An Exploration of the Concept of Kinesthetics and the Development of Rubrics for Their Application in Game-Centric Learning
Rees Shad, Jeans Abreu Mieses, Jose Palacios, and Jose Vidal
he impetus for this research project came about in 2012 in the midst of an initiative to develop game-framed STEM curricula to improve success rates in related introductory and review classes. The initiative involved a group of game designers working with a cross-disciplinary panel of professors from the Humanities, Natural
Sciences, Mathematics, Design, and Education departments at Hostos Community College. Professor Jaqueline DiSanto, who represented the Early Education program, spoke to the group early on in the project about the role that physical activity can play in student engagement with, and retention of, information. A memorable example she introduced was that of an athlete whose academic performance recalling information in the classroom might be low, but the same individual on the football field could recall dozens of plays thanks to having learned them in association with physical movement. This was fascinating to us all, but especially to the game designers in the group. We resolved to include physical activity as a heuristic target whenever possible while designing our learning games.
The following year, as my collaborators and I presented the resulting work to colleagues at conferences and the like, an audience member brought up the term “kinesthetic” when we talked about our games involving physical learning. I was unfamiliar with the term in this context. I resolved to research and explore the concept more deeply, and in 2014 a Title V grant at our institution offered the opportunity I was looking for. The grant focused on undergraduate research, and so provided the funds for me to work with three research assistants in the exploration of kinesthetics. Jeans Abreu Mieses, Jose Palacios, and Jose Vidal are all currently enrolled at Hostos as Game Design majors. I selected these three because they are not only strong students academically, but also had expressed interest in developing games that educate. I began by engaging the group in a discussion of how physical activities might lend themselves to aiding in the retention of information. I asked that each one of the students begin researching the meaning of the word kinesthetic, its historical relevance, and the primary researchers in the field.
The results of this initial foray into the topic were incredibly exciting for me as an educator. Our follow-up meetings involved engaging discussions as each student reported their findings to the group. My background is in media production and English literature; so much of this information was quite new to me. I found the roles of teacher and student reversed here, as I took notes and learned from my pupils.
Initially, the team investigated the meaning, history and current usage of the word ‘kinesthetic.’ Jose Vidal, having never even heard the word before, jumped online to discover its origins from the word kinesthesia, defined as “The sensation of movement or strain in muscles, tendons, and joints; muscle sense”(Dictionary.com). Hence, anything involving these sensations is deemed “kinesthetic.” From there he found that a nineteenth-century-British neuroscientist by the name of Henry Charlton Bastian had coined the term “kinaesthetic” in an 1880 paper titled The Brain As An Organ of Mind. Here Bastian described a relationship between brain and body in a broad assortment of creatures, but clearly outlined the human brain’s reception of information from muscles (muscle sense) as “necessary for the brain to coordinate movement” (Pearce 75). This view appears to differ from the concepts of the day, which saw the brain giving instructions to the muscles in a one-sided monologue.
As Jose Vidal was exploring this, Jose Palacios’ efforts focused on the history of Kinesthetic learning starting with Bastian and moving forward through a family tree of analysts and educational researchers. He found that the concept of a body- brain learning connection had been referred to with different terminology over the last century. Mr. Palacios uncovered an original manuscript of Bastian’s work online wherein the scientist spoke about learning through visual, auditory, and kinesthetic means. Bastian related the latter to the relationship between muscle and brain in learning penmanship (Bastian 648). Soon after, Jean-Martin Charcot, a neurologist, introduced one of the first alternative learning systems with an innovative method of teaching that involved illustrating patients’ facial and body expressions when suffering from various mental disorders. His images focused on depicting characteristic symptoms in order to help his medical students learn to identify particular maladies (Thorburn 78).
Eventually, Charcot would become a mentor to Alfred Binet, who along with Theodore Simon developed the Binet-Simon Scale in 1905. The scale was developed to compare children’s mental abilities relative to those of their “normal” peers (Siegler 180). This would provide a base to which they could determine mental age for educational placement. Recognizing the limitations of his scale, he expressed the need for additional studies using more qualitative, not quantitative, measurement (Siegler 189).
