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Can Whole-Body Tracing and Hand Tracing Make Any Difference? Experimental Evidence of Learning Outcomes, Cognitive Load, and Intrinsic Motivation on University Students

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Title: Can Whole-Body Tracing and Hand Tracing Make Any Difference? Experimental Evidence of Learning Outcomes, Cognitive Load, and Intrinsic Motivation on University Students
Language: English
Authors: Genmei Zuo, Lijia Lin (ORCID 0000-0002-9008-9752)
Source: Instructional Science: An International Journal of the Learning Sciences. 2025 53(1):1-25.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 25
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Freehand Drawing, Human Body, Kinesthetic Perception, Adults, Cognitive Processes, Difficulty Level, Kinesthetic Methods, Self Evaluation (Individuals), Epistemology, Learner Engagement, College Students, Student Motivation, Academic Achievement
DOI: 10.1007/s11251-024-09664-w
ISSN: 0020-4277
1573-1952
Abstract: The purpose of the study was to investigate (a) whether the effects of hand tracing and whole-body tracing reported in the literature could be extended to adults, and (b) the relative superiority of whole-body tracing over hand tracing. Two experiments were conducted to investigate the potential effects of these two kinesthetic approaches on learning outcomes, cognitive load, and intrinsic motivation. The results of Experiment 1 revealed that hand tracing enhanced germane load contingent upon a low-to-medium level of perceived difficulty. This effect disappeared in Experiment 2 where additional measures were taken to improve treatment fidelity. The findings of Experiment 2 revealed the beneficial effects of whole-body tracing on germane load, extraneous load, interest, and self-monitoring, some of which were dependent upon learners' perceived difficulty and invested effort. These findings, along with implications, limitations, and future research directions, were discussed within the framework of cognitive load theory and embodied cognition theory.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1460930
Database: ERIC
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  Value: <anid>AN0183175093;isl01feb.25;2025Feb24.04:33;v2.2.500</anid> <title id="AN0183175093-1">Can whole-body tracing and hand tracing make any difference? Experimental evidence of learning outcomes, cognitive load, and intrinsic motivation on university students </title> <p>The purpose of the study was to investigate (a) whether the effects of hand tracing and whole-body tracing reported in the literature could be extended to adults, and (b) the relative superiority of whole-body tracing over hand tracing. Two experiments were conducted to investigate the potential effects of these two kinesthetic approaches on learning outcomes, cognitive load, and intrinsic motivation. The results of Experiment 1 revealed that hand tracing enhanced germane load contingent upon a low-to-medium level of perceived difficulty. This effect disappeared in Experiment 2 where additional measures were taken to improve treatment fidelity. The findings of Experiment 2 revealed the beneficial effects of whole-body tracing on germane load, extraneous load, interest, and self-monitoring, some of which were dependent upon learners' perceived difficulty and invested effort. These findings, along with implications, limitations, and future research directions, were discussed within the framework of cognitive load theory and embodied cognition theory.</p> <p>Keywords: Tracing; Cognitive load; Embodied cognition; Learning; Psychology and Cognitive Sciences Psychology</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0183175093-2">Introduction</hd> <p>Research has indicated that physical movements positively affect learning and cognition (Mavilidi et al., [<reflink idref="bib47" id="ref1">47</reflink>]). Findings from the existing literature further show that hand tracing has the potential to improve learning and optimize cognitive load (Ginns & Kydd, [<reflink idref="bib22" id="ref2">22</reflink>]). In addition, tracing learning materials that involve one's body movements has also been found to have benefits (Johnson-Glenberg et al., [<reflink idref="bib32" id="ref3">32</reflink>]). However, there is insufficient empirical evidence to demonstrate the superiority of whole-body movement over hand tracing. To address this issue, we conducted two experiments to specifically compare the effects of these two types of kinesthetic learning on retention, transfer, cognitive load, and intrinsic motivation.</p> <hd id="AN0183175093-3">Theoretical foundations</hd> <p>According to theories of embodied cognition or grounded cognition, humans' internal cognitive processes are influenced by their body and the external environment (Barsalou, [<reflink idref="bib7" id="ref4">7</reflink>], [<reflink idref="bib8" id="ref5">8</reflink>]; Wilson, [<reflink idref="bib70" id="ref6">70</reflink>]). Piaget emphasized the important role of sensorimotor actions in children's early developmental stages. Specifically, he argued that schemas formed at the sensorimotor level can lay a solid foundation for children's later acquisition of symbolic and abstract information (Piaget & Inhelder, [<reflink idref="bib53" id="ref7">53</reflink>]). Both neuroscience and behavioral research has provided supporting evidence, showing that physical movements and cognitive processes are intertwined. For instance, Hauk et al. ([<reflink idref="bib28" id="ref8">28</reflink>]) presented participants with action words that were related to their arms, faces, and legs (e.g., lick, pick, or kick). Using functional MRI, they observed that the brain regions associated with the sensorimotor systems were activated. In addition, findings revealed by Zwaan and Taylor ([<reflink idref="bib77" id="ref9">77</reflink>]) and Glenberg and Kaschak ([<reflink idref="bib25" id="ref10">25</reflink>]), indicate that language cognition and body movement are interconnected. Zwaan and Taylor asked the participants to listen to sentences describing rotations (e.g., "He turned down the volume.") and to make sensibility judgments by recording their response times to turn a knob. The researchers found that, when the direction implied in the sentence was congruent with the direction of turning the knob, the participants' response time was shorter than the response time in the incongruent condition. Recent studies focusing on how to learn statistical models shed more light on the embodied cognition effect (Zhang et al., [<reflink idref="bib73" id="ref11">73</reflink>]). In two experiments, participants were instructed to use their hand gestures to mimic the placement and orientation of rectangular bars presented in a video that explained the concept of a statistical model. The researchers found that college students whose hand gestures matched the movement of the rectangular bars in the video had the highest scores on the learning outcome posttest, compared to those whose hand gestures did not match the content in the video. Wilson ([<reflink idref="bib70" id="ref12">70</reflink>]) further pointed out that physical movement is a way for people to bypass the bottleneck in their cognitive system by offloading work onto the environment. This viewpoint is not only supported by a number of empirical studies (e.g., Glenberg et al., [<reflink idref="bib26" id="ref13">26</reflink>]), but also overlaps with the recent development of cognitive load theory (Sweller et al., [<reflink idref="bib64" id="ref14">64</reflink>]).</p> <p>Cognitive load theory assumes that human cognitive architecture consists of sensory memory, working memory, and long-term memory (Sweller et al., [<reflink idref="bib62" id="ref15">62</reflink>]). While long-term memory has almost unlimited storage, working memory is limited in terms of its capacity and duration for holding information. Based on these tenets, scholars have proposed the concept of cognitive load, which is "any demands on working memory storage and processing of information" (Schnotz & Kurschner, [<reflink idref="bib54" id="ref16">54</reflink>], pp. 471). According to cognitive load theory, three types of cognitive load are differentiated, which are intrinsic load, extraneous load, and germane load (Sweller et al., [<reflink idref="bib64" id="ref17">64</reflink>]). Intrinsic load is driven by the inherent difficulty or complexity of the learning material. It is also influenced by an individual's existing knowledge, as the difficulty is determined not merely by the material per se but also by learners with different levels of knowledge. Extraneous load is caused by inappropriate instructional design and presentation of the learning material. It taps the instructional features that do not contribute to learning. The concept of germane load was originally defined as the effort contributed to understanding and was believed that the decrease of extraneous load would lead to the increase of germane load (Sweller et al., [<reflink idref="bib62" id="ref18">62</reflink>]). As the theory further advances, germane load was reconceptualized as working memory resources devoted to intrinsic load (Sweller et al., [<reflink idref="bib63" id="ref19">63</reflink>]), and recently redefined as the redistribution of working memory resources (Sweller et al., [<reflink idref="bib64" id="ref20">64</reflink>]). Although the concept of germane load is still ambiguous to some extent, researchers have been empirically measuring germane load, as well as intrinsic load and extraneous load, in the survey and experimental research for the past decade (Cook et al., [<reflink idref="bib13" id="ref21">13</reflink>]; Klepsch et al., [<reflink idref="bib33" id="ref22">33</reflink>]; Leppink et al., [<reflink idref="bib37" id="ref23">37</reflink>], 2014; Sewell et al., [<reflink idref="bib56" id="ref24">56</reflink>]; Zukic et al., [<reflink idref="bib75" id="ref25">75</reflink>]). These findings have provided empirical evidence of a three-component structure of cognitive load, which sheds light on how to measure cognitive load in modern times. With that in mind, what educational researchers and instructional designers have been working on is to overcome the obstacles of working memory by appropriate design and development of instruction.</p> <p>Inspired by evolutionary educational psychology (Geary, [<reflink idref="bib20" id="ref26">20</reflink>]), scholars have further developed cognitive load theory by incorporating an evolutionary view of human cognitive structure (Paas & Sweller, [<reflink idref="bib52" id="ref27">52</reflink>]). Evolutionary educational psychology identifies biologically primary knowledge and biologically secondary knowledge. Biologically primary knowledge is formed through human evolution and can be acquired effortlessly without being constrained by working memory. For instance, human gestures and locomotion are non-verbal ways of interacting with others that we are born with. Biologically secondary knowledge, such as writing and reading, requires people to learn with deliberate effort. Paas and Sweller ([<reflink idref="bib52" id="ref28">52</reflink>]) advocated the use of biologically primary knowledge to aid the learning of biologically secondary knowledge. One potential application is to engage learners with hand gestures and body movements so that these physical activities can offload their working memory burden and thus provide learners with more cognitive resources for learning.