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Utilizing Student Socio-Coordinated Mimicry: Complex Movement Conversations in Physical Education

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Title: Utilizing Student Socio-Coordinated Mimicry: Complex Movement Conversations in Physical Education
Language: English
Authors: Rhoades, Jesse Lee (ORCID 0000-0002-4218-5088), Hopper, Timothy Frank (ORCID 0000-0002-1347-5422)
Source: Quest. 2018 70(3):275-291.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 17
Publication Date: 2018
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Physical Education, Learning Processes, Imitation, Movement Education, Biomechanics, Cooperation, Teaching Methods, Learning Theories, Team Sports, Racquet Sports, Motor Reactions, Games, Educational Research, Scientific Research, Physical Activities, Novices
DOI: 10.1080/00336297.2017.1373683
ISSN: 0033-6297
Abstract: This article explores Complexity Theory and applications of nonlinear dynamics in physical education (PE). The authors of this article discuss Complexity Theory and its applications to students and student learning. Additionally, there will be discussion of how the networking learning process associated with socio-coordinated mimicry creates a "movement conversation" between learners rooted in the relational complexity of students within a PE lesson. Finally, the authors discuss research opportunities within PE, allowing for a close collaboration between pedagogical researchers and biomechanists to examine the emergent qualities within the "movement conversations" between students. Overall, this article seeks to illustrate the possibilities of utilizing Complexity Theory within PE pedagogy and research.
Abstractor: As Provided
Number of References: 64
Entry Date: 2018
Accession Number: EJ1187787
Database: ERIC
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  Value: <anid>AN0131143750;qus01jul.18;2019May22.08:26;v2.2.500</anid> <title id="AN0131143750-1">Utilizing Student Socio-coordinated Mimicry: Complex Movement Conversations in Physical Education. </title> <p>This article explores Complexity Theory and applications of nonlinear dynamics in physical education (PE). The authors of this article discuss Complexity Theory and its applications to students and student learning. Additionally, there will be discussion of how the networking learning process associated with socio-coordinated mimicry creates a "movement conversation" between learners rooted in the relational complexity of students within a PE lesson. Finally, the authors discuss research opportunities within PE, allowing for a close collaboration between pedagogical researchers and biomechanists to examine the emergent qualities within the "movement conversations" between students. Overall, this article seeks to illustrate the possibilities of utilizing Complexity Theory within PE pedagogy and research.</p> <p>Keywords: Complexity Theory; non-conscious mimicry; pedagogical strategies; student learning</p> <hd id="AN0131143750-2">Introduction</hd> <p>Ask any physical educator to describe their learning environment and invariably the word "complex" will enter their description. Interestingly, even without a formal definition of the term complexity, these teachers may be more correct in their assertion than even they realize. In this manuscript, we make connections between the complexity of adaptive learning systems in the natural world (Capra & Luisi, [<reflink idref="bib5" id="ref1">5</reflink>]) and the transfer of successful movement patterns between learners committed to a common task (Chartrand & Bargh, [<reflink idref="bib6" id="ref2">6</reflink>]). In particular, we focus on the concepts of socio-coordinated mimicry and "movement conversations" in complex environments. It is the authors' assertion that these concepts should allow a unique lens through which to examine pedagogical strategies in gymnasium-based physical education (PE).</p> <hd id="AN0131143750-3">Complexity Theory</hd> <p>At its most basic, Complexity Theory attempts to explain how certain systems within nature self-organize; that is, produce order from disorder, without any top-down intervention. Through the processes of self-organization, these systems tend to evolve and co-evolve with their environment. The study of complex systems is in no way a novel pursuit; however, the acknowledgement and unification of the underlying principles of complex systems does seem to be picking up pace within the hard and social sciences (Camazine, [<reflink idref="bib4" id="ref3">4</reflink>]; Witzany, [<reflink idref="bib61" id="ref4">61</reflink>]). One may even point to the seminal work of Lorenz ([<reflink idref="bib31" id="ref5">31</reflink>]) in chaos theory, in which he describes the meteorological postulation of the butterfly effect, as a beginning point for this popularization. In his description, Lorenz describes how a butterfly flapping its wings in Central Park could produce a storm on the other side of the planet. This nonlinear relationship between cause and effect are at the heart of complexity and chaos theories, respectively. However, even before Lorenz ([<reflink idref="bib31" id="ref6">31</reflink>]) developed the seeds of chaos theory, ideas of nonlinear systems and sensitivity to initial states had been discussed in many facets of science (Rosser, [<reflink idref="bib50" id="ref7">50</reflink>]). Further, notions of dynamic, adaptive, and complex systems were discussed far before Lorenz's ([<reflink idref="bib31" id="ref8">31</reflink>]) discovery, most notably, notions of self-organization and emergence can be found in the works of Darwin's Theory of natural selection (Johnson, [<reflink idref="bib23" id="ref9">23</reflink>]).</p> <p>In motor learning, notions of dynamic and complex systems were tackled early on by Karl Newell, where he gave some of the first insights into the dynamism and emergence of coordination in motor patterns (Newell, [<reflink idref="bib41" id="ref10">41</reflink>]). Other notable early authors were Pier Zanone and Scott Kelso in their discussion of dynamics, phase transitions, and learning (Zanone & Kelso, [<reflink idref="bib63" id="ref11">63</reflink>], [<reflink idref="bib62" id="ref12">62</reflink>], [<reflink idref="bib64" id="ref13">64</reflink>]). Other researchers of note include Ester Thelen, Linda Smith, and Beverly Ulrich, who studied more broadly, motor development within the human species through a dynamic systems approach (Kelso, [<reflink idref="bib25" id="ref14">25</reflink>]; Thelen, [<reflink idref="bib54" id="ref15">54</reflink>]; Thelen, Schöner, Scheier, & Smith, [<reflink idref="bib55" id="ref16">55</reflink>]; Thelen & Smith, [<reflink idref="bib56" id="ref17">56</reflink>]; Thelen, Ulrich, & Wolff, [<reflink idref="bib57" id="ref18">57</reflink>]). These researchers were at the forefront of dynamic systems applications within nonlinear motor development and learning. They laid a critical foundation for applications of Complexity Theory within the PE learning environment.