Jose Palacios’ explorations, while seemingly drifting away from kinesthetics, enthralled the group as his fellow students joined him in their research in order to trace the paths of influence. As a media professor focusing primarily on studio with my students, historical research was quite refreshing, and so I encouraged these students to follow their interests. From Binet and Simon they uncovered Jean Piaget, a Swiss developmental psychologist and philosopher. Piaget revised and updated the Binet-Simon Scale to become the Stanford-Binet Intelligence Scale, which he used as an aid in the classification of developmentally disabled children. Binet did this as part of his first experimental studies of the growing mind at the Ecole de la rue de la Grange-aux-Belles, a boys’ institution created by Binet and then directed by Simon (Chapman). The team found that, around the same time period, Carl Jung was in the process of coining the term “collective unconscious,” which refers to the structures of the unconscious mind that are shared between beings of the same species (Corbett). This research inspired Isabel Meyers and Katherine Briggs in developing the Meyers- Briggs indicator test, which was initially a personality test used to find suitable jobs for women during the Second World War before being used for assigning appropriate vocations to the general population. They were a bit surprised to find that this type of indicator test continues to be updated and is widely used today (Myers and Briggs). In a feeble attempt to reign in the research, I asked where these discoveries were leading us, and my students-turned-educators promptly pointed out that both the Stanford-Binet and Meyers-Briggs tests rely on recognizing the learning styles of their subjects. Systems of identifying learning styles, they said in near chorus, would be essential to developing games that teach.
It is important to note that as budding game designers working toward their associate degrees, none of my research assistants had ever heard of, nor would they be expected to have familiarity with, any of these individuals and their work. It was exciting to engage in discussions with my students about subjects running the gamut from the effectiveness of intelligence tests to what sorts of learning styles exist, and the merits of various learning systems we had personally experienced. Eventually, we began wondering how the brain actually learns.
This sort of organic exploration of a topic was immensely satisfying, and we began engaging with the research quite democratically. This work began resembling courses I had taught in graduate thesis development at other schools, where a single question can veer a student into entirely unexpected territory, and the outcomes are almost secondary to the thrill of exploration. The four of us would touch base at a weekly meeting in my office to discuss their latest findings. They would present their week’s findings, examine potential connections, discuss appropriate directives, and establish, with my help, goals for the following week.
We all recognized that we needed a bit more understanding of how the brain actually worked in order to identify effective learning systems, and in what contexts they can best be applied. We saw the importance of getting an idea of how the brain processes information during kinesthetic learning. To this end, Jeans and I began a side project researching the brain, embarking on a self-taught introductory course in neurology.
One of the very first areas of exploration was into brain development and learning, which brought me to a book by Gill Connell and Cheryl McCarthy, entitled A Moving Child is a Learning Child. This taught us about an element of early brain development where the child learns to control his/her body across something called “midlines”.
Here, we must imagine the human body bisected by three planes. One separating front from back (the coronal plane), a second dividing top and bottom (the transverse plane), and the third left from right (the sagittal plane). In early development the child is unable to consciously isolate one side from the other—one hand is lifted and its counterpart also rises, an infant lying on their stomach lifts their legs and their back arches as the shoulders and head mirror the movement. Over time, the child is able to master individuation of these midlines, manipulating just one foot or finger. Their brain is “feverishly building neural pathways [or ‘superhighways’ as the authors describe them] to keep up, and in particular, create and strengthen the pathways that cross the midline of the brain (the corpus callosum)” (Gill and McCarthy 108). The extent of this “integration” is incredibly important to the brain’s performance, as each part of the brain is a subsystem of the larger central nervous system (CNS), and streamlining the intercommunication of the various subsystems is integral to human intelligence and motor activities.
As children grow these superhighways form throughout, as well as between, the various parts of the brain. “Automaticity,” wherein repetitive or very common functions in the brain are automated, is incredibly important to its performance. This is especially true as the brain becomes more sophisticated. Automaticity frees up what many neurologists refer to as the brain’s “processing power.” As Connell and McCarthy explain, repetition aligns the brain and the body, creating muscle memory and automating movement. They explain that this is not simply repeating a drill, but actually just the opposite. “When a child does something over and over, that means that her brain is working through the steps of memorizing the activity. Myelination is happening, too. Her brain rewards her with positive feelings (a sense of fun), so she wants to continue” (Gill and McCarthy 25).