</p> <hd id="AN0183175093-4">The tracing effect</hd> <p>The tracing effect refers to the beneficial effect generated from using fingers to dynamically trace on the surface of a learning material. Neuroscience research has indicated that people pay more attention to visual information when their hands are in close proximity to the visual display (Abrams et al., [<reflink idref="bib2" id="ref29">2</reflink>]). Based on this finding, some scholars argue that tracing can function as a visual cueing device to guide learners' attention (Talsma et al., [<reflink idref="bib65" id="ref30">65</reflink>]; Tang et al., [<reflink idref="bib66" id="ref31">66</reflink>]). As a result, tracing has the potential to reduce extraneous cognitive load. This claim is supported by findings revealed in a recent experiment. Tang et al. asked elementary school students to learn about the water cycle by either tracing it on paper or studying it. Those students were later tested for their recall of key terms and capacity to transfer their understanding to novel problems, as well as their perceived intrinsic and extraneous cognitive load. The posttest results revealed the benefits of tracing in terms of reducing extraneous cognitive load and enhancing recall and transfer with large effect sizes. An eye-tracking study by Korbach et al. ([<reflink idref="bib34" id="ref32">34</reflink>]) also demonstrated that tracing guided the focus of visual attention in the multimedia learning environment. However, this study did not reveal any significant findings regarding the tracing effect on subjective ratings of cognitive load.</p> <p>Tracing may have an impact not only on extraneous cognitive load but also on intrinsic cognitive load. According to the information-packaging hypothesis, hand gestures such as tracing can help organize information by packaging it into chunks (Alibali, [<reflink idref="bib5" id="ref33">5</reflink>]). Therefore, tracing has the potential to reduce intrinsic cognitive load. In Yeo and Tzeng's experiment (2019, Experiment 1), teenage students studied mathematical worked examples by tracing instructions presented on paper or studied the same worked examples with or without textual cues (e.g., "Please pay attention to the parallel lines."). They found that tracing enhanced students' performance on the transfer test and led to shorter solution time on the transfer test. In addition, they found that students in the tracing condition experienced lower perceived test item difficulty with a large effect, an indication of reduced intrinsic load. While Ginns and Kydd ([<reflink idref="bib22" id="ref34">22</reflink>]) investigated this effect in a different domain (i.e., human physiology), their results were consistent with what Yeo and Tzeng (2019, Experiment 1) had demonstrated: tracing led to better performance and lower perceived difficulty than no tracing.</p> <p>In addition to cognitive load, incorporating a hand in the learning material may also have an impact on motivation. For instance, Wang et al. ([<reflink idref="bib69" id="ref35">69</reflink>]) found that elementary school students who were instructed to use their index finger to trace on the surface of four worked examples had higher intrinsic motivation than their peers who did not trace. The beneficial tracing effect on motivation was further supported by Ginns and King ([<reflink idref="bib21" id="ref36">21</reflink>]) and Zuo and Lin ([<reflink idref="bib76" id="ref37">76</reflink>]) where the target learners were university students.</p> <p>Despite these findings, a number of empirical studies reported a non-significant tracing effect on cognitive load but with a positive effect on learning (Agostinho et al., [<reflink idref="bib3" id="ref38">3</reflink>]; Ginns et al., [<reflink idref="bib24" id="ref39">24</reflink>]; Macken & Ginns, [<reflink idref="bib46" id="ref40">46</reflink>]). For instance, Agostinho et al. ([<reflink idref="bib3" id="ref41">3</reflink>]) instructed primary school students to trace temperature graphs with their index fingers on an iPad or without tracing. They found that students in the tracing condition had better performance on the transfer test but equivalent test item difficulty ratings – used as an index of intrinsic cognitive load – compared to their peers in the no tracing condition.</p> <p>A few studies even reported a non-significant tracing effect on learning outcomes. For instance, Yeo and Tzeng ([<reflink idref="bib72" id="ref42">72</reflink>], Experiment 2) designed their tracing condition in a way that students were asked to finger trace the multiplication and addition symbols when they were learning the laws of exponents. This type of mathematics learning was not visual-spatial in nature compared to learning angle relationship that was used in Experiment 1. They found that hand tracing on the non-spatial material did not lead to improved retention, transfer, or reduced subjective ratings of difficulty.</p> <p>The mixed findings regarding the tracing effect on learning and cognitive load could be due to the failure of taking into account an individual's perceptual processing, as evidence revealed from the latest research has indicated its potential impact on multimedia learning (Wang & Duff, [<reflink idref="bib67" id="ref43">67</reflink>]; Wang et al., [<reflink idref="bib68" id="ref44">68</reflink>]). But it can also be attributed to the congruency between the hand movements and the learning task, as research shows that hand gestures can improve learning when these activities are congruent with the learning task but can result in limited improvements or even no improvements in learning when the activities are incongruent with the learning task (Brooks & Goldin-Meadow, [<reflink idref="bib9" id="ref45">9</reflink>]; Segal, [<reflink idref="bib55" id="ref46">55</reflink>]).</p> <hd id="AN0183175093-5">The whole-body tracing effect</hd> <p>Physical activities involving whole-body movements may engage learners more deeply than hand gestures (Mavilidi et al., [<reflink idref="bib47" id="ref47">47</reflink>]; Skulmowski & Rey, [<reflink idref="bib61" id="ref48">61</reflink>]). In the recent development of cognitive load theory, scholars refer to this as the human movement effect and have argued that human movement can reduce working memory load, thus positively influencing learning and cognitive load. In addition, scholars have proposed a few frameworks to guide the design of embodied learning environments (DeSutter & Stieff, [<reflink idref="bib15" id="ref49">15</reflink>]; Johnson-Glenberg et al., [<reflink idref="bib32" id="ref50">32</reflink>]; Lindgren, [<reflink idref="bib42" id="ref51">42</reflink>]; Lindgren & Johnson-Glenberg, [<reflink idref="bib43" id="ref52">43</reflink>]; Skulmowski & Rey, [<reflink idref="bib61" id="ref53">61</reflink>]). For instance, the taxonomy and guidelines for embodied learning by Lindgren and Johnson-Glenberg ([<reflink idref="bib43" id="ref54">43</reflink>]) and Johnson-Glenberg et al. ([<reflink idref="bib32" id="ref55">32</reflink>]) specifically pointed out that a high degree of sensorimotoric engagement should involve locomotion and gestures that are congruent with the content. Based on this framework, Johnson-Glenberg et al. designed and developed an embodied mixed reality learning environment (EMRLE) condition where students learned chemistry and disease transmission via kinesthetically interacting with the environment. They found a larger learning gain from the EMRLE condition compared to a regular instruction condition. However, they did not compare learning gains of conditions with different degrees of sensorimotoric engagement. Similarly, Skulmowski and Rey ([<reflink idref="bib61" id="ref56">61</reflink>]) pointed out two factors for effective embodied learning, which are bodily engagement and task integration. Bodily engagement describes how much a learner's bodily activities are involved, whereas task integration is about how much the learner's body movement is related to the learning task.</p> <p>Existing literature shows empirical evidence to support the human movement effect on children and adolescents. For instance, Alvarez-Bueno et al. ([<reflink idref="bib6" id="ref57">6</reflink>]) meta-analyzed 36 empirical studies that compared the effects of physical activity interventions on children and adolescents. Their findings revealed small-to-medium, positive effects of such interventions on children's and adolescents' cognition and meta-cognition. A more recent systematic review conducted by Singh et al. ([<reflink idref="bib60" id="ref58">60</reflink>]) revealed somewhat consistent results. Specifically, in addition to the cognitive benefits, they also found that physical activities enhanced children's academic performance, math in particular, in six outcome variables.</p> <p>There is also specific evidence revealing the benefits of whole-body movements. For instance, Lindgren et al. ([<reflink idref="bib44" id="ref59">44</reflink>]) designed two simulations for high-school students and undergraduate students to learn crosscutting concepts in science. They found that these simulations, in which learners were engaged with their body movements, resulted in larger learning gains when compared to traditional instruction. Similarly, Abrahamson and Lindgren ([<reflink idref="bib1" id="ref60">1</reflink>]) designed and utilized an immersive virtual reality system to assist learners in understanding physics principles. Learners' movements were cued by the system so that they moved as if they were an asteroid traveling through space. The researchers found that learners with such experiences had a deeper understanding of the scientific principles.</p> <p>Two studies specifically compared different levels of embodiment (Mavilidi et al., [<reflink idref="bib47" id="ref61">47</reflink>]; Zohar & Levy, [<reflink idref="bib74" id="ref62">74</reflink>]). Mavilidi et al. ([<reflink idref="bib47" id="ref63">47</reflink>]) compared two physical exercise conditions to a gesture condition, as well as a verbal response condition to see if there was a positive effect of physical exercises on learning a foreign language. Their target student population was preschool children in childcare centers. They found that children achieved the highest learning outcome when their physical movements were congruent with the meanings of the words. Zohar and Levy ([<reflink idref="bib74" id="ref64">74</reflink>]) investigated the effects of different levels of embodiment in learning chemistry concepts. They specifically compared four types of learner-computer interaction that involve different degrees of body movements: watching a movie, interacting with a simulation with a mouse, interacting with the simulation with a joystick, and interacting with the simulation using a haptic device. Their quantitative and qualitative data revealed that the use of the haptic device that involved the largest degree of body movements led to the largest learning gains among the four conditions. Moreover, the haptic condition facilitated learners' causal understanding of the relationship among chemistry concepts.</p> <p>A close examination of the literature shows that the positive effect of whole-body movement is observed when learners' movements are well-integrated into the learning task (e.g., Lindgren et al., [<reflink idref="bib44" id="ref65">44</reflink>]). Most of these tasks involve learning topics related to science (see Mavilidi et al. for an exception). Therefore, based on the findings revealed from the current literature, the whole-body movement effect is likely to be more noticeable in the science domain than in other domains.