</p> <p>Interestingly, whether the public recognizes it or not, a great many laypersons have been exposed to the ideas of complexity through popular media. Jeff Goldblum, for example, in <emph>Jurassic Park</emph>, discusses quite accurately chaotic systems, strange attractors, and eloquently describes how "nature finds a way" (Kennedy, Molen, & Spielberg, [<reflink idref="bib27" id="ref19">27</reflink>]). In fact, Goldblum's descriptions can be directly linked to Lorenz's ([<reflink idref="bib31" id="ref20">31</reflink>]) previous work. Additionally, Ashton Kutcher gives a great demonstration of the nonlinear effects of initial conditions in <emph>The Butterfly Effect</emph> (Rhulen et al., [<reflink idref="bib48" id="ref21">48</reflink>]), which again is a direct reference to Lorenz's ([<reflink idref="bib31" id="ref22">31</reflink>]) work with meteorological phenomena. These are just two examples of a litany of complexity and nonlinear systems references within popular culture. Much in the same way, if we examine seemingly dispersed disciplines, we find common strands linking back to notions of complexity. PE is no different; if we examine the relationship between students in a PE learning environment as more than the sum of its components, this begins to unveil the emergent, nonlinear aspects of learning for students as complex systems that, drawn together, create social environments that form a dynamic learning complex system.</p> <p>As noted by Ovens, Hopper, and Butler ([<reflink idref="bib43" id="ref23">43</reflink>]), complex systems thus become a logical starting point in understanding how Complexity Theory can affect the PE learning environment. Essentially, Complexity Theory allows researchers to examine learners as independent complex systems both evolving and co-evolving, within, as well as forming a part of the learning environment (Johnson, [<reflink idref="bib23" id="ref24">23</reflink>]). Interestingly, learning within PE is distributed in visual displays, in that it can be directly observed as learners' constructed knowledge evolves. This is made possible by the overt displays of learned psychomotor behaviors. In PE, learning realizes the idea of cognition as distributed within the emerging interactions of students within systematically designed tasks.</p> <hd id="AN0131143750-4">Complex systems</hd> <p>If we start to think of PE lessons as a complex system with teachers and students interacting in innumerable ways, this opens up a possibility of analysis through a lens of Complexity Theory. From the Complexity Theory perspectives, the PE learning environment is made up of teachers and students as complex systems unto themselves, nested within a system co-created by their actions to form a learning environment. A "complex" system is a particular form of system. A cursory examination of complexity literature allows for a delineation of three distinct categories (McMurtry, [<reflink idref="bib34" id="ref25">34</reflink>]). First are simple systems; these have a relatively few number of constituent parts, which interact following the predictions of Newtonian mechanics. Much like a gearbox, the gears interact in a very specific manner resulting in a very predictable behavior. The second category are complicated systems; these systems have a great number of parts interacting in diverse ways. Unlike simple systems, complicated systems behaviors prediction are difficult, requiring much expertise; however, these systems still fall within the predictability of linearity outlined through Newtonian mechanics (Davis & Simmt, [<reflink idref="bib12" id="ref26">12</reflink>]; Mason, [<reflink idref="bib32" id="ref27">32</reflink>]; McMurtry, [<reflink idref="bib34" id="ref28">34</reflink>]; Morrison, [<reflink idref="bib37" id="ref29">37</reflink>]). The third system type is complex. These systems are a collective of interrelated dynamic structures that cannot be reduced to discrete parts. These structures adapt based on the interaction between them and the environment that gives them coherence. The key difference between simple and complicated compared to complex systems is that with the latter you cannot reduce the system to its salient parts. Even though complicated systems can often produce what seems to be a chaotic and nonlinear behavior (Alhadeff‐Jones, [<reflink idref="bib1" id="ref30">1</reflink>]), complex systems have openness in function, as they allow an exchange of components, information, and energy with their environments (Mason, [<reflink idref="bib32" id="ref31">32</reflink>]). As noted by Capra and Luisi ([<reflink idref="bib5" id="ref32">5</reflink>]), complex systems create dynamic interactions between their components and the environment; these exchanges confound the process of system prediction. Changes in environmental conditions create pressure on these exchanges; this environmental tension elicits what is called self-organization at the system level, where parts of the system interact through a structural coupling with energy sources from the environment to reform, reorganize, and react to the environment in order to advance and survive (Capra & Luisi, [<reflink idref="bib5" id="ref33">5</reflink>]). Self-organization is an observed phenomenon within nature, where systems begin to create order among their constituent parts, with no predetermined set of instruction for said order (Prigogine & Glansdorff, [<reflink idref="bib45" id="ref34">45</reflink>]).</p> <p>Complex systems, accordingly, produce behaviors to compensate for pressure placed on the system and this self-organization occurs with no a-priori guidance. Pressure placed on a system is often referred to as a constraint, consisting of both internal and external variables that pressure the system's functioning. As a result, through their self-organization, these systems produce emergent behaviors that allow the system to successfully function within their environment (Morrison, [<reflink idref="bib37" id="ref35">37</reflink>]). An excellent example of this is featured in evolution. A species over the course of a great number of generations and prolonged periods of time will develop cumulative biological features that allow for its continued survival. These emergent biological changes are self-organized based upon feedback loops from the environment, which eliminate unsuccessful adaptations, and reinforce more successful ones. In this manner, feedback loops to the system allow emergent behaviors to be continually refined, thus producing increasingly successful behaviors. Inversely, the environment will also evolve based upon the interactions of the systems operating within it. In this way, the complex system and its environment will co-evolve, creating an evolutionary tension between the system and its environment (see Kauffman [[<reflink idref="bib24" id="ref36">24</reflink>]] for in-depth analysis of this process across different complex systems).