This prompted the team to take a closer look at the brain’s cells. There are two kinds of brain cells, glial cells, also known as neuroglia or simply “glia,” and neurons. Glial cells “provide support and nutrition, maintain homeostasis, form myelin, and facilitate signal transmission in the nervous system.” Neurons, on the other hand, are specifically designed to transmit electrical messages through the body via the Central Nervous System (CNS). As Jeans described them, they are the messengers racing around in supersonic racecars while the glial cells act as their pit crew. Interestingly, while I had never heard of a glial cell, the American Association of Neurological Surgeons informed us that they outnumber neurons (which I had heard of) by roughly 50 to one.
Each neuron has a “nucleus” as the central core of a wide reaching branch-like set of tendrils or receptors called “dendrites.” These gather impulses in the form of neurotransmitters and pass them down a linked chain of myelin coated nodes called the ‘axon’ that ends in a smaller web of transmitters called “telodendria.”
There are three different kinds of neurons—sensory, intermediate, and motor neurons. Sensory neurons communicate the status of the rest of the body to the brain. They communicate the sensory input from the skin, eyes, ears, taste buds, nose, and proprioceptors. Proprioceptors, we discovered, are constantly reporting into the brain from throughout the nervous system the current amount of flex, tension, or stretch in the body’s various muscles, joints, ligaments, and tendons (Gill and McCarthy 91). Undoubtedly, these would be important in any kinesthetic experience.
Intermediate neurons facilitate networking, relaying information to a greater network of similar neurons throughout the CNS. Carla Hannaford refers to the intermediate neural network as “command central, having instantaneous access to the brain’s complete information system” (Hannaford 24). These intermediate neurons account for 99.98% of the total neurons in the system (24). They consolidate the information, process it, and then make the body, muscles, and glands respond accordingly through the motor neurons.” The motor neurons carry the messages from the CNS to the muscles or glands in order for them to carry out their functions.
So each time a person has a new experience, their brain’s cells react in concert to build connectivity and establish precedence for both the body and mind to reference in the event that the experience should repeat itself. This is how we learn. The brain factors in all stimuli, and in the case that the stimuli should occur in a similar way at another time, the data is correlated with established data, assessed, and acted upon. The comparison to a computer here is common, but in fact a computer only has a limited amount of processors while the human CNS has billions of them communicating literally at lightening speed to correlate, compare, update, assess, and act on data input.
Each time this sort of repetition occurs, glial cells provide myelin to the neurons’ axons through a process called “myelination.” This creates an insulating sheath protecting the axon from damage or disruption from all the other neural activity going on in nearby neurons. Just like the insulation on a wire within an electronic circuit this myelination strengthens and focuses the neuron’s signal. As Margot Sunderland explains in her book The Science of Parenting, this “both speeds up processing power and helps cement experience into permanent conscious or unconscious memories. In short, myelination makes the brain faster and ‘stickier’” (Sunderland 22). So, automaticity seemed to us to be promoted by myelination, allowing the various subsystems in the brain to build foundational thinking on which further learning can rely. The research team asked one another if myelination could be encouraged by kinesthetic learning.
Just as there are midlines in the body, there are also midlines of a sort in the brain. There are the two hemispheres—left and right—but there are also layers in the brain that have evolved over the last 300 million years. According to the neuroscientist Paul MacLean’s Triune Brain Theory, these layers expand from an inner central “reptilian” core that deals with instinctual behaviors and bodily functions. This is encapsulated within another “paleo-mammalian” or “mammalian” brain (the limbic system) concerning itself with social behaviors that in turn has been enveloped within a “neo mammalian” or “rational” brain (the neo-cortex) responsible for reasoning.
This outer layer is also referred to as the cerebral cortex and has individuated areas or lobes with areas of particular focused brain activity. All of the regions of the brain are interconnected by a massive network of billions of brain cells that over the course of one’s life are pruned, honed, and strengthened by life experience for optimal inter-communication.
I was concerned to learn that more than half of our brain cells are lost over the course of a lifetime (this outside of extracurricular high school and college psychotropic explorations). A child is born with around 200 BILLION brain cells and within a year this number is reduced by 80 billion. Neurologists call this process “synaptic pruning” and it continues (but at a far less dramatic rate) for the rest of one’s life. By the teenage years, the human brain has lost close to 90 billion cells, and 100 billion by the age of 35. If I live to be seventy, it is likely that I will only retain 90 billion of my original 200 billion allotment (Sunderland 22).