</p> <hd id="AN0183175093-6">Overview of the present experiments</hd> <p>Most research with respect to hand tracing and whole-body tracing has targeted children, rather than more mature adults (e.g., Brooks & Goldin-Meadow, [<reflink idref="bib9" id="ref66">9</reflink>]; Tang et al., [<reflink idref="bib66" id="ref67">66</reflink>]). In addition, rarely did researchers compare the effects of one kinesthetic learning over another (for exceptions, see Mavilidi et al., [<reflink idref="bib47" id="ref68">47</reflink>] and Zohar & Levy, [<reflink idref="bib74" id="ref69">74</reflink>]), even though existing theoretical frameworks support the claim that larger body movements lead to better embodied learning (e.g., Lindgren & Johnson-Glenberg, [<reflink idref="bib43" id="ref70">43</reflink>]; Skulmowski & Rey, [<reflink idref="bib61" id="ref71">61</reflink>]). As a result, there is very limited empirical evidence reported in the literature regarding the relative superiority of whole-body tracing over hand tracing. Based on that, the purpose of the current study was two-fold. First, it is unknown whether the positive effects of whole-body tracing and hand tracing revealed in the literature could be extended to adults, an issue that we would like to address in the present study. Second, we specifically compared hand tracing to whole-body tracing to see whether tracing with larger body movements would lead to improved learning outcomes, optimized cognitive load, and enhanced intrinsic motivation. We addressed the following research questions:</p> <p></p> <ulist> <item> Would whole-body tracing and hand tracing be better than no tracing in terms of learning outcomes (i.e., retention and transfer)?</item> <p></p> <item> Would whole-body tracing and hand tracing be better than no tracing in terms of cognitive load (i.e., intrinsic load, extraneous load, and germane load)?</item> <p></p> <item> Would whole-body tracing and hand tracing be better than no tracing in terms of intrinsic motivation?</item> </ulist> <p>In two randomized experiments, we manipulated the way that a learner traced on the surface of a learning material, which was using hand tracing, whole-body tracing, or no tracing. The dependent variables were learning outcomes (retention and transfer), cognitive load, and intrinsic motivation. It is of note that both retention and transfer were taken into account because existing research on hand tracing and whole-body movements has shown that not only can learners' recall of information but also their deep understanding be impacted (e.g., Agostinho et al., [<reflink idref="bib3" id="ref72">3</reflink>]; Johnson-Glenberg et al., [<reflink idref="bib32" id="ref73">32</reflink>]). In addition to the aforementioned variables, we also accounted for perceptual processing by assessing baseline difficulty and effort using self-report rating scales.</p> <hd id="AN0183175093-7">Experiment 1</hd> <p></p> <hd id="AN0183175093-8">Method</hd> <p></p> <hd id="AN0183175093-9">Participants & design</hd> <p>The participants of Experiment 1 were a total of 59 students from a public university in Shanghai, China. They were recruited from the University's psychology subject pool, as well as a WeChat group formed by the University's students. No screening tests were used to select participants, as long as they were university students and at least 18 years old. There were 47 females (80%) and 12 males (20%) whose ages ranged from 18 to 26 (mean = 21.34, standard deviation = 2.11). Their areas of study spanned a wide range of domains, including education, psychology, computer science, art, etc. All of them were right-handed. They received a small stipend for their participation.</p> <p>All participants were randomly assigned to one of the three experimental conditions formed by a one-way, between-subjects design: the whole-body tracing condition (n = 19), the hand tracing condition (n = 20), and the no tracing condition (n = 20).</p> <hd id="AN0183175093-10">Materials</hd> <p>The learning materials consisted of (a) paper-based multimedia material, (b) audio narrations delivered via smartphones, and (c) paper-based diagrams varied across conditions.</p> <p>The multimedia material, printed on A4 paper (210 * 297 mm, 8.27 * 11.69 inch), described the human cardiovascular system. The choice of the content material was based on our understanding of the current literature, which not only provides guidelines for the design of embodied learning environments but also shows a noticeable human movement effect in the science domain (e.g., Lindgren & Johnson-Glenberg, [<reflink idref="bib43" id="ref74">43</reflink>]; Lindgren et al., [<reflink idref="bib44" id="ref75">44</reflink>]; Skulmowski & Rey, [<reflink idref="bib61" id="ref76">61</reflink>]). In addition, our previous research (Lin et al., [<reflink idref="bib39" id="ref77">39</reflink>]; Zuo & Lin, [<reflink idref="bib76" id="ref78">76</reflink>]) has shown that the blood flow in the human cardiovascular system is a dynamic process that has the potential to be traced by learners' hand gestures and body movements.</p> <p>The multimedia material had four pages, each including a static diagram, a QR code, and instructions. The four static diagrams provided visualizations of the human heart, capillaries, arteries and veins, and blood circulation. Text labels, as well as arrows, were added to the diagrams to facilitate understanding. A QR code, with textual instruction "<emph>Scan the code to access the audio</emph>", was inserted below each diagram. On top of each page was the instruction that asked the learners to study the visual content while at the same time listening to the audio narration. After piloting the material with nine participants, we provided additional textual instruction ("<emph>Audio can be listened to multiple times</emph>.") to ensure that learners obtained sufficient knowledge in the initial learning phase.</p> <p>Verbal instructional explanations about the human heart, capillaries, arteries and veins, and blood circulation were provided by four audio narrations. A graduate student, who was a native Chinese, recorded the narrations. The duration of these audios were 47 s, 44 s, 103 s, and 112 s, respectively.</p> <p>Two poster-sized diagrams were used as the material for experimental manipulation. A small-sized poster (700 * 1000 mm, 28 * 39 inch) with a diagram and text labels showing the blood circulation, was stuck to the wall (see Fig. 1). Learners in the hand tracing condition received the instruction to use their index fingers to trace the blood flow on the diagram three times while listening to the audio narration by scanning the QR code printed on the instruction sheet using their smartphones. Based on the anecdotal data collected from the pilot study that involved nine participants, we decided to instruct learners to trace three times, even though previous research instructed learners to trace five times (Tang et al., [<reflink idref="bib66" id="ref79">66</reflink>]). Learners in the no tracing condition also studied the content on this poster, but they were only asked to study the blood flow by looking at the visual and listening to the audio narration. A large-sized poster (1200 * 1800 mm, 47 * 70 inch) with the same visual elements was laid on the floor. Learners in the whole-body tracing condition received the instruction on paper to listen to the audio by using their smartphones to scan the QR code while using their feet to trace the blood flow on the diagram three times.</p> <p>Graph: Fig. 1 Hand tracing a whole-body tracing b and no tracing c in Experiment 1</p> <hd id="AN0183175093-11">Measures & instruments</hd> <p>The paper-based assessment included a retention test, a transfer test, a self-reported questionnaire, a prior knowledge self-rating scale, and a demographic survey. The retention test included 16 multiple-choice questions and two open-ended questions. The 16 multiple-choice items were adapted from the previous research (Ginns & Kydd, [<reflink idref="bib22" id="ref80">22</reflink>]; Lin et al., [<reflink idref="bib40" id="ref81">40</reflink>]). Each item has four choices, with one correct answer and three distractors. An individual could get one point for a correct choice. Therefore, an individual could achieve a maximum score of 16 points. A sample item was "<emph>In the pulmonary circulation, where does the right ventricle pump blood?</emph>" The Cronbach's α for these 16 multiple-choice items was 0.73.</p> <p>The two open-ended retention questions ("<emph>Please describe how arterial blood turns into venous blood.</emph>" and "<emph>Please describe how blood flows through the human body.</emph>") were adapted from a college-level physiology textbook. The first author developed a scoring rubric, where each correct idea unit was awarded one point. For example, "Arterial blood is the oxygenated blood in the circulatory system." is a correct idea unit and should receive one point. The first author trained the second author on how to use the scoring rubric to score the participants' responses. Then, the two authors independently scored 15 (25%) of the participants' responses and reached a high consistency (Cronbach's α = 0.93 for the first question and Cronbach's α = 0.98 for the second question). Therefore, the first author scored the remaining participants' responses. The maximum score on the retention test was 30 points.</p> <p>The transfer test consisted of three open-ended questions ("<emph>A classmate's instep is inflamed. The doctor injects anti-inflammatory drugs into his hip muscles. What is the circulation path when the drug reaches the inflamed place?</emph>", "<emph>Why do older people have cold feet and hands in winter?</emph>", and "<emph>Why can stimulating the foot promote blood circulation?</emph>"). They were adapted from Chi et al ([<reflink idref="bib10" id="ref82">10</reflink>]). Similar to the scoring of the two open-ended retention questions, the first author developed a scoring rubric where each correct idea unit should receive one point. After that, she provided training to the second author on how to use the rubric. Then, they independently scored 15 (25%) of the participants' responses and reached a high consistency (Cronbach's α = 0.96 for the first question and Cronbach's α = 1.00 for the second and third questions). Therefore, the first author scored the remaining participants' responses. The maximum score on the transfer test was 14 points.</p> <p>The self-reported questionnaire included measures that assessed learners' perceived cognitive load and intrinsic motivation after instruction, as well as a baseline measure of perceived difficulty. Cognitive load was measured by 12 self-report items with three subscales: intrinsic load, extraneous load, and germane load (see Table 1). They were adapted from previous studies (Klepsch et al., [<reflink idref="bib33" id="ref83">33</reflink>]; Krell, [<reflink idref="bib35" id="ref84">35</reflink>]; Leppink et al., [<reflink idref="bib37" id="ref85">37</reflink>]). Each item was implemented on a 7-point Likert scale, ranging from 1 ("<emph>not at all true</emph>") to 7 ("<emph>very true</emph>"). The Cronbach's α for intrinsic load, extraneous load, and germane load was 0.85, 0.68, and 0.75, respectively. The size of the Pearson correlations between these subscales ranged from small to medium (0.15 between extraneous load and intrinsic load, −0.25 between extraneous load and germane load, 0.38 between germane load and intrinsic load), showing evidence of good discriminant validity.