</p> <hd id="AN0131143750-5">Attractors</hd> <p>While complex systems are inherently nonlinear and subsequently difficult to predict, it should be understood that these systems do have a measure of predictability based upon their anticipated interactions with system constraints. A useful illustration can be seen below in Figure 1. This diagram charts possible complex system behaviors, on what is called an attractor landscape. Along this landscape exists an infinite number of possible behavior configurations. If we envisioned this landscape as having peaks and valleys, much like in Figure 1, and we were to place a ball, representing our complex system, on this landscape, we will notice that the ball will roll and stop in one of the valleys. These valleys are described as attractor wells; this is to say that in these wells, the system is extremely stable. Complex systems inherently move toward stability, but evolve through instability, which is generally brought on by changes in constraints. If instability is great enough, this may dislodge the system from its current attractor well, resulting in the emergence of new behaviors. While attractors and the process of self-organization are inherently nonlinear, some attractors may be within a measure of predictability, based upon the expected stability of certain behaviors.</p> <p>Graph: Figure 1. Attractor wells.</p> <p>In this manner, the system streamlines its structure through more effective coupling to the environment as it converts energy into actions (Capra & Luisi, [<reflink idref="bib5" id="ref37">5</reflink>]; Maturana & Varela, [<reflink idref="bib33" id="ref38">33</reflink>]). Complex systems, through this process, can be observed to evolve over time in relation to continuous interactions with the defining environment of their actions (in a PE lesson, peers' actions, space, equipment, and teacher tasks/feedback) and the history of their structures engagement with the environment (Davis & Simmt, [<reflink idref="bib12" id="ref39">12</reflink>]; Mason, [<reflink idref="bib32" id="ref40">32</reflink>]; McMurtry, [<reflink idref="bib34" id="ref41">34</reflink>]; Morrison, [<reflink idref="bib37" id="ref42">37</reflink>]).</p> <hd id="AN0131143750-6">The complex PE pupil</hd> <p>What could complex systems possibly have to do with PE? Many researchers postulated that humans in general can be conceptualized as complex adaptive neurobiological systems (Hristovski, Davids, Araujo, & Passos, [<reflink idref="bib22" id="ref43">22</reflink>]). Further it is clear, through our definition of complex systems, that students inherently fit this description; they are comprised of many different system elements (i.e., muscles, neurons, bone), as well as exchanging energy, matter, and information with their environment, as they influence it and each other within it.</p> <p>Through an understanding that our students are inherently complex systems, it is clear that the constraints they are placed under should be a mediating factor in any behaviors they produce. In fact, pedagogical approaches have been developed based upon this student definition. Specifically, the constraints-led approach in motor learning embraces the concept of constraint manipulation as a valid pedagogical strategy for fostering productive emergent behaviors (Davids, Button, & Bennett, [<reflink idref="bib11" id="ref44">11</reflink>]; Renshaw, Chow, Davids, & Hammond, [<reflink idref="bib47" id="ref45">47</reflink>]). Newell ([<reflink idref="bib41" id="ref46">41</reflink>]) described constraints as existing within three distinct categories, namely environmental, task, and organismic (learner). Figure 2, adapted by Davis and Burton ([<reflink idref="bib14" id="ref47">14</reflink>]) from Newell ([<reflink idref="bib41" id="ref48">41</reflink>]), highlights this constraints-led approach.</p> <p>Graph: Figure 2. The ecological task analysis model adapted from Davis and Burton ([<reflink idref="bib14" id="ref49">14</reflink>]).</p> <p>Referring to the diagram in Figure 2, we can define the three main components in the following way.</p> <p></p> <ulist> <item> Task constraints. These constraints include the goal of the specific task, rules of the activity, and the implements or equipment used during the learning experience. The proficiency with which physical educators can manipulate task constraints like modifying equipment available to learners, or the size of playing areas, setting relevant task goals in games or enforcing specific rules for performance can shape the emergence of learners' behaviors in PE. Task constraints play a powerful role in influencing learners' intentions and are open to manipulation within an instructional setting.</item> <p></p> <item> Learner constraints. These are constraints that every student must individually address. These constraints encompass items such as muscular strength, bone density, balance, cognitive ability, expected social interaction, and past experience (Renshaw et al., [<reflink idref="bib47" id="ref50">47</reflink>]). These constraints directly affect each student individually. As these constraints change (i.e., students get stronger, get injured, get older, and have more experience), these constraints will have an effect on the overall emergent behaviors of the students (Newell, [<reflink idref="bib41" id="ref51">41</reflink>]; Renshaw et al., [<reflink idref="bib47" id="ref52">47</reflink>]). Learner constraints illustrate how the learners' structures affect their ability to learn skills simply by the constraints that their own body and past experiences place them under. In light of learner constraints, the concept of readiness takes on new meaning, as the simple act of readiness may merely be precipitated by a phase transition in motor pattern that is the product of the student reaching some system critical mass and thus producing the observed "readiness."</item> <p></p> <item> Environmental constraints. These constraints could consist of the type of gymnasium, air temperature, ground friction, social setting, lighting conditions, and ambient noise level, or anything within the learning environment. Again, as with any constraint, as there is a change, there are levels of recursive adaptation which take place to adjust to the new constraints that a task must be performed under (Newell, [<reflink idref="bib41" id="ref53">41</reflink>]; Renshaw et al., [<reflink idref="bib47" id="ref54">47</reflink>]). In addition, spaces where learners have opportunity to play are critical as these non-formal environmental spaces, chosen by learners, are critical to develop expertise as learner constraints adapt. In addition, critical environmental constraints have been described as "social factors like peer groups, social and cultural expectations" (). Such influences are of particular relevance for young learners, whereby "motor learning is often strongly influenced by group expectations, trends and fashions, and the presence of critical group members such as the teacher or classmates" (Chow et al., [<reflink idref="bib8" id="ref55">8</reflink>], p. 264).</item> </ulist> <p>The continuous and mutual interactive nature of each student's complex system, interacting with other students within the social system formed around common tasks, forms interacting couplings with the environment. This then creates the conditions for the constraints-led approach for nonlinear learning. Simply put, students learn in relation to prior experiences, the task at hand, and the actions of their peers in their perceptual worlds related to a common set of tasks.