Being at the age when retirement accounts and the education of grandchildren have suddenly become quite pertinent to me, I was concerned that this loss of my brain trust might be devastating. Fortunately we found that, in fact, the brain’s remaining cells actually get stronger when kept active, and the brain quite literally renovates or regenerates itself—developing new pathways of connectivity with new experiences, and even bypassing or re-allotting functions of damaged or incapacitated neurons as in the case of stroke victims. In addition, there is a whole group of regenerative stem cells available to the brain. “From these stem cells, as many as 6,000 new nerve cells per day have been formed, mainly in two areas of the brain; the hippocampus, a structure crucial for learning and memory, and the olfactory bulbs, which receive input from odor sensing cells in the nose, and are also associated with memory” (Hannaford 22). It goes without saying that upon hearing of this regenerative process I relaxed considerably.
Meanwhile, Jean continued his research into the various roles of the active areas of the cerebral cortex, which taught him that there are four major lobes on each hemisphere. The frontal lobe primarily involves itself with actions based around organizing and planning. These include learning tasks initiating and stopping actions, regulating one’s behavior, as well as being a foci for more abstract thoughts, logic, and language translation. Apparently, a lot of our personality is found here (Soc. Care Inst. for Excellence).
Behind the frontal lobe lies the parietal lobe, which is the center of body sense—that part of the brain’s duties that take in current input from the body’s proprioceptors. We found it interesting that this is also a focal point for sentence construction, mathematical calculation, interpretation of all the visual information processed from the eyes through the occipital lobe, and significant to the locating of visual objects.
The occipital lobe is at the rear of the brain, above the cerebellum. This processes information from the eyes interpreting things like color, shape, and movement. As previously stated, this does not actually interpret the information in terms of meaning (that happens in the parietal lobe), but instead focuses on differentiating visual input. Beneath these three is the fourth of the major lobes making up the cerebral cortex. The temporal lobe is involved in the learning and retention of new information. It is the part of the brain most involved with storage of verbal and visual memories. It is also the part of the brain most associated with attention (Soc. Care Inst. for Excellence).
As previously mentioned, these lobes are mirrored on the left and right hemisphere, which in turn have different foci from one side to the other. Left hemisphere lobes are associated with logic and analysis, detail, and sequence. The right hemisphere is more focused on the macro of things, with attention paid to similarities, estimation, and “the big picture.” As Connell and McCarthy explain, “Broadly speaking, the right side [of the cortex] gathers and experiments with new sensations, ideas, and information, focusing on the here and now. The left side analyzes and organizes information into reasoned thinking and future planning” (Gill and McCarthy 110).
Jeans’ research into how all this relates to brain functions during educational and physical activity brought him to the discovery that the memory retention of individuals is enhanced to retain a larger amount of information when involved in physical activity. Fred Gage Salk at the Institute for Biological Studies in La Jolla, California did a study with rats involving play where the juveniles were given an enriched play environment to live in that contained multiple running wheels, play tubes, and social interaction. At the end of a two month period these rats had an average of 50,000 more brain cells than their control group had (Salk 31).
On hearing this, Jose Palacios’ recognized a correlation with his research into “student centered learning.” This, he explained is the instructional approach where students influence the content and pace of learning. The instructor will provide opportunities for the student to learn independently, collaboratively, and critically and creatively think. I was only slightly miffed that I was the only one in the room who recognized how truly familiar with this concept the four of us already were.
Mr. Palacios pulled out his notes from Carla Hannaford’s book Smart Moves: Why Learning Is Not All in Your Head where she describes studies with monkeys where researchers discovered that simple repetition of a behavior does not influence whether the subject will learn the behavior. “Neural connections can be altered and grown only if there is full attention, and focused interest on what we do” (Hannaford 22). She goes on to explain that if fully engaged and focused, human beings can within a period of three weeks get ten times more proficient at anything at all. “Self initiated movement, exploration, interaction and physical experience for the joy and challenge of it, facilitates neurogenesis (nerve growth) throughout one’s lifetime” (22).