</p> <p>Table 1 Cognitive load and intrinsic motivation measures</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Item</p></th><th align="left"><p>Subscale</p></th></tr></thead><tbody><tr><td align="left"><p>1. The learning task was very challenging</p></td><td align="left"><p>ICL</p></td></tr><tr><td align="left"><p>2. The explanations were very unclear</p></td><td align="left"><p>ECL</p></td></tr><tr><td align="left"><p>3. The learning task really enhanced my understanding of the blood circulation</p></td><td align="left"><p>GCL</p></td></tr><tr><td align="left"><p>4. I enjoyed learning the content</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>5. I believe this activity could be of some value to me</p></td><td align="left"><p>Value</p></td></tr><tr><td align="left"><p>6. The content was very difficult to me</p></td><td align="left"><p>ICL</p></td></tr><tr><td align="left"><p>7. During the learning process, it was exhausting to find the important information</p></td><td align="left"><p>ECL</p></td></tr><tr><td align="left"><p>8. The learning task really enhanced my understanding of the cardiovascular system</p></td><td align="left"><p>GCL</p></td></tr><tr><td align="left"><p>9. I thought this activity was quite enjoyable</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>10. I think this is important to learn the content</p></td><td align="left"><p>Value</p></td></tr><tr><td align="left"><p>11. The content was easy to learn. (R)</p></td><td align="left"><p>ICL</p></td></tr><tr><td align="left"><p>12. I was thinking intensively during the process of learning</p></td><td align="left"><p>GCL</p></td></tr><tr><td align="left"><p>13. I thought this was a boring activity. (R)</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>14. While I was doing this activity, I was thinking about how much I enjoyed it</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>15. During the learning process, many things needed to be kept in my mind simultaneously</p></td><td align="left"><p>ICL</p></td></tr><tr><td align="left"><p>16. This activity did not hold my attention at all. (R)</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>17. I believe the design of the learning environment was very inconvenient for learning</p></td><td align="left"><p>ECL</p></td></tr><tr><td align="left"><p>18. I did not work very hard during the process of learning. (R)</p></td><td align="left"><p>GCL</p></td></tr><tr><td align="left"><p>19. I like studying the cardiovascular system</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>20. The content was very complex</p></td><td align="left"><p>ICL</p></td></tr><tr><td align="left"><p>21. I believe doing this activity could be beneficial to me</p></td><td align="left"><p>Value</p></td></tr><tr><td align="left"><p>22. The content attracted my attention</p></td><td align="left"><p>Interest</p></td></tr><tr><td align="left"><p>23. I think that doing this activity is useful for me to know the cardiovascular system and the blood circulation</p></td><td align="left"><p>Value</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>Note</emph>: R in the parentheses indicates that the item was reverse coded <emph>ICL</emph> intrinsic cognitive load, <emph>ECL</emph> extraneous cognitive load <emph>GCL</emph> germane cognitive load</p> <p>A total of 11 items were used to assess an individual's intrinsic motivation, which included two subscales: interest and value (see Table 1). They were adapted from previous studies (Lin et al., [<reflink idref="bib41" id="ref86">41</reflink>]; McAuley et al., [<reflink idref="bib50" id="ref87">50</reflink>]). Like the cognitive load assessments, the intrinsic motivation items were also implemented on a 7-point Likert scale, ranging from 1 ("<emph>not at all true</emph>") to 7 ("<emph>very true</emph>"). The Cronbach's α for interest and value was 0.89, and 0.83, respectively. In addition, a 7-point Likert-scale item measuring learners' perceived difficulty ("<emph>I thought learning the human heart/capillaries/arteries and veins/blood circulation was very difficult</emph>." 1 ("<emph>not at all true</emph>") to 7 ("<emph>very true</emph>")) was administered to obtain learners' baseline perceptual processing about the learning material. It was implemented four times in the initial learning phase, which was arranged as the following page right after the content page describing the human heart, capillaries, arteries and veins, or blood circulation. An individual's intrinsic load, extraneous load, germane load, interest, value, and baseline perception of difficulty were computed by averaging their responses on the items.</p> <p>The prior knowledge self-rating scale consisted of two Likert-scale items and a prior knowledge checklist. The Likert-scale items asked individuals to rate their knowledge about the human cardiovascular system and physiology from 0 ("<emph>extremely unknowledgeable</emph>") to 10 ("<emph>extremely knowledgeable</emph>"). The checklist asked them to report if they had the following learning experiences: (a) taken a course in human anatomy/physiology, (b) watched an educational program on how the cardiovascular system works, (c) studied how the cardiovascular system works, (d) seen pictures of the structures of the cardiovascular system, (e) talked to a doctor about how the cardiovascular system works, and (f) read books about the cardiovascular system, or (g) none of them above. Each option, except the last one, was counted as one point. The total score of an individual's prior knowledge was computed by adding their responses to the two Likert-scale items and the prior knowledge checklist. The maximum score for prior knowledge was 26. This prior knowledge score was used as a covariate in the analysis of covariance (ANCOVA), which was reported in the results.</p> <p>The demographic questionnaire collected an individual's information about sex, age, major, and whether they were left-handed, right-handed, or both.</p> <hd id="AN0183175093-12">Procedure</hd> <p>The experiment was conducted one-on-one in a quiet laboratory. First, participants were given a consent form to read and sign. After the researcher collected the signed consent form, each participant was randomly assigned to one of the three conditions by using an experiment ID. The rationale for using experiment IDs was to preserve the anonymity of each participant. After random assignment, they began the experiment by completing the one-page demographic questionnaire and prior knowledge checklist.</p> <p>In the initial learning phase, participants were asked to study a four-page multimedia material about (a) heart, (b) capillaries, (c) arteries & veins, and (d) blood circulation. After each content page, they were asked to provide their difficulty ratings of the content on a separate page.</p> <p>Afterward, the activities varied according to the condition an individual was assigned to. Participants in the hand tracing condition traced the blood flow using their index finger three times, while those in the whole-body tracing condition traced the blood flow using their feet three times. Participants in the no tracing condition were only asked to study the diagram while keeping their hands still (see Fig. 1). In the final testing phase, all participants completed the self-report questionnaire assessing their cognitive load and intrinsic motivation, followed by the retention test and transfer test. No time restrictions were imposed on participants in the initial learning phase, experimental manipulation phase, or testing phase. But it took an individual approximately 30 min to complete the entire experiment.</p> <p>The research was approved by the University's Institutional Review Board (HR 478–2020).</p> <hd id="AN0183175093-13">Results</hd> <p>We used partial η<sups>2</sups> or Cohen's <emph>d</emph> as the effect size index. Accordingly, 0.01, 0.06, and 0.14 are considered as the η<sups>2</sups> values for small, medium, and large effect sizes, respectively, and 0.20, 0.50, and 0.80 are considered as the <emph>d</emph> values for small, medium, and large effect sizes (Cohen, [<reflink idref="bib11" id="ref88">11</reflink>]). Mean and standard deviations (SD) are presented in Table 2.</p> <p>Table 2 Descriptive statistics of learning outcomes, cognitive load, & intrinsic motivation in experiment 1</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Hand tracing</p></th><th align="left"><p>Whole-body tracing</p></th><th align="left"><p>No tracing</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>(<italic>N</italic> = 19)</p></td><td align="left"><p>(<italic>N</italic> = 19)</p></td><td align="left"><p>(<italic>N</italic> = 19)</p></td></tr><tr><td align="left" /><td align="left"><p>M<sup>a</sup> (SD<sup>b</sup>)</p></td><td align="left"><p>M (SD)</p></td><td align="left"><p>M (SD)</p></td></tr><tr><td align="left"><p>Retention<sup>c</sup></p></td><td align="left"><p>23.79 (6.05)</p></td><td align="left"><p>21.37 (5.94)</p></td><td align="left"><p>22.95 (3.78)</p></td></tr><tr><td align="left"><p>Transfer<sup>d</sup></p></td><td align="left"><p>7.00 (3.27)</p></td><td align="left"><p>6.63 (2.39)</p></td><td align="left"><p>6.32 (3.09)</p></td></tr><tr><td align="left"><p>Intrinsic load</p></td><td align="left"><p>3.63 (1.05)</p></td><td align="left"><p>3.62 (1.28)</p></td><td align="left"><p>3.39 (1.06)</p></td></tr><tr><td align="left"><p>Extraneous load</p></td><td align="left"><p>2.25 (1.01)</p></td><td align="left"><p>2.35 (1.07)</p></td><td align="left"><p>2.00 (.79)</p></td></tr><tr><td align="left"><p>Germane load</p></td><td align="left"><p>5.96 (.67)</p></td><td align="left"><p>5.68 (.86)</p></td><td align="left"><p>5.46 (.81)</p></td></tr><tr><td align="left"><p>Interest</p></td><td align="left"><p>4.88 (1.12)</p></td><td align="left"><p>4.71 (1.03)</p></td><td align="left"><p>4.87 (1.07)</p></td></tr><tr><td align="left"><p>Value</p></td><td align="left"><p>5.47 (1.06)</p></td><td align="left"><p>5.29 (.85)</p></td><td align="left"><p>5.55 (1.13)</p></td></tr></tbody></table> </ephtml> </p> <p> <sups>a</sups>M = Mean <sups>b</sups>SD = Standard Deviation <sups>c</sups>The maximum score of the retention test was 30 points. <sups>d</sups>The maximum score of the transfer test was 14 points</p> <p>Table 3 Descriptive statistics of learning outcomes, cognitive load, intrinsic motivation, & self-explanation in experiment 2</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2" /><th align="left"><p>Hand tracing</p></th><th align="left"><p>Whole-body tracing</p></th><th align="left"><p>No tracing</p></th></tr><tr><th align="left"><p>(<italic>N</italic> = 24)</p></th><th align="left"><p>(<italic>N</italic> = 24)</p></th><th align="left"><p>(<italic>N</italic> = 26)</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>M<sup>a</sup> (SD<sup>b</sup>)</p></td><td align="left"><p>M (SD)</p></td><td align="left"><p>M (SD)</p></td></tr><tr><td align="left"><p>Manipulation time<sup>c</sup></p></td><td align="left"><p>90 (41.89)</p></td><td align="left"><p>90.13 (37.77)</p></td><td align="left"><p>104.39 (71.07)</p></td></tr><tr><td align="left"><p>Retention<sup>d</sup></p></td><td align="left"><p>15.79 (4.17)</p></td><td align="left"><p>16.13 (4.37)</p></td><td align="left"><p>15.81 (4.27)</p></td></tr><tr><td