</p> <hd id="AN0131143750-7">Attractor states and the pupil</hd> <p>Referring back to our previously addressed concept of attractor states, and accepting PE students as complex systems, it becomes apparent that PE students may naturally discover some stable movement patterns. For example, kicking a soccer ball with the inside of the foot is a more stable pattern for accurately kicking a ball along the ground. The reason for this is that the larger surface area along the side of the foot allows for more control of the ball during the force-imparting phase of the kick. A PE student prior to experience with kicking a soccer ball may over the course of several trials kick it with either the toe or the inside of the foot. However, over a period of time, the player will experience failure with the toe kick method much more often than with the side kicking method. If there is enough turbulence created through these failures, constraints may change, instability may occur, and a new attractor state may be achieved. Of course, this outcome is heavily dependent upon the learner constraints being adequate to perform the more successful action. One of the more exciting notions of these attractor states is that they should emerge, with or without instruction. Further, if a non-productive attractor is attained, but does not or cannot provoke enough instability through constraint changes, the less productive motor pattern may persist—this is often referred to as a bad habit. In this instance, the teacher as guide is very important. They now may be able to artificially modify task and or environmental constraints in such a way as to allow the student to achieve a more productive attractor state and subsequently break out of a bad habit well. While this seemingly changes the notion of what it means to be a teacher, it emphasizes the extreme importance of a competent instructional professional. This teacher must be able to identify bad habits, and masterfully modify constraints, in such a way as to assist a student in their internal learning process.</p> <hd id="AN0131143750-8">Socio-coordinated mimicry</hd> <p>Socio-coordinated mimicry is a phenomenon that has been reported anecdotally as well as empirically, where individuals, who are participating in an activity together, will tend to alter their physical actions based upon each other's actions, intentionally or unconsciously, especially toward an action that gains most coherence with a dynamic environment (Athreya, Riley, & Davis, [<reflink idref="bib2" id="ref56">2</reflink>]; Chartrand & Bargh, [<reflink idref="bib6" id="ref57">6</reflink>]; Cheng & Chartrand, [<reflink idref="bib7" id="ref58">7</reflink>]; Condon & Sander, [<reflink idref="bib10" id="ref59">10</reflink>]; Finkel et al., [<reflink idref="bib16" id="ref60">16</reflink>]; Kendon, [<reflink idref="bib26" id="ref61">26</reflink>]; Nagasaka, Chao, Hasegawa, Notoya, & Fujii, [<reflink idref="bib38" id="ref62">38</reflink>]; Oullier, De Guzman, Jantzen, Lagarde, & Kelso, [<reflink idref="bib42" id="ref63">42</reflink>]; Shockley, Santana, & Fowler, [<reflink idref="bib52" id="ref64">52</reflink>]). This phenomenon has been found to occur in large groups as well as individual dyads. Classifications of the alterations can be described as being in mimicry/imitation or spontaneous synchronization (Chartrand & Bargh, [<reflink idref="bib6" id="ref65">6</reflink>]; Oullier et al., [<reflink idref="bib42" id="ref66">42</reflink>]). Spontaneous synchronization may occur with simple rhythmic activities, and mimicry/imitation may be exhibited with more complex skills (Néda, Ravasz, Vicsek, Brechet, & Barabási, [<reflink idref="bib40" id="ref67">40</reflink>]). Interestingly, unconscious mimicry will emerge through exchange of visual information between individuals (Fowler, Richardson, Marsh, & Shockley, [<reflink idref="bib17" id="ref68">17</reflink>]). Finally, it has been found that social rapport is a factor in the emergence of spontaneous synchronization (Lakin & Chartrand, [<reflink idref="bib28" id="ref69">28</reflink>]; Lakin, Jefferis, Cheng & Chartrand, [<reflink idref="bib7" id="ref70">7</reflink>]). This social coordinated mimicry processes, also known as the chameleon effect, under the right conditions, affects learners in a related learning task by encouraging the more effective movement patterns to emerge across a social group (Lakin, Jefferis, Cheng, & Chartrand, [<reflink idref="bib29" id="ref71">29</reflink>]).</p> <hd id="AN0131143750-9">Environmentally framed student "movement conversations"</hd> <p>If we consider students as independent complex learning systems, then learning would be construed as the emergence of motor patterns in response to environmental, task, and/or learner constraints. This notion lends itself to a very interesting and obvious constraint tension: namely, the peers of each student, who are in constant contact throughout a PE lesson, forms a part of the learning environment for every other student. Therefore, each other student should have a theoretical effect on the learning of other students.</p> <p>Interestingly, this line of logic would indicate that whenever a student learns, and subsequently performs that learned behavior, they in turn change the environmental constraints for every other student within their class. PE is particularly susceptible to these changing environmental constraints, as our primary area of learning is within the psychomotor domain. Consequently, our students can observe learning in real time, as exhibited by their peers. In as much as a conversation among friends might change the thinking of the parties to that conversation, when they are open to each other's opinions, the psychomotor interchange, through synchronization or mimicry, is akin to a conversation. Unlike a verbal conversation, however, this dialogue takes place through emergent motor patterns that are cumulative, mutual, and co-evolutionary.</p> <p>This notion and exploration of student's influence on peer learning will require the development and prescription of research strategies that describe learning environments through a lens of holism, rather than reductionism through isolated skills developed by individual learners. That is to say, if we are to observe these "movement conversations," research must be conducted on intact, in situ PE classes. Research into this phenomenon will require nearly simultaneous observation of an entire class of research participants, within the naturalistic learning environment.