Jose Vidal quickly pointed out that what Hannaford is describing here is “active learning.” The process of keeping students physically active while engaged in learning through activities that involve them gathering, processing, and using that information to problem solve. “This style of learning occurs once the teacher creates a curriculum or environment in which engaging activity is more likely to occur” (Joel 146). By implementing these types of approaches, the opportunity for engagement and retention greatly increases (146).
One study in particular sought to isolate the various conditions of the enhanced learning environment described in Salk’s rat experiments. The researchers concluded that physical activity was particularly important to the retention of brain cells as well as neurogenesis (Praag, et. al 268). Jeans had found that this is especially true of information needing to be recalled when the subject is under pressure, such as a test situation or similar time constraint.
According to Emmanuel Gerardin, a researcher at the Institute of Cognitive Science (Institut des Sciences Cognitives) in Lyon France, the frontal cortex, the cerebellum and the motor cortex (the area of conjunction between the Frontal and Parietal lobes) are all most active during information processing. They are also largely related to unconscious movement, reflexive visual processing, and anticipation of movement soon to occur. Interestingly, Jose Vidal’s research into Bastian’s work found that he referred to this area of the brain as the “Kinaesthetic” cortex (Bastian).
This has been identified by an increase in (and strengthening of) myelination relating to memory retention during physical activity. Gerardin proved that the amount of physical enhancement involved in educational settings can improve the retention rate of subjects when properly balanced—too much activity, however, can overload a student and have adverse effects upon such retention (Dimitriadis).
At this point in the research, we all had separate projects underway involving the design of learning games, and each new piece of information began almost sidetracking our work as we analyzed how it might be applied to our other projects. While we were finally coming to understand the relationship of learning, physicality, and brain development, we had yet to conceive of a workable outcome. The end of the semester was looming large when our little research group sat down to look at the big picture in terms of what information we had gathered, where this was likely to lead us, and, most importantly, what we wanted to be our take away from this endeavor.
The two Jose’s were involved in a game design project that involved a tabletop card game used to teach Japanese, and Jeans was exploring a learning game concept of his own. Meanwhile a colleague of mine and I were engaged in developing a new system of play for assessing reading retention and encouraging group discussion in the classroom. We all wanted this research to bring us closer to a means of gauging the effectiveness of learning games, and we hoped it would fortify our work on these other projects, but we hadn’t quite connected the dots yet.
The neural lens we had picked up was showing us the importance of practice for developing strong and permanent connections in the brain via myelination. We were also beginning to see that different aspects of learning were happening in different places in the brain. It seemed reasonable to suggest that getting the various parts of the brain to engage with a game’s topic simultaneously would lead to stronger subject retention in student players, and that physical activity (such as moving across a room or even sorting cards) would encourage a wider spectrum of engagement with the brain. This too would increase myelination as well as involve a wider assortment of neural activities. I was elated to see that our little band of game designers was becoming a group of rookie neurologists!
So we began sorting through the data for primary takeaways to apply to our work. The right side of the brain’s propensity to be focused on new sensations seemed as good a place to start as any. Professor DiSanto had told the Game-Framed curriculum designers about how tactile stimulation was often underappreciated in learning environments. The texture of a game asset, therefor, could offer a subtle but powerful trigger in a learning game. The team also focused on the right cortex’s assessment of information in terms of sorting as an important point in what I was beginning to refer to as the “game brain relationship.” The left cortex’s emphasis on organization and analysis in terms of future thinking made it integral to game strategy. So these two sides would obviously need to be triggered in tandem as often as possible.
The team began organizing a group of important attributes for a learning game. We began considering the occipital lobe’s focus on interpreting shape and color to engage with the parietal lobe’s actual interpretation of these in terms of meaning. The group recognized the importance of aesthetically interesting, colorful, and varied asset design in a learning game. We decided that, in terms of a tabletop game, a powerful interaction for a memorable experience would involve sorting through colorful assets with a variety of shapes or aesthetic qualities paired with a strong individuation of tactile characteristics.
The temporal lobe’s focus on verbal and visual memories gave rise to the opinion that auditory confirmation of an asset’s importance was also essential to subject retention. Having a player read off the information from an asset should be encouraged when possible.