align="left"><p>Transfer<sup>e</sup></p></td><td align="left"><p>10.33 (4.06)</p></td><td align="left"><p>11.42 (3.27)</p></td><td align="left"><p>10.42 (3.78)</p></td></tr><tr><td align="left"><p>Intrinsic load</p></td><td align="left"><p>4.48 (1.15)</p></td><td align="left"><p>4.35 (1.17)</p></td><td align="left"><p>4.23 (1.00)</p></td></tr><tr><td align="left"><p>Extraneous load</p></td><td align="left"><p>2.96 (1.40)</p></td><td align="left"><p>2.50 (.91)</p></td><td align="left"><p>2.59 (1.25)</p></td></tr><tr><td align="left"><p>Germane load</p></td><td align="left"><p>5.38 (1.04)</p></td><td align="left"><p>5.62 (.86)</p></td><td align="left"><p>5.66 (.61)</p></td></tr><tr><td align="left"><p>Interest</p></td><td align="left"><p>4.18 (1.17)</p></td><td align="left"><p>4.93 (1.14)</p></td><td align="left"><p>4.62 (1.09)</p></td></tr><tr><td align="left"><p>Value</p></td><td align="left"><p>5.33 (1.20)</p></td><td align="left"><p>5.60 (.90)</p></td><td align="left"><p>5.38 (1.07)</p></td></tr><tr><td align="left"><p>Paraphrase</p></td><td align="left"><p>7.79 (2.11)</p></td><td align="left"><p>6.79 (3.53)</p></td><td align="left" /></tr><tr><td align="left"><p>Incorrect self-explanations</p></td><td align="left"><p>1.50 (1.25)</p></td><td align="left"><p>2.00 (1.69)</p></td><td align="left" /></tr><tr><td align="left"><p>Monitoring statements</p></td><td align="left"><p>.79 (.14)</p></td><td align="left"><p>.42 (1.02)</p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <p> <sups>a</sups>M = Mean <sups>b</sups>SD = Standard Deviation <sups>c</sups>The unit of time is second <sups>d</sups>The maximum score of the retention test was 22 points. <sups>e</sups>The maximum score of the transfer test was 22 points</p> <p>Data collected from two neurobiology-majored individuals (one in the hand tracing condition and the other in the no tracing condition) were excluded from analysis due to their high level of prior knowledge (prior knowledge score > 19), leaving the final sample with 57 individuals.</p> <hd id="AN0183175093-14">Learning outcomes</hd> <p>Two ANCOVAs were conducted to evaluate the potential difference on retention and transfer, with the prior knowledge score as the covariate. The homogeneity-of-slope assumption was evaluated. The results did not indicate a violation of the assumption due to the fact that the interaction between the condition and the covariate was non-significant, both <emph>p</emph>s > 0.51. We therefore conducted ANCOVA but there was no significant difference across the three conditions on retention or transfer, both <emph>p</emph>s > 0.34.</p> <hd id="AN0183175093-15">Cognitive load</hd> <p>Three ANCOVAs were conducted to evaluate the potential difference on intrinsic load, extraneous load, and germane load among the whole-body tracing condition, the hand tracing condition, and the no tracing condition. The baseline perception of difficulty was used as the covariate. The homogeneity-of-slope assumption was evaluated before conducting each ANCOVA. The interaction between the condition and the baseline difficulty was non-significant for neither extraneous load nor intrinsic load, both <emph>p</emph>s > 0.39. Therefore, we proceeded with ANCOVA. The results were non-significant, both <emph>p</emph>s > 0.63.</p> <p>For germane load, the interaction between the condition and the covariate was non-significant, <emph>F</emph>(<reflink idref="bib2" id="ref89">2</reflink>, 51) = 2.93, <emph>p</emph> = 0.06. However, the size of this interaction was medium-to-large, partial <emph>η</emph><sups><emph>2</emph></sups> = 0.09, indicating that germane load in the three conditions varied as a function of the baseline difficulty (i.e., violation of the homogeneity-of-slope assumption). Therefore, simple main effect analysis was conducted to examine the differences across conditions when the covariate was one SD below the mean, at the mean, and one SD above the mean (Engqvist, [<reflink idref="bib18" id="ref90">18</reflink>]; Green & Salkind, [<reflink idref="bib27" id="ref91">27</reflink>]). The modified Shaffer sequential Bonferroni procedure was used to control the inflation of type I error rate (Donoghue, [<reflink idref="bib16" id="ref92">16</reflink>]; Shaffer, [<reflink idref="bib57" id="ref93">57</reflink>]). When the covariate was one SD below the mean, hand tracing (adjusted mean = 6.26) had significantly higher germane load than whole-body tracing (adjusted mean = 5.27), <emph>p</emph> < 0.01, Cohen's <emph>d</emph> = 0.94 (large effect), and no tracing (adjusted mean = 5.34), <emph>p</emph> = 0.02, Cohen's <emph>d</emph> = 0.81 (large effect). When the covariate was at the mean, hand tracing (adjusted mean = 5.98) had significantly higher germane load than no tracing (adjusted mean = 5.51), <emph>p</emph> = 0.04, Cohen's <emph>d</emph> = 0.70 (medium-to-large effect). When the covariate was one SD above the mean, the results of all pairwise comparisons were non-significant, all <emph>p</emph>s > 0.35.</p> <hd id="AN0183175093-16">Intrinsic motivation</hd> <p>Two ANCOVAs were conducted to evaluate the potential difference on interest and value across the three conditions, with the baseline perception of difficulty as the covariate. The homogeneity-of-slope assumption was evaluated. Both interactions between the condition and the covariate were non-significant, both <emph>p</emph>s > 0.73. ANCOVA revealed non-significant results on interest and value, both <emph>p</emph>s > 0.65.</p> <hd id="AN0183175093-17">Discussion</hd> <p>The findings of Experiment 1 indicated that the use of hand tracing on the learning material enhanced germane cognitive load. However, this positive, medium-to-large effect was dependent upon how difficult learners perceived the material. Specifically, the hand tracing effect was only noticeable if learners perceived the initial learning material to be not difficult and the effect waned if they perceived the initial material as difficult.</p> <p>To the best of our knowledge, rarely have the results regarding self-report germane load been reported in the literature, with Zuo and Lin ([<reflink idref="bib76" id="ref94">76</reflink>]) as the only exception. Some previous research utilized the one-item self-report difficulty as the assessment of the overall cognitive load (e.g., Agostinho et al., [<reflink idref="bib3" id="ref95">3</reflink>]; Ginns et al., [<reflink idref="bib23" id="ref96">23</reflink>]; Hu et al., [<reflink idref="bib30" id="ref97">30</reflink>]), whereas more recent research (e.g., Ginns et al., [<reflink idref="bib24" id="ref98">24</reflink>]; Tang et al., [<reflink idref="bib66" id="ref99">66</reflink>]) utilized multiple items to measure intrinsic load and extraneous load only. Korbach et al. ([<reflink idref="bib34" id="ref100">34</reflink>]) found, by means of eye tracking, that hand tracing led to a higher number of eye fixation transitions between texts and pictures compared to no tracing. They interpreted this result as evidence of deep cognitive processing, an indication of germane load. Based on the status quo, it is not easy to draw any conclusions regarding whether hand tracing has an effect on germane load, which calls for more empirical research.</p> <p>The results of Experiment 1 did not reveal any positive effects of whole-body tracing or hand tracing on learning outcomes or intrinsic motivation. It could be due to issues of treatment fidelity. For example, the time that participants spent in the manipulation phase may be different across the three conditions, which could contribute to the findings revealed in Experiment 1. We were unable to rule out this alternative explanation because neither did we control the time, nor did we keep track of the time. It is also possible that participants in the whole-body tracing condition and hand tracing condition were not sufficiently engaged in the experimental manipulation activity due to the fact that no specific training was provided to them. If a short training similar to Tang et al. ([<reflink idref="bib66" id="ref101">66</reflink>]) were implemented, participants might gain a better understanding of tracing and the tracing procedure. To rule out these alternative explanations, we conducted Experiment 2.</p> <hd id="AN0183175093-18">Experiment 2</hd> <p></p> <hd id="AN0183175093-19">Method</hd> <p></p> <hd id="AN0183175093-20">Participants & design</hd> <p>Participants in Experiment 2 were a total of 78 students from a public university in Shanghai, China. They were recruited from the University's psychology subject pool, as well as a WeChat group formed by the University's students. No screening tests were used to select participants, as long as they were university students and at least 18 years old. There were 67 females (86%) and 11 males (14%) whose ages ranged from 18 to 28 (mean = 22.05, standard deviation = 2.49). Their areas of study spanned a wide range of domains, including education, psychology, computer science, art, etc. All of them were right-handed. They were paid 20 RMB each for their participation.</p> <p>All participants were randomly assigned to one of the three experimental conditions formed by a one-way, between-subjects design: the whole-body tracing condition (n = 25), the hand tracing condition (n = 25), and the no tracing condition (n = 28).</p> <hd id="AN0183175093-21">Materials</hd> <p>The learning materials consisted of a) computer-based multimedia material, and b) paper-based diagrams varied across conditions (see Fig. 2). The computer-based learning material consisted of four videos that explained the human heart, capillaries, arteries and veins, and blood circulation. These videos were presented through an online survey platform (<ulink href="http://www.wjx.cn">www.wjx.cn</ulink>). The diagrams and narrations in the videos were identical to those used in Experiment 1.</p> <p>Graph: Fig. 2 Hand tracing a whole-body tracing b and no tracing c in Experiment 2</p> <hd id="AN0183175093-22">Measures & instruments</hd> <p>The computer-based assessment included a retention test, a transfer test, a self-reported questionnaire, a prior knowledge self-rating scale, and a demographic survey. They were implemented through an online survey platform (<ulink href="http://www.wjx.cn">www.wjx.cn</ulink>).</p> <p>The retention test included an identification item and 16 multiple-choice items. The identification item, adapted from a college physiology textbook, asked the learners to identify six parts of the human cardiovascular system from a static diagram by writing down their names. An individual could get one point for each correct answer. The 16 multiple-choice items (Cronbach's α = 0.76) were identical to those used in Experiment 1. Therefore, the maximum score of the retention test was 22 points.</p> <p>The transfer test consisted of five open-ended questions, three of which were identical to those used in Experiment 1. To increase the sensitivity of the test, we added two additional transfer questions, which were adapted from Chi et al. ([<reflink idref="bib10" id="ref102">10</reflink>]). These two questions were "<emph>Many anti-smoking groups claim that nicotine reacts with cells in the brain which causes smokers to become addicted to cigarettes. If smoke is inhaled into the lungs, how does nicotine get to the cells in the brain?</emph>" and "<emph>Why is exercise good for blood circulation?</emph>". The maximum score of the transfer test was 22 points.