# Involving the Body to Improve Letter Knowledge and Script: an Experimental Study in French Kindergarten

Fernando Núñez-Regueiro, Natacha Boissicat, Fanny Gimbert, Céline

Pobel-Burtin, Marie-Caroline Croset, Marie-Line Bosse, Cécile Nurra

► To cite this version:

Fernando Núñez-Regueiro, Natacha Boissicat, Fanny Gimbert, Céline Pobel-Burtin, Marie-Caroline Croset, et al. Involving the Body to Improve Letter Knowledge and Script: an Experimental Study in French Kindergarten. Educational Psychology Review, 2024, 36 (3), pp.86. 10.1007/s10648-024-09923-9. hal-04671837

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# Involving the Body to Improve Letter Knowledge and Script: An Experimental Study in French Kindergarten

Fernando Núñez-Regueiro, Natacha Boissicat, Fanny Gimbert, Céline Pobel-Burtin, Marie-

Caroline Croset, Marie-Line Bosse, and Cécile Nurra

Université Grenoble Alpes

## Author Note

Article accepted for publication in *Educational Psychology Review*. Correspondence for this article should be addressed to Fernando Núñez-Regueiro at: fernando.nunez-regueiro@univ-grenoble-alpes.fr
Abstract

Research suggests that providing children with activities that involve using their bodies to form the shapes of letters can help them acquire pre-reading skills. Little is known, however, as to the extent to which such embodied learning interventions are superior to more traditional pencil-and-paper activities, which of specific arm or body movements are most effective, and whether this approach compensates or exacerbates the learning gap between high- and low-skilled pre-readers.

Using a preregistered randomized-controlled experiment (N = 160 kindergarten students, M = 5.18 years, 54% girls), the present study assessed the educational effects of 6-week long training sessions with increasing degrees of bodily movement integration (pencil-and-paper training vs. arm training vs. arm-body training) on five measures of letter knowledge and script. Aligning with theories of embodied cognition and cognitive load in instructional designs, results showed that integrating arm movements exercises into handwriting training bore the greatest acquisitions in pre-reading skills overall, and were most beneficial to students with initially low pre-reading skills (compensatory effect against learning inequalities). Implications are drawn on the need to consolidate and replicate present findings, while highlighting their potential for supporting educational effectiveness and equity in kindergarten.

Keywords: letter knowledge; letter script; embodied learning; cognitive load theory; randomized-controlled trial
Introduction

Kindergarten skills in letter knowledge (e.g., letter shape, letter name) and letter script
(e.g., stroke quality, stroke direction) are known to be strong predictors of reading and spelling
skills during primary and secondary school, making them strategic acquisitions for positive
childhood literacy development (Castles et al., 2018; Leppanen et al., 2006; Scarborough, 2009;
Schatschneider et al., 2004; Suggate et al., 2019). But what learning environment is most
conducive to acquiring these skills during kindergarten? For letter script skills, standard practice
privileges a pencil-and-paper approach informed by handwriting exercises, in which students
observe how the teacher performs handwriting (e.g., demonstration of how to grip the pencil and
to position the paper, exemplary writing of letters on board or in the air), before doing their own
handwriting (Graham et al., 2008; Vander Hart et al., 2010). Letter knowledge instruction is
often provided in conjunction with handwriting exercises, as the teacher presents the letter shape,
letter names, and letter-sound connections associated with each worked example (Damsgaard et
al., 2022; Graham et al., 2008). In these traditional pencil-and-paper activities, students learn by
reproducing the letter names, sounds, and writing gestures demonstrated by their teacher, notably
by using effective techniques such as explicit instruction and handwriting practice exercises
(Graham et al., 2008; Vander Hart et al., 2010).

