningful and fruitful learning opportunities, what are strategies a teacher can use to gain a deeper understanding of a student’s Umwelt in any given situation, particularly when a student is currently lacking in engagement?

Novack et al found that gesture was an important component of grade 3 students learning how to group when adding more than two numbers as they used their hand to gesture the v-formation of combining values.  How can gesture be incorporated into the teaching of more complex mathematical processes?

Resources:

Klopfer, E., & Sheldon, J. (2010). Augmenting your own reality: Student authoring of science‐based augmented reality games. New directions for youth development, 2010(128), 85-94.

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.

Wu, W. H., Jim Wu, Y. C., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817-827.

In the readings this week regarding embodied learning, I was interested in the applications of embodied learning theories to mathematics. The ability to understand numbers and unknowns is a concept that many students struggle with at some point in their academics.  Being able to experience the learning through more tactile means adds another dimension on learning to students.  Also, using signs and symbols to represent numerical equations can assist in students understanding on mathematical phenomena. Radford (2009) emphasizes that in order for students to embody their learning, they must first disembody their previous notions of spatial awareness. When students have partially developed ideas of mathematical concepts, it can be much more difficult for them to learning through embodied methods.

In the paper by Carraher et al (1985), the authors noted how children that had little to know formal education in Brazail were about to understand and compute mathematical problems as they bartered for goods in the markets. This demonstrates how the way we go about learning math in more formal education settings is not the only way to build real world skills.  For a project with my grade 4s, I gave them the opportunity to plan a party with a budget of $100. We walked to our nearby grocery store so that students could decide on products they wanted based on how many guests they were having. They had to use their math skills as well as planning skills to make sure they’re guests would be satisfied. I think this is the closed I’ve come to teaching embodied learning in mathematics. I’m curious what new educational technologies will emerge for educations to use in the classroom.

Some questions I have :

Embodied learning to me seems to be more of a teaching strategy that educators turn to when more traditional disembodied methods are not working. How can we make embodied learning more relevant and integrated into the curriculum?

The second questions ties into the first… If we use embodied learning in the classroom, how do we know it’s working? It seems that we may flip back to the traditional assessment formats to measure its success. I was wondering what types of measurable assessment can we conduct to demonstrate its effectiveness?

References:

Carraher, T. N., Carraher, D. W., & Dias Schliemann, A. (1985).

British journal of developmental psychology: Mathematics in the streets and in schools British Psychological Society.

Radford, L. (2003) Gestures, Speech and Sprouting Signs: A Simiotic Cultures Approach to Students’ Types of Generaltizations. Mathematical Thinking and Learning

Zydney and Warner (2016) conduct a comprehensive literature review of the use of mobile apps for science learning.  With increasing stress by district administrations on technology integration, BYOD programs, and school programs promoted by both Apple and Google, it is not surprising that innovations in the mobile and app industry “…have prompted educators and researchers to utilize these devices to promote teaching and learning” (p. 1).  I have also experienced the excitement with mobile apps through peers in the MET program as well as through PD sessions hosted by the Manitoba Teacher’s Society.

Zydney and Warner (2016) explain the advantages of mobile apps as being, “…interactive and engaging…” (p. 1).  Further, that apps “[allow] educators to teach without being restricted by time and place…” (p. 1).  Other posts this week stressed the drawback of time spent in class learning new technologies for both teachers and students, especially in lieu of discussing curriculum during that time.  Perhaps the use of mobile apps is a possible solution that in fact once adequately mastered by teacher and students, benefits from being used unrestricted of time and place.  A great example of this is the use of twitter I read about in a previous MET course.  A math teacher essentially tweeted interesting and complex math problems to students outside class and received responses from students thus facilitating learning beyond the scheduled math class during the day.

Zydney and Warner (2016) discovered 6 main features in their review that included, “technology-based scaffolding, location-aware functionality, visual/audio representation, digital knowledge-construction tools, digital knowledge-sharing mechanisms and differentiated roles” (p. 6).  It appears all these features follow very closely support the T-GEM and LfU models we have explored in module B.