</p> <p>The self-reported questionnaire included measures that assessed learners' perceived cognitive load and intrinsic motivation after instruction, baseline measures of perceived difficulty and effort, as well as a manipulation check item. Measures of cognitive load, intrinsic motivation, and baseline difficulty were identical to those used in Experiment 1. The Cronbach's α for intrinsic load, extraneous load, and germane load was 0.84, 0.78, and 0.75, respectively. The size of the Pearson correlations between these subscales ranged from small to medium (0.45 between extraneous load and intrinsic load, −0.28 between extraneous load and germane load, and 0.04 between germane load and intrinsic load), showing good discriminant validity. The Cronbach's α for interest and value was 0.90, and 0.83, respectively. The baseline measure of perceived difficulty was identical to the one used in Experiment 1. The baseline effort item asked learners "<emph>How much effort did you invest in learning the human heart/capillaries/arteries and veins/blood circulation?</emph>". Like the baseline difficulty item, this baseline effort item was also implemented on a 7-point Likert scale, ranging from 1 ("<emph>no effort at all</emph>") to 7 ("<emph>extremely high effort</emph>"). The manipulation check item asked learners "<emph>How many times did you watch the video describing the human heart/capillaries/arteries and veins/blood circulation?</emph>". The two baseline items, as well as the manipulation check item, were implemented four times in the initial learning phase. By requiring learners to click the next page button, the difficulty item, and effort item, and the manipulation check item followed the content page describing the human heart, capillaries, arteries and veins, or blood circulation. An individual's intrinsic load, extraneous load, germane load, interest, value, and baseline perception of difficulty and effort were computed by averaging their responses on the items.</p> <p>The prior knowledge self-rating scale and the demographic survey were the same as those used in Experiment 1.</p> <p>In addition to the computer-based assessments, video recording was used during the manipulation phase to obtain the time (in seconds) each participant spent when they were whole-body tracing, hand tracing, or studying without tracing.</p> <hd id="AN0183175093-23">Procedure</hd> <p>The experiment was conducted one-on-one in a quiet laboratory. First, participants were given a consent form to read and sign. After the researcher collected the signed consent form, each participant was randomly assigned to one of the three conditions by using an experiment ID. After random assignment, participants began the experiment by using a computer to complete the demographic questionnaire and prior knowledge self-rating scale. Upon completion, they proceeded with the experiment by starting the initial learning phase. They were asked to study the human cardiovascular system by watching four videos. After each video, they were asked to complete the manipulation check item, as well as provide ratings on the baseline difficulty and effort items.</p> <p>The next phase was the experimental manipulation, which was almost identical to that in Experiment 1 with three exceptions. First, before the start of the tracing or studying activity, the researcher provided participants with a short training video that showed how the first author, who was a content expert, performed whole-body tracing or hand tracing. Specifically, in the whole-body tracing condition, a 12-s video was used to demonstrate how the researcher used her body movements to trace the blood flow on the poster laid on the floor. In the hand tracing conditions, a 17-s short video was provided to demonstrate how she traced on the poster on the wall with her index finger. The purpose of the brief training was to provide participants with a modeling example to help them understand the concept of tracing and how to trace. In the no tracing condition, a static picture showing the researcher standing and looking at the poster was provided. Second, at the beginning of the experimental manipulation phase, the researcher asked all participants in the whole-body tracing and hand tracing conditions to think aloud with two prompting questions ("<emph>How does the blood flow in the human body?</emph>" and "<emph>How does the material exchange occur during this process?</emph>"). The purpose of implementing think aloud is to engage participants in the tracing activities, a procedure that was also implemented in the previous tracing research (Tang et al., [<reflink idref="bib66" id="ref103">66</reflink>]). In the no tracing condition, the researcher asked participants to mentally think about the two questions and look at the poster on the wall while keeping their hands still. The prompting questions were adapted from Chi et al. ([<reflink idref="bib10" id="ref104">10</reflink>]) and piloted with seven participants (three in the whole-body tracing condition, two in the hand tracing condition, and two in the no tracing condition). No matter which condition an individual was assigned to, this entire process of the manipulation phase was video-recorded by the researcher.</p> <p>In the final testing phase, all participants completed the retention test, the transfer test, and the self-reported questionnaire.</p> <p>All computer-based materials and assessments were delivered through an online survey platform (<ulink href="http://www.wjx.cn">www.wjx.cn</ulink>). No time restrictions were imposed on participants in the initial learning phase, experimental manipulation phase, or testing phase. But it took an individual approximately 30 min to complete the entire experiment.</p> <p>The study was approved by the University's Institutional Review Board (HR 478–2020).</p> <hd id="AN0183175093-24">Self-explanation coding scheme</hd> <p>Learners' verbal self-explanations, obtained through the think aloud method, were coded according to a scheme developed by Ainsworth and Burcham ([<reflink idref="bib4" id="ref105">4</reflink>]). A number of researchers, such as De Koning et al. ([<reflink idref="bib14" id="ref106">14</reflink>]) and Lin and Atkinson ([<reflink idref="bib38" id="ref107">38</reflink>]), have used this scheme to code self-explanations.</p> <p> <emph>Paraphrase</emph>: Statements were coded as such if learners merely described what they had learned in their own words without adding new explanatory information. For example, a statement from our collected data "<emph>(The blood) flows out of the left atrium, then through the artery, and then through this capillary</emph>[<reflink idref="bib1" id="ref108">1</reflink>]" would fall into this category.</p> <p> <emph>Incorrect self-explanations</emph>: A statement was coded as such if learners made an incorrect explanation. For example, a statement from our collected data "<emph>The pulmonary circulation flows from the left atrium.</emph>" would be categorized as an incorrect self-explanation.</p> <p> <emph>Monitoring statements</emph>: A statement was coded as such if it indicated that learners did or did not understand the content. For example, a statement from our collected data "<emph>I can't remember where it comes from.</emph>"</p> <p>All verbal self-explanations were first transcribed into texts and then numerically coded. One researcher independently coded 27 participants' self-explanations, while the other researcher independently coded all self-explanations. They reached a consensus through open discussion once disagreement emerged. In addition, the number of words of paraphrase, incorrect self-explanations, and monitoring statements for each participant was counted.</p> <hd id="AN0183175093-25">Results</hd> <p>The total number of times each participant watched the videos was computed by tallying their responses on the four manipulation check items. A close examination of the data showed that one individual in the whole-body tracing condition reported watching 15 times, one in the hand tracing condition reported 15 times, and two in the no tracing condition reported 11 and 25 times. We considered these four participants as outliers and excluded their data from the final analysis. No individuals' data were excluded due to a high level of prior knowledge. Therefore, the final sample consisted of 74 participants. Means and SDs are presented in Table 3.</p> <hd id="AN0183175093-26">Manipulation check</hd> <p>A one-way ANOVA was conducted to evaluate whether participants spent an equivalent amount of time during the manipulation phase. The results demonstrated that there was no significant difference among the three conditions, <emph>F</emph>(<reflink idref="bib2" id="ref109">2</reflink>, 71) = 0.62, <emph>p</emph> = 0.54.</p> <hd id="AN0183175093-27">Learning outcomes</hd> <p>Two ANCOVAs were conducted to evaluate the potential difference on retention and transfer, with the prior knowledge score as the covariate. The homogeneity-of-slope assumption was evaluated. The results did not indicate a violation of the assumption due to the fact that the interaction between the condition and the covariate was non-significant, both <emph>F</emph>s < 1.00. We therefore conducted ANCOVA but there was no significant difference across the three conditions on retention or transfer, both <emph>p</emph>s > 0.32.</p> <hd id="AN0183175093-28">Cognitive load</hd> <p>Three ANCOVAs were conducted to evaluate the potential difference on intrinsic load, extraneous load, and germane load, with the baseline difficulty and effort as the covariates. The homogeneity-of-slope assumption was evaluated before conducting ANCOVA. For intrinsic load, the results did not indicate a violation of the assumption because all interactions between the condition and the two covariates were non-significant, all <emph>p</emph>s > 0.13. Therefore, ANCOVA was conducted and the results revealed a non-significant difference among the three conditions, <emph>F</emph> < 1.00.</p> <p>For germane load, results suggested that, although the interaction between the baseline difficulty and the condition was non-significant, <emph>F</emph>(<reflink idref="bib1" id="ref110">1</reflink>, 62) = 2.88, <emph>p</emph> = 0.06, the size of this effect was substantial, partial η<sups>2</sups> = 0.09 (medium-to-large). Also, there was a medium-to-large effect between the baseline effort and the condition, <emph>F</emph>(<reflink idref="bib1" id="ref111">1</reflink>, 62) = 2.96, <emph>p</emph> = 0.06, partial η<sups>2</sups> = 0.09. Therefore, the homogeneity-of-slope assumption could be violated for germane load and simple main effect analysis was conducted to examine the differences across conditions when both covariates were one SD below their means, at means, and one SD above their means. The modified Shaffer sequential Bonferroni procedure was used to control the inflation of the type I error rate. When both covariates were one SD below their means, individuals in the hand tracing condition (adjusted mean = 4.89) had significantly lower ratings on germane load than their peers in the no tracing condition (adjusted mean = 5.83), <emph>p</emph> = 0.01, Cohen's <emph>d</emph> = 0.73 (medium-to-large effect). In addition, the mean of the whole-body tracing condition (adjusted mean = 5.14) was smaller than the mean of the no tracing condition, <emph>p</emph> = 0.06, with a medium effect, Cohen's <emph>d</emph> = 0.52. When both covariates were one SD above their means, individuals in the body tracing condition (adjusted mean = 6.09) had significantly higher ratings on germane load than their peers in the no tracing condition (adjusted mean = 5.38), <emph>p</emph> = 0.05, Cohen's <emph>d</emph> = 0.55 (medium effect). The results of other pairwise comparisons were non-significant, all <emph>p</emph>s > 0.10.