While the pencil-and-paper learning approach typically involves certain fine motor skills
associated with cursive handwriting, evidence suggests that involving the body to a larger extent,
notably to reproduce the shape of letters (e.g., using hands, arms, or whole-body movements to
trace a letter) or its symbolic association (e.g., moving like a snake to represent and learn the
letter-sound “s”), may enhance skill acquisition (Bara & Bonneton-Botté, 2018; Berninger et al.,
1997; Botha & Africa, 2020; Damsgaard et al., 2020, 2023). Complementary research is now
needed to assess whether the gains of such techniques are superior to those of traditional pencil-and-paper learning (Damsgaard et al., 2020), to determine whether the gains are distributed evenly among students with high or low initial pre-reading skills (Damsgaard et al., 2023), but also to identify what types of motor skills training (fine vs. gross motor skills) are most beneficial with regard to multiple indicators of letter knowledge and script (Bara & Bonneton-Botté, 2018). Building on embodied cognition and cognitive load theories in instructional designs (Skulmowski & Rey, 2018; Sweller et al., 2019), the present experiment tested the impact of three instructional designs with increasing levels of bodily involvement (i.e., pencil-and-paper training, arm training, arm-and-body training) on letter knowledge and script acquisitions among French preschool children (N = 160), while considering potential (in)equalitarian effects associated with students' initial skills level.

**Involving the Body to Acquire Letter Knowledge and Script: Preliminary Evidence from Embodied Learning Research**

According to embodied cognition theories (Barsalou, 2008; Versace et al., 2014), encoding processes in humans are integrated and activated within the sensorimotor systems involved in the experiences being encoded (e.g., modal systems for vision, bodily movements, affective states), a major implication being that individual learning is multimodal in nature as opposed to amodal (i.e., independent from any sensorimotor system). In particular, extant evidence from embodied learning research shows that involving sensorimotor experiences during instruction (e.g., sense of touch, arm or body movement) enhances children's acquisitions in numeric cognition, foreign language vocabulary, and spelling skills (Brooks & Goldin-Meadow, 2016; Gimbert et al., 2013; Macedonia, 2019; Mavilidi et al., 2015), but also in basic reading and writing skills. For example, experiments showed that interventions reinforcing fine-motor skills
through handwriting exercises improved the letter script and spelling skills of primary school
students (Berninger et al., 1997; Bosse et al., 2014), but also letter recognition skills of preschool
students (Longcamp et al., 2005), over interventions that did not reinforce such fine-motor skills
(i.e., receiving phonological awareness training, spelling words aloud, using a computer to type
the letter). Similarly, interventions that asked kindergarten and elementary school children to
make specific movements with their bodies or their hands to represent objects or living creatures
associated with letter-sound connections (e.g., moving like a snake to represent the letter-sound
“s”, moving slowly to represent long letter-sounds), were shown to improve learning of letter
names and letter-sound matching, as compared to an ordinary pencil-and-paper instructional
design (Damsgaard et al., 2022, 2023).

More related to the present research, three studies assessed the relative educational
effectiveness of integrating hand-, arm-, or whole-body movements to reproduce the shapes of
letters, over a control group that only explored the shape of letters visually. The motor-training
interventions implemented in a Danish study (Damsgaard et al., 2020) asked first-grade students
to reproduce the shapes of the letters “b” and “d” either with their hands (fine-motor skill group)
or with their arms (gross-motor skill group), whereas the control group asked students to find and
correctly identify the two letters on a sheet depicting a cloud of letters (composed of the two
letters), while seated and with their arms resting. The two experimental groups outperformed the
control group in terms of acquisitions in recognition accuracy of these two letters, and the fine-
motor-skills group outperformed the gross-motor-skills group as well (Damsgaard et al., 2020).
A strength of the study was its capacity to differentiate fine- and gross-motor skills training
effects, while a limitation was its confinement to a single measure of acquisition (i.e., letter
recognition of letters “b” and “d”). Similarly, two French studies involving kindergarteners (Bara
et al., 2013; Bara & Bonneton-Botté, 2018) assessed the effectiveness of a motor-training intervention that asked pupils to identify letters presented by the teacher on a chalk board and then reproduce them in the air using their arms as well as their bodies by walking along the features of the letter, with or without blindfolds. Their results (Bara et al., 2013; Bara & Bonneton-Botté, 2018) showed a positive effect of motor training (vs. control visual training) on letter recognition, on stroke direction and overall legibility of letter script, but negative effects on handwriting fluency and non-significant effects on letter-name knowledge. A strength of the French studies was their reliance on multiple measures of pre-reading skills acquisition, but a limitation was the lack of differentiation of arm- and body-motor training effects (also a limitation in Damsgaard et al., 2022, 2023), rendering the relative contribution of each training unknown. Another limitation of both the Danish and French studies was the nature of the control group that did not accurately reflect ordinary pencil-and-paper learning, because students explored letters on their own, without being guided by the teacher (see Introduction).
<figure><img src="image_2.png" /><figcaption>Figure 1. Illustration of Sensorimotor Training Across Conditions</figcaption></figure>