Technology-based scaffolding and visual/audio representation plays a role in the initial stages of helping students develop their ideas.

Digital knowledge-construction tools and sharing mechanisms help in creating meaning, recording observed data, and making thinking visible for students; especially in a cooperative manner.

With the plethora of apps available today for learning science in conjunction with the omnipresence of mobile technology both in and out of the classroom, it appears mobile apps provide a great asset to teaching science content and conduct science inquiry both in and out of the classroom.

Question for peers:

Is there a mobile app that you have some experience with that in your opinion is excellent in teaching content and leading science inquiry?

Reference

Like a few of my classmates, I have found myself intrigued with many of the readings this week, moving from one article to another as I become more involved in the different aspects of this type of learning, with each new article giving me something new to ponder on. The idea that most caught my interest was that of immersive participatory augmented reality simulations as posited by Dunleavy, Dede, & Mitchell, and the link to gaming environments. I have long been fascinated by the idea of using game elements in the classroom to increase student engagement and motivation, and AR simulations provide the means to implement this. The technology-mediated narrative and the interactive, situated, collaborative problem solving affordances of the AR simulation were highly engaging, especially among students who had previously presented behavioural and academic challenges in the classroom (Dunleavy, Dede, & Mitchell).

Winn notes that cognition is embodied in physical activity, that is embedded in the learning environment, and that learning is the result of the adaptation of the learner to the environment and the environment to the learner (Winn, 2002). This idea is corroborated by further research suggesting that learning and cognition are complex social phenomena distributed across mind, activity, space, and time.  A student’s engagement and identity as a learner is shaped by his or her collaborative participation in communities and groups, as well as the practices and beliefs of these communities (Dunleavy, Dede, & Mitchell).  The idea of collaboration using Participatory Simulations is reiterated by Collella in the Participations Simulations Project using the Thinking Tags. Participants personal connections to the educational situation enable them to bring their previous experiences to bear during the activity, establish strong connections to the activity and the other participants, and to be able to draw upon their experiences for the future (Collella, 2000).

The idea of using the area around my school to create an AR activity, such as the one presented in Alien Contact, fired my interest in creating such a project. This would be a great way to embody physical activity, science and math into an already familiar environment using digital resources to create the simulation. I was also intrigued by the idea that the narrative was an important component to the activity. This is a gaming feature to engage the students the background story is most important. The problem solving using science and math is embedded in the story. The most significant affordance of AR is its unique ability to create immersive hybrid learning environments that combine digital and physical objects, thereby facilitating the development of process skills such as critical thinking, problem solving, and communicating utilized through interdependent collaborative exercises, its ability to blend a fictional narrative with the real and familiar physical environment such as the school playground (Dunleavy, Dede, & Mitchell).

However, as all of the participatory simulations I discovered used specific technology, perhaps not available to all schools, my questions are these:

How can we use technology already in the hands of our students, such as smart phones or tablets, to engage them in AR participatory simulations?

How can we best leverage the hybrid environments of digital and physical artifacts to create a rich, collaborative inquiry integrating math and science?

How can we interest teachers in integrating AR type simulations into their classroom program?

Resources

Vanessa Colella (2000) Participatory simulations: Building collaborative understanding through immersive dynamic modeling, Journal of the Learning Sciences, 9:4, 471-500 doi:10.1207/S15327809JLS0904 4

Dunleavy, M., Dede, C., & Mitchell, R. (2008). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology,18(1), 7-22. doi:10.1007/s10956-008-9119-1

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: http://www.hitl.washington.edu/people/tfurness/courses/inde543/READINGS-03/WINN/winnpaper2.pdf

Students may develop misunderstandings of science due to a variety of factors including representations, teachings or models that do not fully explain a phenomenon or incorrectly explain a phenomenon. Virtual reality can help to create sound scientific conceptions if it is designed correctly. Research has found that current conceptions can be challenged by new ones especially if they arouse curiosity and that conceptual change is greater when engagement is high. Virtual reality immerses the students in the learning and increases engagement and immersion and presence help conceptual change. Students are able to have deeper learning through active discovering through immersion in the environment (Winn, 2003).