</p> <p>For extraneous load, results were quite similar, <emph>F</emph>(<reflink idref="bib1" id="ref112">1</reflink>, 62) = 2.30, <emph>p</emph> = 0.09, partial η<sups>2</sups> = 0.07 (medium effect), suggesting that the homogeneity-of-slope assumption may be violated. Therefore, simple main effect analysis was conducted to examine the differences across conditions when both covariates were one SD below their means, at their means, and one SD above their means. The modified Shaffer sequential Bonferroni procedure was used to control the inflation of the type I error rate. When the baseline difficulty was one SD above the mean and the baseline effort was one SD below the mean, individuals in the whole-body tracing condition (adjusted mean = 1.95) had significantly lower ratings of extraneous load than their peers in the hand tracing condition (adjusted mean = 4.41), <emph>p</emph> = 0.03, Cohen's <emph>d</emph> = 0.66 (medium-to-large effect). Moreover, the mean of the whole-body tracing condition was descriptively lower than the mean of the no tracing condition (adjusted mean = 3.52), <emph>p</emph> = 0.08, with a medium effect, Cohen's <emph>d</emph> = 0.49. When the baseline difficulty was one SD above the mean and the baseline effort was at the mean, individuals in the whole-body tracing condition (adjusted mean = 2.49) had significantly lower ratings of extraneous load than their peers in the hand tracing condition (adjusted mean = 3.82), <emph>p</emph> = 0.02, Cohen's <emph>d</emph> = 0.71 (medium-to-large effect). Moreover, the mean of the whole-body tracing condition was descriptively smaller than the mean of the no tracing condition (adjusted mean = 3.40), <emph>p</emph> = 0.08, with a medium effect, Cohen's <emph>d</emph> = 0.49.</p> <hd id="AN0183175093-29">Intrinsic motivation</hd> <p>Two ANCOVAs were conducted to evaluate the potential difference on interest and value across the three conditions, with the baseline perception of difficulty and effort as the covariates. The homogeneity-of-slope assumption was evaluated. The interactions between the condition and the covariates were non-significant, both <emph>p</emph>s > 0.16. ANCOVA revealed non-significant results on value, <emph>F</emph>(<reflink idref="bib2" id="ref113">2</reflink>, 69) = 1.58, <emph>p</emph> = 0.21, partial η<sups>2</sups> = 0.04. However, there were significant differences among the three conditions on interest, <emph>F</emph>(<reflink idref="bib2" id="ref114">2</reflink>, 69) = 3.76, <emph>p</emph> = 0.03, partial η<sups>2</sups> = 0.10 (medium-to-large effect). The modified Shaffer sequential Bonferroni procedure was used to control the inflation of the type I error rate. The results revealed that individuals in the whole-body tracing condition (adjusted mean = 5.03) had significantly higher ratings on interest than their peers in the hand tracing condition (adjusted mean = 4.19), <emph>p</emph> < 0.01, Cohen's <emph>d</emph> = 0.78 (large effect). Also, individuals in the whole-body tracing condition had significantly higher ratings than their peers in the no tracing condition (adjusted mean = 4.52), <emph>p</emph> = 0.04, Cohen's <emph>d</emph> = 0.47 (medium effect). The difference between the hand tracing condition and the no tracing condition was non-significant, <emph>p</emph> = 0.34.</p> <hd id="AN0183175093-30">Self-explanations</hd> <p>Three ANCOVAs were conducted to evaluate the potential difference on the three categories of self-explanations (i.e., paraphrase, incorrect self-explanations, and monitoring statements) between the whole-body tracing condition and the hand tracing condition, with the number of words for each category as the covariate.</p> <p>The homogeneity-of-slope assumption was evaluated for paraphrase. There was an indication that the assumption was violated because the interaction between the covariate and the condition was significant, <emph>F</emph>(<reflink idref="bib1" id="ref115">1</reflink>, 44) = 4.88, <emph>p</emph> = 0.03, partial η<sups>2</sups> = 0.10. Therefore, simple main effect analysis was conducted when the covariate was one SD below the mean, at the mean, and one SD above the mean. The modified Shaffer sequential Bonferroni procedure was used to control the inflation of the type I error rate. The results revealed that all pairwise comparisons were non-significant, all <emph>p</emph>s > 0.11.</p> <p>The homogeneity-of-slope assumption was evaluated for incorrect self-explanations. The results indicated that the interaction between the covariate and the condition was non-significant, <emph>p</emph> = 0.89. ANCOVA further revealed non-significant differences across the three conditions, <emph>F</emph>(<reflink idref="bib1" id="ref116">1</reflink>, 46) = 1.35, <emph>p</emph> = 0.25.</p> <p>The homogeneity-of-slope assumption was evaluated for monitoring statements. The results indicated a potential violation of the assumption because the interaction between the covariate and the condition was significant, <emph>F</emph>(<reflink idref="bib1" id="ref117">1</reflink>, 44) = 9.58, <emph>p</emph> < 0.01, partial η<sups>2</sups> = 0.18 (large effect). Therefore, simple main effect analysis was conducted when the covariate was equal to zero,[<reflink idref="bib2" id="ref118">2</reflink>] at the mean, and one SD above the mean. The modified Shaffer sequential Bonferroni procedure was used to control the inflation of the type I error rate. The results revealed that, when the covariate was one SD above the mean, whole-body tracing (adjusted mean = 2.02) led to significantly more monitoring statements than hand tracing (adjusted mean = 1.57), <emph>p</emph> = 0.02, Cohen's <emph>d</emph> = 0.71 (medium-to-large effect). The results of other pairwise comparisons were non-significant, both <emph>p</emph>s > 0.27.</p> <hd id="AN0183175093-31">Discussion</hd> <p>We conducted Experiment 2 to examine if learners could engage more deeply in the tracing activities. Specifically, we a) provided a brief training at the beginning of the experimental manipulation, b) asked learners in the whole-body tracing condition and the hand tracing condition to self-explain, and c) kept track of the time each learner spent in the experimental manipulation phase. Experiment 2 revealed several significant findings regarding whole-body tracing. Specifically, whole-body tracing had positive effects in terms of improving germane load, reducing extraneous load, and enhancing interest. However, these cognitive and motivational benefits were contingent upon learners' perceived difficulty and invested effort in the learning material that was initially presented to them. If they perceived the initial learning to be difficult and thus invested low-to-medium mental effort, engaging in whole-body tracing had medium-to-large effects on facilitating learners' understanding of the instructional explanations, helping them find important information, and strengthening their beliefs of having convenient learning experiences, all indicating the reduction of extraneous load. If they invested a large amount of effort in initial learning, they did allocate more cognitive resources when using their bodies tracing on the learning material, indicating the improvement of germane load. Although the results of Experiment 2 did not reveal any significant findings on retention and transfer, the results with respect to cognitive load in Experiment 2 were somewhat consistent with the research conducted by Mavilidi et al., ([<reflink idref="bib47" id="ref119">47</reflink>], [<reflink idref="bib48" id="ref120">48</reflink>], [<reflink idref="bib49" id="ref121">49</reflink>]). In a series of experiments conducted with preschool children, Mavilidi et al., ([<reflink idref="bib47" id="ref122">47</reflink>], [<reflink idref="bib48" id="ref123">48</reflink>], [<reflink idref="bib49" id="ref124">49</reflink>]) integrated physical activities into science learning and foreign language learning. Their research indicated, in terms of learning outcomes, the superiority of the integrated physical condition over a more passive condition where children were not doing any physical activities. However, their research did not measure cognitive load probably because those widely used self-report cognitive load measures were not suitable for children. Therefore, not only did Experiment 2 successfully extend the human movement effect from children to adults, but also added empirical evidence regarding its beneficial effects on cognitive load.</p> <p>In addition to the abovementioned cognitive benefits, Experiment 2 revealed that whole-body tracing also had a positive, medium effect on intrinsic motivation, interest in particular. Moreover, it showed its potential to foster learners to self-monitor their own learning process if they generate a sufficient quantity of monitoring statements. To the best of our knowledge, research on tracing or physical activities within the cognitive load theory framework has revealed very limited findings regarding motivation and meta-cognition. Only two previous studies included motivation measures and reported motivational benefits of hand tracing (Ginns & King, [<reflink idref="bib21" id="ref125">21</reflink>]; Wang et al., [<reflink idref="bib69" id="ref126">69</reflink>]). Thus, the findings of Experiment 2 significantly contribute to the literature by showing a novel way of tracing that involves learners' whole-body movement and, moreover, providing additional evidence to support integrating task-relevant physical activities into the design of instruction. Also, the findings of Experiment 2 can be a call for researchers to take into account non-cognitive factors in future studies and to re-consider the cognitive-affective theory of learning with media (Moreno, [<reflink idref="bib51" id="ref127">51</reflink>]).</p> <hd id="AN0183175093-32">General discussion</hd> <p>The purpose of the study was to investigate a) whether the effects of hand tracing and whole-body tracing reported in the literature could be extended to adults, and b) the relative superiority of whole-body tracing over hand tracing. We conducted two experiments to address the following research questions: (a) <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of learning outcomes (i.e., retention and transfer)?</emph> (b) <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of cognitive load (i.e., intrinsic load, extraneous load, and germane load)?</emph> and (c) <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of intrinsic motivation?</emph> The results of these two experiments revealed several significant findings. First, the results of Experiment 1 revealed that hand tracing enhanced germane load contingent upon a low-to-medium level of perceived difficulty. This effect disappeared in Experiment 2 where additional measures were taken to improve treatment fidelity. Second, the findings of Experiment 2 revealed the beneficial effects of whole-body tracing on germane load, extraneous load, interest, and self-monitoring, some of which were dependent upon learners' perceived difficulty and invested effort. These findings are discussed in light of the research questions below.</p> <p> <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of learning outcomes (i.e., retention and transfer)?