</p> <hd id="AN0131143750-10">Pedagogical utilization of the movement conversation</hd> <p>Many studies determined that social coordination as well as unconscious mimicry will occur based upon social contact, visual information, and social rapport (Chartrand & Bargh, [<reflink idref="bib6" id="ref72">6</reflink>]; Duarte et al., [<reflink idref="bib15" id="ref73">15</reflink>]; Latif, Barbosa, Vatiokiotis-Bateson, Castelhano, & Munhall, [<reflink idref="bib30" id="ref74">30</reflink>]; Meerhoff & De Poel, [<reflink idref="bib35" id="ref75">35</reflink>]; Richardson, Marsh, & Schmidt, [<reflink idref="bib49" id="ref76">49</reflink>]; Schmidt, O'Brien, & Sysko, [<reflink idref="bib51" id="ref77">51</reflink>]). Generally, this phenomenon has not been examined within the context of large networks of interacting dyads. An examination of the underlying networks that must form from the interconnectedness of students acting as important environmental constraints is necessary. An understanding of the underlying factors that mediate students' propensity to coordinate their motor behaviors may allow for the development of prescribed pairing strategies. This is to say that if a teacher can create optimal student pairings, students may be able to learn at an augmented rate. However, basic research into dyadic factors of social coordination must take place before such a strategy can be postulated.</p> <hd id="AN0131143750-11">Student pairings and social-coordinated mimicry</hd> <p>The concept of students acting as important environmental constraints becomes even more important when examining how students are grouped within PE class. It is quite common for a teacher to pair students by ability, or if given a choice, students seek out a person of similar ability. The result is the highly advanced students are paired together, while students who are still performing at a rudimentary level end up partnered. On its face, this approach seems logical in that the advanced students are able to perform at their own level, challenge each other in competition, while the less advanced students are not constantly failing in their attempts against a more accomplished partner. Notions of mimicry, however, challenge this pairing strategy. If students are engaged in active emergent mimicry, pairing within ability level would cause a form of stagnation to set in. That is, the high performing students would only mimic each other in basic movement patterns, while the low performing students would struggle with movement behaviors that limit their ability to progress, leading to eventual frustration and avoidance in relation to the activity. Obviously, an effective teacher tries to remedy this by rotating partners, but this can be disruptive and unpopular with students, especially in any competitive situation. Accordingly, if we wish to have students expand beyond the behaviors that they collectively developed based upon the skill grouping, we must develop methods by which students can be exposed to innovative gameplay strategies, while avoiding the motor skill pairing issues outlined above.</p> <hd id="AN0131143750-12">Social-coordinated mimicry and complexity</hd> <p>To pedagogically harness the idea of social-coordinated mimicry, we turn back to Complexity Theory. Some key aspects of Complexity Theory are the utility in the development of nonlinear instructional strategies through the application of principles such as (<reflink idref="bib1" id="ref78">1</reflink>) neighborly interactions between agents of a system, (<reflink idref="bib2" id="ref79">2</reflink>) structurally determined actions of a learner, and (<reflink idref="bib3" id="ref80">3</reflink>) the ability to draw on the diversity of agents in a system engaged in a common task (Davis & Sumara, [<reflink idref="bib13" id="ref81">13</reflink>]). Essentially, the principles indicate: (a) students in a learning environment will create nonlinear effects on the other students, especially the students who are the closest to them within the classroom or gymnasium; (b) learners will progress through a process of knowledge adoption and adaptation in a fashion dictated by the constraints under which they are placed; and (c) diversity of skill levels will be an asset to the system, as it will create turbulence within the system, thus eliciting system corrections toward attractor states that best address movement challenges for the system.</p> <p>Through these principles, it is somewhat indicated that when dealing with learning, Complexity Theory in many ways resembles constructivist notions of humans learning by finding coherence with the world. Constructivism generally describes a process by which a learner constructs knowledge at the local level through a continuous process of construing and reconstruing their experiences (Proulx, [<reflink idref="bib46" id="ref82">46</reflink>]). Feedback from these experiences crafts the learner's behavior over time, thus the learner constructs knowledge based upon their experiences interacting with their local constraints. Like in Complexity Theory, constructivism embraces some limited seeds of self-organization—however, in a much more linear fashion. The constructivist's perspective on self-organization consists of learners adapting in relation to his or her pre-existing structure, and through interactions with the environment renews through the emergence of a reforming structure (Parry, [<reflink idref="bib44" id="ref83">44</reflink>]; Proulx, [<reflink idref="bib46" id="ref84">46</reflink>]). A complexivist point of view, however, involves the learner and the world co-defining each other through nonlinear interactions and recursive feedback. As noted by Proulx ([<reflink idref="bib46" id="ref85">46</reflink>]), "The world of meaning is not in us, nor in the physical world, it is in the interaction of both in a mutually affective relationship" (p. 21). As explained by Varela and Maturana ([<reflink idref="bib60" id="ref86">60</reflink>]), "changes that result from the interaction between the living being and its environment are brought about by the disturbing agent but <emph>determined by the structure of the disturbed system</emph>" (p. 96, emphasis in the original). Through this lens, it becomes apparent that the teacher's instructional processes and the actions of learners guided by this instruction (system to be disturbed) are fundamentally both a constraint and enabler of learning based on how learners are allowed to manipulate the constraints in the environment. In particular, the relationship between learners, catalyzed by the rules and physical constraints of the game, creates the conditions for stable movement patterns to emerge.</p> <p>Hopper ([<reflink idref="bib19" id="ref87">19</reflink>]; Hopper, Sandford, & Clarke, [<reflink idref="bib21" id="ref88">21</reflink>]) suggested that games can be designed drawing on role-playing video game design strategies, where the game constraints adapt to encourage the novice player or to challenge the more advanced player. In coupling pairings, he described this as a process of modification by adaptation akin to the game teaching principles advocated by Thorpe and Bunker ([<reflink idref="bib58" id="ref89">58</reflink>]) of modification by representation and exaggeration (Hopper, [<reflink idref="bib20" id="ref90">20</reflink>]). Essentially, in modification by adaptation, the outcome of a game encounter where one player wins is that the game structure adapts to make the game play situation more challenging for the successful player. This means that games allow for competitive pairing between students of differing abilities, allowing for a leveling of the playing field between their skill levels. Such a coupling of players increases the opportunity for cross-ability mimicry in the dyad, especially if prompted by the teacher, allowing cross-ability mimicry for the less competent player, and increased challenge to overcome for the more advanced player.