Presence in virtual reality is defined as a measure of the soundness of sensory cues that give a sense of physical presence or direct experience (Whitelock, Brna & Holland, 1996). This is further broken down into the degree to which the technology delivers realistic renderings, colours, textures, motion etc, the extent to which the environment that is simulated is familiar to the user and as “real” to life control over this environment (Whitelock, Brna & Holland, 1996). When virtual reality meets these criteria students show improved understanding of concepts. That being said, virtual reality can also exacerbate previous misconceptions or even build new misconceptions.  An example is seen in the example of Virtual Puget Sound. In this virtual reality the concept that water speeds up when moving through narrow channels was misunderstood by a student who thought that longer arrows in narrow channels showed that they were more clogged (Winn, 2003). The concepts laid out in virtual reality environments may not be intuitive to new learners or learners with previous little experience or understanding of the phenomenon.

Questions I wonder about…and hope you may shed some light on….

How can we mitigate scientific misunderstandings that may be fostered through virtual reality that has not been effectively designed?

How are we assessing understandings and concepts learned in virtual environments? Are we checking in to ensure students are correct in there scientific understandings throughout the virtual reality process or are we expecting the technology to lead them down the “right path” without effective facilitation?

Should virtual reality be field tested to ensure that the design is optimal or is this dependent on too many outside factors out of the designers’ control? (Age of students, previously held scientific beliefs, educators’ understandings and useage of the technology, etc.)

Whitelock, D, Brna, P., Holland, S (1996). What is the value of virtual reality for conceptual learning? Towards a theoretical framework. CITE REPORT. Retrieved from https://www.researchgate.net/profile/Simon_Holland/publication/251442609_What_is_the_Value_of_Virtual_Reality_for_Conceptual_Learning_Towards_a_Theoretical_Framework/links/581792fd08aeffbed6c33b4f.pdf

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: http://www.hitl.washington.edu/people/tfurness/courses/inde543/READINGS-03/WINN/winnpaper2.pdf

This week, I had the opportunity to investigate Winn (2003), Aleahmad and Slotta, J. (2002), and Núñez (2012).

Winn (2003) provides a robust reimagining of the constructivist framework in light of developments in neuroscience. Winn (2003) situates embodied learning as an outgrowth of both constructivist and information processing theories. Winn (2003) rejects the constructivist perspective that a learner’s constructions are to unique to be adequately measured. He asserts that, instead, an educational designer can make use of artificial environments add predictability to the constructions students might make.  With regards to information processing, Winn (2003) views the previous views as inadequate due to an exaggerated focus on symbol manipulation and insufficient exploration of their meaning. Winn expresses theory central tenants to the theory: That cognition is linked to our physical being (embodiment), that we are coupled to our environment (embeddedness), and that we influence, and are influenced by our environment (adaptation).

Nunez (2012) examines the state of embodied cognition as a theory. Nunez identifies that embodied cognition is capable of providing rich descriptions of phenomena but that many other theories have stalled at this point. To be considered scientific, embodied cognition must begin to generate testable theories. If it is unable to provide these, embodied cognition may not have a sufficient claim be being considered scientific.

Aleahmad and Slotta (2002) looked at the use of handheld devices for data collection when combine with the wise environment. They found promising results from two trials and were able to implement both survey style and measurement style data types.

Aleahmad and Slotta (2002) seem to have happened upon a possible solution to issues faced in Winn (2003). Winn asserts that engagement with artificial environments is key to realizing their benefits. What the tablet devices may allow is a sort of bridge between the artificial and real environments. When students leave the classroom, they must uncouple from an artificial environment. The tablet might serve as a kind of tether. The presence of the device, and the fact that data collect with it will return to the artificial environment, serves to continually remind students of the presence of the artificial environment waiting for them back in the classroom. Despite not being present, the artificial environment still acts upon the cognition of the student and influences how they behave in the real environment. These actions, in turn, will alter the artificial environment through the input of new data. In essence, while Winn (2003) was looking for a solution to students becoming distracted from the desired artificial environment, Aleahmad and Slotta (2002) are using a tablet to, in a way, distract students from the real environment and back to the artificial one.