Note. Following a hierarchical approach on task integration of bodily movements in embodied learning (see Table 1), participants in all conditions received handwriting exercises (common training session), but differed by receiving pencil-and-paper training (pencil-and-paper training condition), arm training (arm training condition), and arm and body training (arm-body training condition). Arm and body training exercises were adapted from previous research by Bara and collaborators (Bara et al., 2013; Bara & Bonneton-Botté, 2018).
Which Sensorimotor Training Works Best? A Hierarchical Implementation of Embodied
Learning in Ordinary and Experimental Instructional Designs

As reported in the previous section, embodied learning experiments varied greatly in the kind of bodily involvement used to enhance pre-reading skills (e.g., handwriting practice, letter-sound imagined with bodily movements, letter tracing with hands, arms, or whole-body) and did not compare differences between them (except in Damsgaard et al., 2020), nor in relation to ordinary pencil-and-paper instruction (except in Damsgaard et al., 2022, 2023). Complementary research is therefore needed to characterize and test differences between ordinary and embodied learning settings, notably by pinpointing more precisely the kind of sensorimotor experiences involved in each setting and their added value. In the present study, we adopted a hierarchical approach for the design of sensorimotor training, by assuming that memory traces generated by bodily experiences more distal to the content of the activity (e.g., arm- and body-movement) need to be nested within memory traces of more proximal levels (e.g., hand-movement of handwriting) to create synergies in learning and to be tested effectively (see Table 1). Theoretical, empirical, and methodological reasons guided this approach.

Theoretically, embodied learning instruction can be described as a function of its degree of task integration and engagement of bodily movement (Skulmowski & Rey, 2018), and as a function of the cognitive load being processed across sensorimotor modalities of its design (Sweller et al., 2019). To the extent that bodily movement is task-integrated in a way that produces multisensorial traces (e.g., encoding the representation of a letter in motor and visual traces by reproducing its shape with one's arm; Bara & Bonneton-Botté, 2018; Damsgaard et al., 2020), a higher degree of bodily engagement in instruction is expected to lead to an increase in learning. This is because, according to embodied cognition theories, training that cumulates
various levels of memory traces (see Table 1) will reinforce the encoding and retrieval of letter knowledge and script, by integrating multimodal representations of the learning content across perceptual, motor and introspective states (Barsalou, 2008; Versace et al., 2014). For simplicity, and by analogy with the modality effect (Ginns, 2005), we will call this phenomenon a “multimodality effect”. Yet, from a cognitive load theory perspective, engaging the body will be effective insofar as it contributes to decrease the cognitive load associated with the learning task, as opposed to increase it. Complementary mechanisms may intervene to decrease (or increase) the cognitive load in embodied learning. For example, reproducing the shape of a letter implies distinct mental representations of the movement depending on whether a pencil, one's arm, or the whole body is used (Table 1). According to the variability effect (Paas & Van Merriënboer, 1994), experiencing these distinct representations using a hierarchical design will increase the identification of the relevant features of the trained letter (e.g., in terms of shape, size, letter stroke direction) and differentiate them from irrelevant features more specific to the pencil, arm, or whole-body gesture. The gains from the variability effect will therefore increase the cognitive load intrinsic to the learning task, and diminish the extraneous load by disposing of those irrelevant features. This, in turn, could enhance both letter knowledge and script acquisitions.