</p> <hd id="AN0131143750-13">Modification by adaptation game for net games</hd> <p>The next section unpacks how socio-coordinated mimicry has been observed through the progressive development of constraints for a modified game within the net/wall game category (Thorpe & Bunker, [<reflink idref="bib59" id="ref91">59</reflink>]). Figure 3 represents a series of stages that a teacher could go through to guide a group of students to play a modified game called the "Line Game."</p> <p>Graph: Figure 3. Line game phases.</p> <p>This game is designed to offer an opportunity to learn how to play the core movement elements of net games such as tennis, badminton, pickle ball, and volleyball. As shown in Figure 3, in preparation, the teacher groups students into threes with at least one player placed in the group who has more experience playing in one of the net games. The intent here is that the more experienced player developed the key movement characteristics of an effective mover in one or several of the net games. This player would then offer a visual representation attractor to the other players when engaging in the game. It has to be noted that this would only be one possible attractor state, and if the other students' learner constraints do not allow for the development of these behaviors, mimicry may not be possible. An infant, for example, can watch her parents walk all day long; however, if she lacks the strength and balance, which are key learner constraints for walking, she simply will be unable to reproduce that behavior.</p> <p>Further, in this adaptive phase, the initial rule constraints are to bounce the ball on the player's side of the line made by the markers and bounce the ball between the two markers. The player's partner attempts to catch the ball before it bounces on his/her side of the line and then sends the ball back bouncing on his/her side of the line and between the markers for the player to catch. The ball must be sent with an overarm throwing action and from where the ball was caught.</p> <p>The intent of the game in stage 1 is to play with a cooperative focus. The observer enforces these rules and by watching the game starts to see how the players develop movement patterns in response to the thrown ball; for example, a wide base with knees bent and feet staggered to enable side-to-side and forward and back movements. The cooperative focus allows the players to adapt to the rules as they coordinate their actions as guided by the teacher through questioning and demos and as reinforced by the observer.</p> <p>In Figure 3, stage 2, the players still cooperate, but now challenge each other by sending the ball in order to make each other move, experimenting with aiming to different spaces and different depths by varying the force used to send the ball. In stage 2, the observer helps the players to technically improve their overarm throwing technique and catching ability as they experiment within the rules with their positioning after sending the ball. In particular, moving to cover anticipated target areas to the side, or moving in closer to the markers if they drop the ball short to cover a softly sent ball that landed short. In stage 2, the more novice player starts to see where to go based on his/her partner's options when they send the ball. In this stage, the players are learning to control the ball as the observer is learning to help them explore different options they have when they send the ball. Key here is encouraging appropriate on-the-ball skill of sending the object and effective off-the-ball movements as advocated by Mitchell, Oslin, and Griffin ([<reflink idref="bib36" id="ref92">36</reflink>]), and explained by Hopper ([<reflink idref="bib18" id="ref93">18</reflink>]) in relation to net/wall games.</p> <p>In stage 3, the players move into a more competitive focus with points scored and service rule to start the point. However, the game adapts critically here based on the outcome of a point using the approach known as modification by adaptation. In this approach, the game structure changes so that the player who wins the points has his/her area of play increased. As shown in stage 3 part (b), an extra pair of triangle markers are added. The space between these additional markers is wider than the initial markers and becomes the target for the winning player's opponent, effectively increasing that player's target area to challenge the successful player.</p> <p>This means that the novice player can now challenge his/her more competent opponent by sending the ball to the extra space created by his/her wider target. When the novice player's system is triggered to realize how to take advantage of the increased target area, a "movement conversation" begins between the two players. Essentially, through the manipulation of task constraints, a conversation which was difficult due to skill differences at the beginning of the game is now amplified. Inherently, this conversation could not take place at the beginning of the game because, essentially, these students were speaking "moving" different languages. The more competent student was moving in such a way as to over challenge his competitor, while the lower competent student was moving in such a way as to under challenge. Through the manipulation of constraints, we build a platform by which a conversation can take place. Specifically in this case, the more competent player has to move with more efficient patterns to cover the space. Additionally, their over-arm sending motion is increasingly challenged to be more accurate as their target area becomes more difficult to hit.</p> <p>Once the novice player starts to dictate the point, move his/her more competent opponent around, anticipating where the ball with be sent back, the players engage in repeated patterns of: (a) throw to an open space, (b) move to cover the space, and (c) vary the force on the throw as they look to attack the spaces on the opponent's side. The novice player is learning to manipulate the game, and engage in a gameplay "movement conversation." The combination of the changing physical structure with the opportunity for the opponent to take advantage of this structure creates a greater potential for optimum challenge for both players. For example, the novice player can learn to manipulate space and force with the greater target area, and the more competent player is forced to cover more area, extending their more agile movement ability. At some point, the game structure and the players' ability reach a point where the outcome of the game becomes totally unpredictable within the to-and-fro exchange of the game. Success is now based on taking a risk at the right time, luck, or outsmarting the opponent.