</emph> The findings of the two experiments did not reveal any effects of whole-body tracing and hand tracing on learning outcomes assessed by the retention test or the transfer test. This is different from the findings revealed in the previous research where positive effects on learning outcomes were reported (e.g., Link et al., [<reflink idref="bib45" id="ref128">45</reflink>]; Mavilidi et al., [<reflink idref="bib47" id="ref129">47</reflink>]; Tang et al., [<reflink idref="bib66" id="ref130">66</reflink>]). One possible explanation is that most previous research targeted the young-aged student population, some studies involving elementary school students (e.g., Link et al., [<reflink idref="bib45" id="ref131">45</reflink>]; Tang et al., [<reflink idref="bib66" id="ref132">66</reflink>]) and some studies even involving preschool children (e.g., Mavilidi et al., [<reflink idref="bib47" id="ref133">47</reflink>]). As research has shown that children tend to be more physically active than adults (Sigmund et al., [<reflink idref="bib59" id="ref134">59</reflink>]), it is possible that what can be observed in preschool and elementary school children may not be noticeable in adults. In addition, the quality of learners' tracing may also be an influencing factor. For instance, a learner's retention or transfer may not be improved if she/he incorrectly traced the blood flow in the human cardiovascular system. Researchers can consider examining the tracing quality in future studies. In addition, future research can continue examining the tracing effect on different age groups to verify whether this effect is dependent upon age.</p> <p> <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of cognitive load (i.e., intrinsic load, extraneous load, and germane load)?</emph> We did find that whole-body tracing beneficially impacted learners' germane load and extraneous load contingent upon their perceived difficulty and invested effort. This adds more empirical evidence to support the embodied cognition perspective and the evolutionary perspective of cognitive load theory, which advocates the use of human movement in learning and instruction. However, the current self-report cognitive load measures may not be quite suitable for children, which could explain why some of the previous research involving children did not measure cognitive load (e.g., Mavilidi et al., [<reflink idref="bib47" id="ref135">47</reflink>]). As a result, caution should be taken when interpreting our findings because what was revealed in our adult participants may not be observed in children. Future research could consider utilizing eye tracking to measure cognitive load, a plausible way that enables direct comparisons between adults and children.</p> <p> <emph>Would whole-body tracing and hand tracing be better than no tracing in terms of intrinsic motivation?</emph> We did find that whole-body tracing enhanced learners' interest compared to hand tracing and no tracing. Nowadays, many adults are in a state of physical inactivity and they are in need of all sorts of physical activities (World Health Organization, [<reflink idref="bib71" id="ref136">71</reflink>]). Participants in our study may lack physical activities, which could lead to their increased interest when they were engaged in a learning task integrated with some sort of physical activity. However, the results of our experiments did not indicate any hand tracing effects on intrinsic motivation. One possible explanation, from the embodied cognition perspective, is that hand gesturing requires very limited body movement, which costs very little cognitive resources, which in turn leads to an equivalent level of intrinsic motivation, cognitive load, and learning outcomes compared to passively studying (i.e., no tracing). As biometric sensors have been increasingly used to measure motivation-related constructs (Conley et al., [<reflink idref="bib12" id="ref137">12</reflink>]; Kula et al., [<reflink idref="bib36" id="ref138">36</reflink>]), future research can incorporate this type of technology to detect the fluctuation of motivation during the learning process.</p> <p>We implemented self-explanation to improve the treatment fidelity. By comparing different types of self-explanations between whole-body tracing and hand tracing, we found that whole-body tracing fostered learners' self-monitoring if they generated a sufficient quantity of monitoring statements. Together with the findings regarding the germane load, it is possible that learners engaged in the whole-body tracing allocated more cognitive resources to understand the content and thus became more aware of their own learning.</p> <p>Our research does have a few limitations. First, we were unable to collect data from children, causing a generalizability issue and preventing us from directly comparing adults and children. In the existing literature, only Wang et al. ([<reflink idref="bib69" id="ref139">69</reflink>]) collected data from two different student populations (i.e., high-school students and college students). But they did not compare these two groups. More research is needed to address this issue. Second, although the tracing activities were recorded in Experiment 2, we were still unable to determine the quality of learners' tracing. For example, we were uncertain whether the learners in our study traced the blood flow correctly or traced all paths of the blood flow in the human cardiovascular system. Future research could use biometric sensors to collect process data (Conley et al., [<reflink idref="bib12" id="ref140">12</reflink>]) and data mining techniques to examine the process data (Jiang et al., [<reflink idref="bib31" id="ref141">31</reflink>]).</p> <p>Our research does have crucial educational implications. In modern times, physical activities are children's daily routines but are not part of adults' daily lives (World Health Organization, [<reflink idref="bib71" id="ref142">71</reflink>]). The results of our research suggest that instructional designers consider incorporating activities that require learners to engage in body movement. Not only can this trigger learners' interest, but also help them understand the content. Moreover, if the human body movement goes beyond hand gestures, the inclusion of this type of activity in learning may have additional benefits that hand gestures cannot bring. It is of note that our findings are based on the samples obtained from university students. Therefore, the present research findings, which support the design and implementation of physical activities in university-level courses, have important implications for instructional design in higher education.</p> <hd id="AN0183175093-33">Data availability</hd> <p>The data that support the findings of this study are available from the corresponding author, upon reasonable request.</p> <hd id="AN0183175093-34">Declarations</hd> <p></p> <hd id="AN0183175093-35">Conflict of interest</hd> <p>The author(s) declare(s) that there is no conflict of interest.</p> <hd id="AN0183175093-36">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0183175093-37"> <title> References </title> <blist> <bibl id="bib1" idref="ref60" type="bt">1</bibl> <bibtext> Abrahamson D, Lindgren RSawyer K. Embodiment and embodied design. 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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Can Whole-Body Tracing and Hand Tracing Make Any Difference? Experimental Evidence of Learning Outcomes, Cognitive Load, and Intrinsic Motivation on University Students
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Genmei+Zuo%22">Genmei Zuo</searchLink><br /><searchLink fieldCode="AR" term="%22Lijia+Lin%22">Lijia Lin</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9008-9752">0000-0002-9008-9752</externalLink>)
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  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Instructional+Science%3A+An+International+Journal+of+the+Learning+Sciences%22"><i>Instructional Science: An International Journal of the Learning Sciences</i></searchLink>. 2025 53(1):1-25.
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  Label: Availability
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 25
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Freehand+Drawing%22">Freehand Drawing</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Kinesthetic+Perception%22">Kinesthetic Perception</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Kinesthetic+Methods%22">Kinesthetic Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Evaluation+%28Individuals%29%22">Self Evaluation (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Epistemology%22">Epistemology</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11251-024-09664-w
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0020-4277<br />1573-1952
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The purpose of the study was to investigate (a) whether the effects of hand tracing and whole-body tracing reported in the literature could be extended to adults, and (b) the relative superiority of whole-body tracing over hand tracing. Two experiments were conducted to investigate the potential effects of these two kinesthetic approaches on learning outcomes, cognitive load, and intrinsic motivation. The results of Experiment 1 revealed that hand tracing enhanced germane load contingent upon a low-to-medium level of perceived difficulty. This effect disappeared in Experiment 2 where additional measures were taken to improve treatment fidelity. The findings of Experiment 2 revealed the beneficial effects of whole-body tracing on germane load, extraneous load, interest, and self-monitoring, some of which were dependent upon learners' perceived difficulty and invested effort. These findings, along with implications, limitations, and future research directions, were discussed within the framework of cognitive load theory and embodied cognition theory.
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  Data: 2025
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  Data: EJ1460930
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        Value: 10.1007/s11251-024-09664-w
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 1
    Subjects:
      – SubjectFull: Freehand Drawing
        Type: general
      – SubjectFull: Human Body
        Type: general
      – SubjectFull: Kinesthetic Perception
        Type: general
      – SubjectFull: Adults
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Difficulty Level
        Type: general
      – SubjectFull: Kinesthetic Methods
        Type: general
      – SubjectFull: Self Evaluation (Individuals)
        Type: general
      – SubjectFull: Epistemology
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Student Motivation
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
    Titles:
      – TitleFull: Can Whole-Body Tracing and Hand Tracing Make Any Difference? Experimental Evidence of Learning Outcomes, Cognitive Load, and Intrinsic Motivation on University Students
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Genmei Zuo
      – PersonEntity:
          Name:
            NameFull: Lijia Lin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0020-4277
            – Type: issn-electronic
              Value: 1573-1952
          Numbering:
            – Type: volume
              Value: 53
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Instructional Science: An International Journal of the Learning Sciences
              Type: main
ResultId 1