Another mechanism relates to the nature of the movements involved in letter script and to the added value of simulating these movements physically, instead of visually. From an evolutionary perspective (Geary, 2008; Paas & Sweller, 2012), physical movements represent biologically primary knowledge that humans have evolved to acquire unconsciously and with no explicit instruction, resulting in little cognitive load when processed. For instance, according to the human movement effect, representing letter script by arm or whole-body movements appeals to more immediate and effortless processes of imitation (e.g., mirror neuron system) based on
biologically primary knowledge that have lower costs for the working memory, thereby enhancing learning performance (Höffler & Leutner, 2007; van Gog et al., 2009). By contrast, symbolic representations of letters or movements (e.g., directional arrows as those on letter “b” in Figure 1) constitute secondary knowledge that humans have not evolved to develop spontaneously, which require conscious effort to be learnt and increase the cognitive load when processed. Accordingly, instructional designs that teach multiple kinds of secondary knowledge simultaneously are known to overload the working memory, resulting in impaired learning (Roussel et al., 2022; Tricot et al., 2020). From this perspective, learners in the traditional pencil-and-paper instruction might also be impaired in their learning process because they must combine two sources of secondary knowledge simultaneously, that is, one intrinsic to the learning task (tracing the trained letter) and one extraneous (interpreting the directional arrows). In sum, based on categories of knowledge and their related effects on acquisitions (i.e., human movement effect and cognitive overload effect), one might expect that embodied learning using arm- or body-movements will decrease the cognitive load associated with acquiring letter script, thus augmenting acquisitions in these skills, as compared to a traditional pencil-and-paper instruction.

However, bodily engagement may also increase the cognitive load by incorporating extraneous information, notably if it involves motor gestures that are immature in children (e.g., handwriting fluency; Bara et al., 2013) or that are less relevant to the learning content (Halvorson et al., 2019; Huff et al., 2018). This is because such motor gestures will require high levels of cognitive demands for the learner to identify them as not informative to the learning tasks (irrelevant gestures), or to master them in order to complete the task (immature gestures). In this regard, learning to read or spell words is more strongly related to the development of fine-
motor skills (i.e., fine motor precision and integration, manual dexterity, upper limb coordination) than gross-motor skills (i.e., balance, bilateral coordination, running speed and agility; Botha & Africa, 2020). One might therefore expect that embodied learning will be most effective when the instructional design ensures an optimal balance between encoding more diverse (i.e., from hand, arm, body movements) versus more relevant memory traces (i.e., fine-motor skills of hand movement). Thus, a hierarchical approach might be warranted, during which motor skills more specific to letter knowledge and script (e.g., fine-motor skills) are provided before introducing more general motor skills to alleviate the cognitive load.

Empirically, results from a pilot study partially supported to the notion that high levels of bodily engagement might not be effective, and may even be detrimental, to pre-reading skills acquisition. As reported in the Supplemental Material (SM-A), the pilot study tested the main effects on pre-reading skills of the training specific to each conditions to be presented next (i.e., pencil-and-paper, arm, and arm-body training), but without involving ordinary handwriting exercises in the arm and arm-body conditions. Results showed null or negative effects of bodily engagement on skills acquisition, which we interpreted as being due to the lack of sensorimotor experiences for developing handwriting skills more specific to letter knowledge and script (for details, see SM). As explained next, the present research corrected for this deficit and increased the ecological validity of the experiment by integrating handwriting exercises in all conditions.

Methodologically, in order to ensure that experimental findings from embodied learning research have ecological validity for practical application, the instructional design should be compatible with the time and personnel requirements commonly found in kindergarten settings.