Finally, if these attributes could be paired with physical activity they would be all the more effective. This is not to say that students in a learning game should be made to run around the classroom or jump and dance. The physicality suggested by the studies that our group found could be as simple as picking up and rearranging assets on a board or standing to read information on a card before conducting a trade with a nearby opponent or teammate.
The importance of being consistent in developing interactions where the player was having these areas of the brain triggered simultaneously and in consort was a particularly important point in these discussions. The team saw that the more varied the stimulation, the greater the number of brain centers triggered, and the resulting connections made between these points in the brain when triggered through repetitive but dissimilar actions would strengthen the overall learning network in a student’s brain.
Perhaps if we could organize these attributes into a rubric of best practices we could use them to analyze learning games and have a significant and powerful tool to use in our work as game designers. I wondered aloud if this sort of tool was already available, and Jose Palacios came back with research into a man named Neil Fleming, who created the VARK questionnaire. This is a questionnaire that helps researchers determine how a subject processes and retains information through Visual, Aural, Reading/Writing, and Kinesthetic means (hence the name VARK).
It was in discussing the work of Fleming that the team really found a meaningful directive in our project. While the VARK model gives strong examples to work with, the group discussed the relative subjectivity of research asking subjects questions in order to determine their preferences for various learning styles. We discussed whether or not the evidence was really being proven here or, instead, being gathered through a version of antidotal research. Furthermore, the students did not find any work looking at measuring the level of Visual, Auditory, Reading/Writing, and Kinesthetic experience in a learning environment. Fleming was simply analyzing the preferred learning styles of his subjects. We began looking, but did not find a functional rating system to help an educator create a more balanced kinesthetic learning experience. It was to address this need that we sat down and outlined a set of rubrics to use as a lens for examining appropriate educational experiences for more effective communication, and as we are all educational game designers we focused on rubrics for measuring game-centric learning.
The process began with the team setting up a simple score card listing out the four teaching modes outlined by Fleming (Visual, Auditory, Reading/Writing, and Kinesthetic), and involving a three-step scale of low, medium, and high. I had them test this by applying the rubrics to a variety of imagined learning situations; consequently, it became obvious that the generality of the term “kinesthetic” made for problematic assessments. We were not actually considering the variety of conditions that might be possible.
Another round of discussions helped us to break apart kinesthetic experiences in terms of tactile sensation, degree of motion, and actual physical discovery. The latter involved physical action with the uncovering of a learning element—for example, turning a box over to discover the answer to a riddle beneath it. These three rubrics, while all involving physicality and brain response to motion, would be scored individually and then averaged for an overall kinesthetic variable. We then focused on using our rubric charts for measuring the overall learning experiences of several tabletop games that had been developed by the Game-Framed Math & Science Initiative at Hostos. Our team play-tested each of the fifteen games, filled out individual rubric charts, and averaged their numbers together for a prototype reporting system to benefit the games’ designers.
It wasn’t long before the team recognized that a three-tiered rating system featuring low, medium, and high scoring was insufficient for representing the varied spectrum of experiences here. The team upped the ante by having a five tier scoring system from zero to four, and subsequent test runs with the rubric established what was felt to be a more effective gauge.
By semester’s end, the three students that I had enlisted for the project had explored an area of consideration that none of them were familiar with prior to engaging in this research, and which (if truth be told) their instructor was equally clueless about. We engaged in a collaborative research methodology that was exciting and instructive to us all, while allowing ourselves the flexibility to wander where the research led us. At the outset, we had the pleasure of existing with no thesis to prove or specific goal to attain. We were free to go where the topic took us, and we allowed our interests to converge and engage us in discussion and debate. My students evolved into my collaborators, and we permitted ourselves to simply discover without grades or assessment of performance. In the end we circled back and applied our discoveries to the work that most interests us, and now have an early iteration of a professional assessment tool that we will begin to implement in our individual work.
I have no doubt that this was as informative and engaging an experience for these students, as I know it was for me. The opportunity for undergraduates to work side by side with their professors in exploring and developing work is extremely beneficial to all involved. I encourage my colleagues to take advantage of any such opportunities they may be afforded and create their own opportunities (through grants and the like) if such opportunities are not available in their institutions. In the coming year, the three students involved in this project will be graduating from our AAS program in Game Design and moving on to Baccalaureate programs at four-year colleges across the country. They have promising careers ahead and an admiring professor in their wake.
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