</p> <p>The dyad between two players of different abilities, framed by an adapting game environment, creates a system of feedback loops committed to the emergence of a close game. Anecdotally, we found that an observer becomes critical to this system to ensure the intent of the rules, conditions set by rules, and equipment are used to create the self-organizing system between the two players. In addition, the observer not only assists the two players through reinforcing teacher prompts and game rules, but the observer learns how to manipulate the game through watching his/her peers play. Subsequently, these strategies have been, at this point, anecdotally observed to emerge within the observer's gameplay. Essentially, the observer becomes party to a triadic movement conversation with the playing members of the game.</p> <hd id="AN0131143750-14">Deciphering meaning from the movement conversation</hd> <p>In this line of research, if we are to understand the meaning behind these student-driven movement conversations, we must be able to conceptually translate them. Within kinesiology, biomechanists represent a group of professionals who are most adept at movement observation, while pedagogists embody a group most able to translate these observations into practical pedagogical applications. These roles, as laid out within this collaborative relationship, would seem to be a match made in heaven; however, there are some major considerations that must be taken into account before such collaborative efforts could begin.</p> <p>Biomechanists have several highly specialized optical and pressure instruments for the quantification of human movement. These tools, if adapted appropriately and put to use in the PE environment, might prove to be extremely important in deciphering how students influence each other's motor behaviors. Current methods of biomechanical analysis tend to be limited to laboratory environments (Clark et al., [<reflink idref="bib9" id="ref94">9</reflink>]). Conceptually, however, it is possible for the deployment of kinematic analysis hardware within the PE environment. Setup time, subject preparation, and equipment placement, however, for these techniques, would constitute an extensive intrusion within the learning environment. This makes the deployment of these tools as currently utilized inappropriate for a naturalistic examination of learning through a lens of Complexity Theory.</p> <hd id="AN0131143750-15">Specific limitations of current techniques</hd> <p>Even in the most optimal settings, collecting kinematic data requires considerable time and effort by both the researcher and participant (Best & Begg, [<reflink idref="bib3" id="ref95">3</reflink>]). Current kinematic data collection systems, generally, observe movements through the utilization of observable as well as infrared light, in conjunction with reflective anatomical markers (Best & Begg, [<reflink idref="bib3" id="ref96">3</reflink>]). Anatomical marker placements, camera distribution, Three-Dimensional (3D) environment calibration, and observable area factoring are time consuming and technically difficult. Additionally, these systems are extremely susceptible to instability in the collection environment (i.e., cameras being bumped, anatomical markers falling off). Any PE teacher will tell you that these instabilities are an unavoidable outcome of a PE experience. However, just over the horizon are some techniques that, if explored, may prove very useful to biomechanics and pedagogy researchers.</p> <hd id="AN0131143750-16">Optical flow analysis (OFA)</hd> <p>A current technique called OFA may provide some much-needed insight into the collection of movement data as related to student's unconscious collaborations. OFA has been utilized in several studies to examine social coordination among participants engaged in verbal discussion. Specifically, OFA has been utilized to determine postural and mannerism changes that occur between participants of a verbal conversation (Latif et al., [<reflink idref="bib30" id="ref97">30</reflink>]; Sun, Nijholt, Truong, & Pantic, [<reflink idref="bib53" id="ref98">53</reflink>]). OFA utilizes filmed data and compares pixel brightness between frames. From pixel brightness, a totality of motion for the pixels is determined (Nakajima, Osa, Maekawa, & Miike, [<reflink idref="bib39" id="ref99">39</reflink>]). Rather than providing specific kinematic data, OFA offers a signal signature of observed movement.</p> <p>Research used these signatures to identify spontaneous synchronization between paired participants (Latif et al., [<reflink idref="bib30" id="ref100">30</reflink>]). This development in motion analysis eliminates many of the significant limitations for this research within some of the more common kinematic collection techniques. OFA provides a field deployable system, with little or no intrusiveness into the learning environment, which can provide specific comparable data for individual students. Further, OFA demonstrates a clear method by which pedagogical and biomechanics researchers can overcome issues inherent with observing complex phenomena. This innovation will allow researchers to begin to visualize the movement conversations taking place between students.</p> <hd id="AN0131143750-17">Conclusion and future research</hd> <p>Complexity Theory offers some innovative ideas for researching PE pedagogy. In this article, the authors attempted to illustrate some of these notions and how Complexity Theory and this idea of students as complex systems can be utilized within the learning environment and within future research. In fact, in recent months, the authors successfully applied OFA in an effort to determine if social mimicry can be captured within the gymnasium. ((Anonymized for peer review)), research note (in review) were able to identify spontaneous synchronization emerging during stationary paired basketball dribbling. It is important to emphasize that this is preliminary; however, these findings do support the notion that it is possible to capture spontaneous synchronization within the gymnasium.</p> <p>The next step for future researchers should be the determination of factors within the physical activity-based learning environment that amplify or dampen naturally occurring spontaneous synchronization and social coordinated mimicry. Once again, we should expect this, as previous studies within other environments found there are specific factors that affect propensity to synchronize, such as a common focus and motivation to engage. Finally, it should be determined if there is an effect on student achievement in sport based upon social synchronization and socio-coordinated mimicry, as well as any factors of synchronization that may amplify or dampen student achievement. This progression may seem out of order, in that the overarching goal of this research would be to determine the student benefit of socio-coordinated mimicry. However, to effectively determine the student benefits of socio-coordinated mimicry, we must first determine if it can be identified and where it most often occurs.</p> <p>Overall, the promise of this research line is the development of instructional methods that embrace students on a very individual level. 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Journal of Experimental Psychology: Human Perception and Performance, 23, 1454.