To ensure this is the case, we designed an experiment based on ordinary pencil-and-paper training in all conditions (i.e., handwriting exercises), while adapting the hierarchical approach
mentioned above for motor-skills training in letter knowledge and script. Thus, for the pencil-and-paper training condition, 100% of the time was allocated to generating memory traces specific to the writing activity, in the form of handwriting exercises over a plastic sheet with and without letter models (specific session, 55% of time) and across various formats on slate or paper (common session, 45% of time). By contrast, in the arm training condition, 45% of the time was allocated to ordinary pencil-and-paper handwriting exercises to generate specific memory traces (common session), and 55% of the time to arm training consisting in exercises of tracing letters with one's arm, either in the air or over one's desk (specific session). Finally, the arm-body visuomotor training allocated 45% of the time to pencil-and-paper training (common session), 11% to arm training, and 44% to body training consisting in walking over the trace of a letter written in big format on the ground, on one's own and also blindfolded with the guidance of a peer (specific session; see Appendix).

In sum, this hierarchical experimental design enabled testing the relative benefits of task integrating low (pencil-and-paper training), intermediate (i.e., arm training), and high levels of embodied learning (i.e., arm-body training) in the instructional design, while minimizing the possibility that the beneficial effects of higher-level embodied learning (i.e., arm- and body-movement) were offset by a lack of memory traces from lower-level embodied learning (i.e., respectively pencil-and-paper and arm training).
<table><caption>Table 1. Hierarchical View of Memory Traces in Skills Associated with Ordinary and Experimental Training Conditions</caption><thead><tr><td>Training condition</td><td>Skills trained</td><td>Level of memory traces induced</td></tr></thead><tbody><tr><td>Pencil-and-paper training (ordinary):<br>writing the training letter over a plastic sheet under which the letter is represented or not (55%)<br>+handwriting exercises (45%)</td><td>Assembling visual features into letter perception (inhibitory skills), holding the pencil (fine motor skills), mental representation of letter script movement with pencil (haptic kinesthetic skills), coordinating visual perception and hand movement (vestibular-ocular reflex and fine motor skills)</td><td>Specific</td></tr><tr><td>Arm training (experimental):<br>reproducing the training letter by tracing its form with one's arm in the air and over the desk, with and without blindfolds (55%)<br>+handwriting exercises (45%)</td><td>Creating a gesture with one's arm (manipulative skills), mental representation of letter script movement with arm (haptic and bodily kinesthetic skills), coordinating mental representation and arm movement (manual coordination)<br>+pencil-and-paper training skills</td><td>Intermediate<br>+specific</td></tr><tr><td>Arm-body training (experimental):<br>walking the outline of the training letter written on the ground, with and without blindfolds (44%)<br>+arm training (11%)<br>+handwriting exercises (45%)</td><td>Walking along an outline (locomotor skills), mental representation of walking over the outline (bodily kinesthetic skills), coordinating mental representation and body movement (body coordination)<br>+arm training skills<br>+pencil-and-paper training skills</td><td>General<br>+intermediate<br>+specific</td></tr></tbody></table>

Note. In the present study, training conditions are nested. Thus, arm training involves ordinary handwriting exercises, but no body training; it therefore combines sensorimotor memory traces from specific and intermediate levels of experience of letter knowledge and script. By contrast, pencil-and-paper training only involves specific memory traces (no arm and no body training), whereas arm-body training involves all three levels (specific, intermediate, general; see Method and Appendix for a detailed presentation of training sessions across conditions).
The Potential Hidden Moderator of Students’ Initial Skills Level

Learning gaps between students with initially high and low skills in basic reading skills (e.g., decoding skills, decoding fluency) have been shown to increase over time, in so-called “Matthew effects” (Pfost et al., 2014). Innovative instructional designs should therefore be used with a degree of cautious and evaluated for the possibility that they may contribute to exacerbating such Matthew effects (e.g., minimally guided instruction) or, on the contrary, to compensating them (e.g., response to intervention designs; Kirschner et al., 2006; Núñez-Regueiro, 2022; Slavin et al., 2011). Accordingly, it is important that we assess whether experimental training (see previous section) differentially affects learning processes for low- and high-skilled students. Cognitive load theory in instructional design can inform this issue by anticipating two opposite hypotheses related to the multimodality effect (Choi et al., 2014; Sweller et al., 2019).