In my own practice, I have had some great success using WISE to investigate the cause of the seasons. Instead of data gathering with mobile devices though, I used simulations with my students. The process clearly reflected Winn’s (2003) view that both the student and the artificial environment mutually influence each other. The students began with data to collect. As they manipulated the simulations, they began to develop questions. This led to different tests of the environment yielded further result and more questions. I also found that the use of simulations seems to reduce cognitive load. Students were able to reason more accurately when observing a model/simulation instead of having to use their working memory to represent and manipulate representations of the earth and sun.

Going forward, I would certainly plan to use more simulations to help students discover phenomena, scaffolded by leading questions or key data that needs to be gathered. Timely provision of dissenting information and observations, a key tool I began using in the above WISE unit, will be carried forward into other STEM subjects to help facilitate inquiry learning.

In terms of some questions about embodied learning, I wonder, to what extent could practicing externalized cognition can impact student learning in STEM disciplines? By prescribing certain styles or approaches to of note taking, equation solving, unit analysis, etc., in the external environment, might we be able to shape a student’s conceptions more accurately?

References:

Aleahmad, T. & Slotta, J. (2002). Integrating handheld Technology and web-based science activities: New educational opportunities. Paper presented at ED-MEDIA 2002 World Conference on Educational Multimedia, Hypermedia & Telecommunications. Proceedings (14th, Denver, Colorado, June 24-29, 2002); see IR 021 687. Available at: https://eric.ed.gov/?q=Integrating+handheld+Technology+and+web-based+science+activities%3a+New+educational+opportunities&id=ED476962

Núñez, R. (2012). On the science of embodied cognition in the 2010s: Research questions, appropriate reductionism, and testable explanations. Journal of the Learning Sciences, 21(2), 324-336. http://ezproxy.library.ubc.ca/login?url=http://dx.doi.org/10.1080/10508406.2011.614325

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 March, 2017, from: http://www.hitl.washington.edu/people/tfurness/courses/inde543/READINGS-03/WINN/winnpaper2.pdf

In this week’s readings, I was drawn to the connection between learning and physical activities, actions, and gestures. As Winn (2003) points out, “successful students are anything but passive” (p. 13) and our classrooms must reflect and foster this fact. If students are to involve their entire bodies in learning, rather than just their brains, thereby embodying cognition as Winn describes it, then as educators, we must look carefully at how we construct our lessons and projects to support this. As Winn discusses, our natural views of the world are very limited and are based on our own experiences. While some digital technologies may recreate experiences for us, the question becomes how accurately can a computer programmer recreate a unique experience that each individual user’s knowledge and understanding can evolve from?

Ahmed and Parsons (2013) offer that mobile technologies provide students with “opportunities for increasing engagement, motivation and learning (Lin, Fulford, Ho, Iyoda, & Ackerman, 2012)” (p. 62). Their study uses a mobile learning application called “ThinknLearn” to engage students in abductive scientific inquiry while scaffolding learning so students are able to generate hypotheses based on inferences made through observations. The study places students in a real-life environment where they follow the Abductive Inquiry Model (Oh, 2011, as cited by Ahmed and Parsons, 2013, p. 64) of “exploration, examination, selection, and explanation” (p. 64) to collect and examine data with the aid of the “ThinknLearn” application to enhance learning experiences, performance, and critical thinking skills.

In a “no-tech” embodiment of learning, Novack, Congdon, Hemani-Lopez, and Goldin-Meadow (2014) explore the effect of physical action, concrete gesture, and abstract gesture in helping grade three students solve math equivalence problems, and beyond that, students’ abilities to generalize what they learned to a new concept. While the study showed that the students “were equally likely to succeed on the trained problems after instruction” (p. 5), researchers found “acting gave children a relatively shallow understanding of a novel math concept, whereas gesturing led to deeper and more flexible learning” (p. 6) with abstract gesture aiding generalization and concrete gesture leading to conceptual understanding.