</bibtext> </blist> </ref> <aug> <p>By Jesse Lee Rhoades and Timothy Frank Hopper</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib61" firstref="ref4"></nolink> <nolink nlid="nl2" bibid="bib31" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib50" firstref="ref7"></nolink> <nolink nlid="nl4" bibid="bib23" firstref="ref9"></nolink> <nolink nlid="nl5" bibid="bib41" firstref="ref10"></nolink> <nolink nlid="nl6" bibid="bib63" firstref="ref11"></nolink> <nolink nlid="nl7" bibid="bib62" firstref="ref12"></nolink> <nolink nlid="nl8" bibid="bib64" firstref="ref13"></nolink> <nolink nlid="nl9" bibid="bib25" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib54" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib55" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib56" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib57" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib27" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib48" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib43" firstref="ref23"></nolink> <nolink nlid="nl17" bibid="bib34" firstref="ref25"></nolink> <nolink nlid="nl18" bibid="bib12" firstref="ref26"></nolink> <nolink nlid="nl19" bibid="bib32" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib37" firstref="ref29"></nolink> <nolink nlid="nl21" bibid="bib45" firstref="ref34"></nolink> <nolink nlid="nl22" bibid="bib24" firstref="ref36"></nolink> <nolink nlid="nl23" bibid="bib33" firstref="ref38"></nolink> <nolink nlid="nl24" bibid="bib22" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib11" firstref="ref44"></nolink> <nolink nlid="nl26" bibid="bib47" firstref="ref45"></nolink> <nolink nlid="nl27" bibid="bib14" firstref="ref47"></nolink> <nolink nlid="nl28" bibid="bib10" firstref="ref59"></nolink> <nolink nlid="nl29" bibid="bib16" firstref="ref60"></nolink> <nolink nlid="nl30" bibid="bib26" firstref="ref61"></nolink> <nolink nlid="nl31" bibid="bib38" firstref="ref62"></nolink> <nolink nlid="nl32" bibid="bib42" firstref="ref63"></nolink> <nolink nlid="nl33" bibid="bib52" firstref="ref64"></nolink> <nolink nlid="nl34" bibid="bib40" firstref="ref67"></nolink> <nolink nlid="nl35" bibid="bib17" firstref="ref68"></nolink> <nolink nlid="nl36" bibid="bib28" firstref="ref69"></nolink> <nolink nlid="nl37" bibid="bib29" firstref="ref71"></nolink> <nolink nlid="nl38" bibid="bib15" firstref="ref73"></nolink> <nolink nlid="nl39" bibid="bib30" firstref="ref74"></nolink> <nolink nlid="nl40" bibid="bib35" firstref="ref75"></nolink> <nolink nlid="nl41" bibid="bib49" firstref="ref76"></nolink> <nolink nlid="nl42" bibid="bib51" firstref="ref77"></nolink> <nolink nlid="nl43" bibid="bib13" firstref="ref81"></nolink> <nolink nlid="nl44" bibid="bib46" firstref="ref82"></nolink> <nolink nlid="nl45" bibid="bib44" firstref="ref83"></nolink> <nolink nlid="nl46" bibid="bib60" firstref="ref86"></nolink> <nolink nlid="nl47" bibid="bib19" firstref="ref87"></nolink> <nolink nlid="nl48" bibid="bib21" firstref="ref88"></nolink> <nolink nlid="nl49" bibid="bib58" firstref="ref89"></nolink> <nolink nlid="nl50" bibid="bib20" firstref="ref90"></nolink> <nolink nlid="nl51" bibid="bib59" firstref="ref91"></nolink> <nolink nlid="nl52" bibid="bib36" firstref="ref92"></nolink> <nolink nlid="nl53" bibid="bib18" firstref="ref93"></nolink> <nolink nlid="nl54" bibid="bib53" firstref="ref98"></nolink> <nolink nlid="nl55" bibid="bib39" firstref="ref99"></nolink>
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  Data: Utilizing Student Socio-Coordinated Mimicry: Complex Movement Conversations in Physical Education
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  Data: English
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Rhoades%2C+Jesse+Lee%22">Rhoades, Jesse Lee</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-4218-5088">0000-0002-4218-5088</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hopper%2C+Timothy+Frank%22">Hopper, Timothy Frank</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-1347-5422">0000-0002-1347-5422</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Quest%22"><i>Quest</i></searchLink>. 2018 70(3):275-291.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2018
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Physical+Education%22">Physical Education</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Imitation%22">Imitation</searchLink><br /><searchLink fieldCode="DE" term="%22Movement+Education%22">Movement Education</searchLink><br /><searchLink fieldCode="DE" term="%22Biomechanics%22">Biomechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Theories%22">Learning Theories</searchLink><br /><searchLink fieldCode="DE" term="%22Team+Sports%22">Team Sports</searchLink><br /><searchLink fieldCode="DE" term="%22Racquet+Sports%22">Racquet Sports</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+Reactions%22">Motor Reactions</searchLink><br /><searchLink fieldCode="DE" term="%22Games%22">Games</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+Research%22">Scientific Research</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Activities%22">Physical Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Novices%22">Novices</searchLink>
– Name: DOI
  Label: DOI
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  Data: 10.1080/00336297.2017.1373683
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0033-6297
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article explores Complexity Theory and applications of nonlinear dynamics in physical education (PE). The authors of this article discuss Complexity Theory and its applications to students and student learning. Additionally, there will be discussion of how the networking learning process associated with socio-coordinated mimicry creates a "movement conversation" between learners rooted in the relational complexity of students within a PE lesson. Finally, the authors discuss research opportunities within PE, allowing for a close collaboration between pedagogical researchers and biomechanists to examine the emergent qualities within the "movement conversations" between students. Overall, this article seeks to illustrate the possibilities of utilizing Complexity Theory within PE pedagogy and research.
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  Data: As Provided
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  Data: 64
– Name: DateEntry
  Label: Entry Date
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  Data: 2018
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  Label: Accession Number
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  Data: EJ1187787
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        PageCount: 17
        StartPage: 275
    Subjects:
      – SubjectFull: Physical Education
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Imitation
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      – SubjectFull: Novices
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      – TitleFull: Utilizing Student Socio-Coordinated Mimicry: Complex Movement Conversations in Physical Education
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