On the one hand, the expertise reversal effect (Kalyuga et al., 2012) anticipates that the beneficial effect of combining multiple modalities (e.g., specific, intermediate, and general sensorimotor processes) will be more pronounced for initially low-skilled pre-readers (“novices”) than for initially high-skilled pre-readers (“experts”). This is because multisensorial processes can be informative for novices who will gather complementary information across modalities to apprehend the learning content (increased intrinsic load), but uninformative for experts who already have a schema of this content and who will unnecessarily tax cognitive resources to attend multiple modalities (increased extraneous load: Kalyuga et al., 2012). Also, reproducing the same concept (e.g., letter shape) across multiple modalities might create detrimental redundancies in the learning process of students already familiar with the concept (redundancy effect; Chandler & Sweller, 1991). In this perspective, one would expect that
training combining modalities (e.g., pencil-and-paper training, arm training, arm-body training) would be more beneficial to low-skilled readers than traditional pencil-and-paper training due to their increased exposure to the learning gains of multimodal, embodied learning.

On the other hand, the worked example effect (Renkl, 2014; Sweller & Cooper, 1985) anticipates that low-skilled learners benefit more (vs. high-skilled learners) from the provision of extensive illustrations and schemas about the specific learning activity (e.g., writing with a pencil), because this familiarizes them with the basic conventions or principles (e.g., stroke direction, position of the hand while holding the pen) undergirding knowledge construction. Relatedly, the “sleeper” effect (Brooks & Goldin-Meadow, 2016) anticipates that embodied learning instruction is most effective for students who are already familiar with the target learning content. In this perspective, pencil-and-paper training would be expected to benefit more low-skilled pre-readers (vs. high-skilled pre-readers), by exposing them more durably to external representation (examples of letters) and exercises (writing over the letter) directly related to the learning content (worked example effect); conversely, arm and arm-body training would be expected to benefit more high-skilled pre-readers (vs. low-skilled) due to their familiarity with the learning concept at stake (sleeper effect).

More generally, based on the above expectations, investigating interaction effects between training conditions and students’ initial skills level can inform not only on the relevance of using such training for compensating Matthew effects, but also on the mechanism at stake. If an expertise reversal effect is observed, then one might conclude with greater confidence that a multimodality effect exists in the mechanism linking embodied learning (arm or arm-body training) and letter knowledge and script skills acquisition (see previous section). If a worked example effect or sleeper effect is observed instead, it may be that the experimental conditions
(arm training or arm-body training) do not implement a multimodality effect but, instead, add an additional, non-informative modality at the expense of the worked example.

**Research Aims and Hypothesis**

The present research sought to assess the educational impact of integrating increasing degrees of bodily movements into the design of kindergarten classroom instruction, by comparing the respective effects on pre-reading acquisitions (i.e., letter knowledge and script) of instructions with low (ordinary pencil-and-paper training), intermediate (experimental arm training), and high degrees of bodily engagement (experimental arm-body training). Moreover, to account for potential learning gaps, the experimental effects of these types of embodied learning strategies were compared as a function of students’ initial levels on the trained skills.

Two research questions guided the analyses:

Research question 1 (RQ1): To what extent does the integration of bodily movements in kindergarten instructional designs enhance acquisitions in letter knowledge and script?

Research question 2 (RQ2): Do the gains of integrating high levels of embodied learning depend on students’ initial skills (pre-experimental) in letter knowledge and script?