In my own practice, I can see embodied learning being used to support a simple machines unit in science. Students can plan their machines using gestures and actions (in groups – incorporates social learning/collaboration), and will then build and test their machines in a trial-and-error learning environment, basing their learning on their interactions with the machines they are themselves creating. Technology can be brought in as students research and watch videos to help them overcome difficulties they face throughout the duration of their projects.

Questions for discussion:

1) Is it possible for a student to interact authentically in an artificial environment, given that the environment is created using if-then models based on predictable outcomes?

2) How does the increased integration of digital technology into the classroom impact the physical activities that connect students to learning? Do activities or actions performed using an electronic device connect students to learning in a similar way that physical activities or actions would in a traditional classroom setting?

3) “An artificial environment is completely predictable, because we have made it” (Winn, 2003, p. 13), but how does the environment that corresponds with the programmers’ views of the world match up with the experiences and understandings of an individual user? How does an artificial environment impact the learning of students who are from a different cultural background than the programmer? For example, how would an Indigenous student’s own experiences and knowledges be represented in an artificial environment created by a Western European? How would the experiences of a student who has recently arrived as a refuge be represented in an artificial environment created in North America?

References:

Ahmed, S., & Parsons, D. (2013). Abductive science inquiry using mobile devices in the classroom. Computers & Education, 63, 62-72. Doi: 10.1016/j.compedu.2012.11.017

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. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984351/

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 1-28. Retrieved from: http://isites.harvard.edu/fs/docs/icb.topic1028641.files/Winn2003.pdf

Lindgren and Johnson-Glenberg (2013) discuss the implications of combining embodied learning and advancing technologies. In short, scientific and mathematical concepts are learned through physical, natural movement (such as “gestures, touch, body position”). This learning is combined with new technologies in an emerging field of education known as mixed reality (MR). The authors suggest six precepts for consideration in embodied learning and mixed reality classrooms. They are briefly summarized below:

1. Embodied learning benefits everyone and not just a subset of the population
2. The physical aspects must be properly linked to the development of new ideas
3. The environment should not just augment reality (for example, it should accommodate the ability to overlay visuals or audio or induce collaboration)
4. Provide opportunities for student and peer collaboration
5. Where possible, combine both theory-driven studies and controlled studies to inform the MR classroom
6. 6) Revise assessment to address the changing learning environment

Coella (2009) examines the use of participatory simulations in which a variety of scenarios are guided by a series of rules and structure. Within these constraints, students are able to learn scientific concepts through inquiry, experimentation, and exploration. However, these computer-supported simulations are not actually conducted on the computer; instead, students participate by wearing small, wearable computers and are participants in “unique, life-sized games.” For example, the interactions within a pond ecology were studied by students interacting with each other as either a “big fish” or a “small fish.” Results from the study indicated students were able to: be engaged, identify problems and produce hypotheses, and design and execute relevant experiments.

It is evident from both studies (Lindgren and Johnson-Glenberg, 2013; Coella, 2009) that learning occurs through experience. Students need to be afforded the opportunity to learn through of a diverse array of experiences, whether it be a regulated real-life simulation using hand-held devices or through the physical movement of the body. The concept of kinematics is able to utilize the concepts proposed by embodied learning and mixed reality. For instance, students should be able to physically measure their movements to produce corresponding kinematics graphs. Undoubtedly, the study of motion should inherently involve movement and not rely on textbook recitation or didactic methods, such as lecture.

Questions:

1. The article by Lindgren and Johnson-Glenberg (2013) discuss that assessment needs to depart from “traditional paper-and-pencil-style assessments” and parallel constructivist-inspired learning. How would possibly alter your assessment to match non-traditional learning environments, like MR?
2. In my experience, while educators would like to incorporate different strategies in their lessons (like MR or participatory simulations). They can be sometimes difficult to execute effectively because programs or applications were not necessarily developed with a pedagogical mindset to begin with. Have you encountered any such challenges and how did you overcome them?

References

Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. The Journal of the Learning Sciences, 9(4), 471-500

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.