Concerning RQ1, it was expected from previous embodied learning research (Bara et al., 2013; Bara & Bonneton-Botté, 2018; Damsgaard et al., 2023, 2023) that higher levels of bodily engagement would be associated with increased acquisitions in letter knowledge and script (pencil-and-paper training < arm training < arm-body training; Hypothesis 1). Alternatively, based on complementary evidence (Botha & Africa, 2020; Damsgaard et al., 2020), it was expected that arm movement involved motor skills more closely related to handwriting gestures (e.g., arm movement) than body movement, so that only the former bodily involvement might be more beneficial to learning (i.e., pencil-and-paper training = arm-body training < arm training;
Hypothesis 2). Concerning RQ2, according to the worked example and sleeper effects (Hypothesis 3) and the expertise reversal effect (Hypothesis 4), it was expected that receiving experimental arm or arm-body training (vs. ordinary pencil-and-paper training) would be unfavorable or favorable, respectively, to low-skilled pre-readers, relative to high-skilled pre-readers.

## Method

### Participants and Procedure

Participants were recruited among 13 kindergarten classes taught by trainee teachers working part time in their classrooms and following part-time Master courses at their university institute for teacher training. The participation of classes in the study was voluntary. Once registered in the study, each class was randomly assigned to one of the three training programs and one of the two groups of letters. Recruited classes included 184 students. After exclusion of special needs students (n = 3), non-French speaking students (n = 4), and students with missing data on both pre-tests and post-tests (n = 17), the final sample comprised 160 kindergarten students (M = 5.18 years, SD = 0.29, 54% girls). This study was preregistered on the OSF repository: https://osf.io/4rzam/

### Measures

#### Independent Variables: Training Conditions

We manipulated three experimental conditions: a pencil-and-paper training (condition 1 = 60 students), an arm training (condition 2 = 62 students), and an arm-body training (condition 3 = 38 students). The training conditions consisted in two kinds of training sessions which took place during the same week, one specific to each group, and one common to all groups. Twelve training sessions were delivered, that is, one pair of sessions a week (one
specific and one common) for 6 consecutive weeks. Each pair of sessions trained on a single letter, thus obtaining 6 letters over the training period. To increase the external validity of results, each group was randomly assigned to a different sets of letters (i.e., set 1 = [a, r, n, t, v, b]; set 2 = [i, l, m, d, f, p]). The letters in each set were chosen based on their frequency in the French language and on their various characteristics (vowel, stop consonant, nasal consonant, voiceless consonant, and voiced consonant; Bara et al., 2018). The letters across sets were paired based on their visual (e.g., m and n, l and b, a and d), and phonological similarities in French (e.g., p and b, r and l, f and v; Bara et al., 2013). In addition, to control for ordering effects, the order of presentation of letters in each group was randomized across the 6-week training period.

**Specific Training.** Specific training sessions were designed to last 20 to 25 minutes and consisted in 3 exercises in each group, including one exercise which was common to arm and arm-body training conditions (i.e., exercise 3; for details, see Appendix). Each exercise was first illustrated and described verbally by the teacher in front of the class, before students were prompted to perform the exercises on their own. The specific training differed across conditions in the following manner. For the pencil-and-paper training condition, ordinary handwriting exercises were conducted, consisting in reviewing the letter learned the previous week by copying a model over a slate (exercise 0a, 5 minutes, starting on week 2); re-writing over the model of the (new) training letter written in big format, with the aid of an erasable marker and a superposed plastic sheet (exercise 1, 5 minutes); and, similarly, rewriting over models of the letter written several times in medium and small format along dotted lines and plain lines, before finally writing on the training letter on the last line of the plastic sheet without a model (exercise 2, 15 minutes). These exercises were meant to train skills specific to the writing activity (see Table 1), including well-calibrated inhibitory skills for letter perception and recognition
(Cameron et al., 2012; Rey et al., 2009), fine motor skills for pencil holding and manipulation (Cornhill & Case-Smith, 1996; Seo, 2018), haptic-kinesthetic skills to mentally simulate the writing of the letter with the pencil (Bara & Gentaz, 2011), and visual-motor integration of eye-movement saccades (e.g., vestibular-ocular reflex) and fine motor skills (Daly et al., 2003; Sales & Colafêmina, 2014). These skills were conceived as specific in that their memory traces were driven by the constraints of the activity itself (e.g., training letter size and type, kind of pencil or pen used to write, writing support).

For the arm training condition, the specific training consisted in reviewing the letter learned the previous week by tracing its shape in the air with one's arm (exercise 0b, 5 minutes, starting on week 2); tracing the (new) training letter with one's arm, once from the model of the teacher and three times alone (exercise 3, 10 minutes); and tracing the training letter with one's arm, first in the air one student at a time (twice), then in the air with blindfolds while guided by a peer, and finally alone but with the tracing performed over the desk (exercise 4, 10 minutes).

These exercises were expected to generate memory traces of an intermediate level (see Table 1), by involving skills that are partly related to the writing activity (especially since few activities involve reproducing a shape in the air with one's hand), such as proprioception of the hand (haptic kinesthetics; Bara & Gentaz, 2011) and hand movement control (manipulative skills; Logan et al., 2018); but also skills not generally involved in this activity, including upper-body movement of the arm (bodily kinesthetics) and manual coordination to reproduce the trace of the letter in the air (Naito et al., 2016), which are skills used in everyday life situations for seizing objects and communicating with others. In combination with ordinary pencil-and-paper training (i.e., handwriting exercises), this condition was expected to generate specific-intermediate sensorimotor memory traces.
For the arm-body training condition, it consisted in reviewing the letter learned the previous week by walking over its trace on the ground (exercise 0c, 5 minutes, starting on week 2); tracing of the (new) training letter with one's arm, once from the teacher model and thrice alone (exercise 3, 10 minutes; same as in arm training); and walking twice over the trace of the training letter on the ground, once with blindfolds while guided by a peer and twice alone without blindfolds (exercise 5, 10 minutes). Here, memory traces involved in the body training were expected to have a more general nature (see Table 1), being related to body coordination and fundamental locomotor skills used to walk over the trace of the training letter (e.g., balance, placing one feet after another along the line, being guided by a peer; Logan et al., 2018) and corporeal awareness to visualize walking over the letter (bodily kinesthetic; Naito et al., 2016), all of which are influenced by everyday life situations involving locomotion and not specific to writing activities.

**Common Training.** All groups received the common training session, which took place on days following the specific training session (during the same week) and consisted in 20-minute handwriting exercises. This session consisted in writing the training letter (same as in the specific session) on an individual slate, after observing the teacher's verbalized example of writing on the board (exercise 6, 5 minutes); writing the letter on paper several times along a line, with the letter model at the beginning of the line (exercise 7, 5 minutes); identifying and circling the letter among 24 possible letters (5 occurrences of the training letter, 19 other letters; exercise 8, 5 minutes); and writing the letter on paper from memory (without model; exercise 9, 5 minutes).

By delivering these pairs of sessions for each letter, all groups received a kind of pencil-and-paper training (common training), while also receiving a specific training that reinforced
pencil-and-paper training (pencil-and-paper condition) or, alternatively, the visuomotor training using the arms and whole body (arm-body condition) or arms only (arm condition). This design was chosen to ensure that experimental conditions were nested in each other and combined specific (pencil-and-paper training), specific and intermediate (arm training), and specific, intermediate and general skills training (arm-body training; see Table 1).

**Dependent Variables: Letter Knowledge and Script Skills**

To assess acquisitions in letter knowledge and script of the 6 letters for which the pupils were trained, 5 raw measures and one composite measure were collected prior to (pre-tests, T1) and after (post-tests, T2) the 6 pairs of training sessions.

**Letter recognition.** The experimenter said the name of a letter, and the child had to circle it among 10 other letters. The nine other letters were visually similar to the target letter. Each correct answer was given a score of 1. Only the 6 letters used in the training sessions were tested in this task. These letters were presented in two random orders (to avoid students copying each other), with each of the 6 letters presented twice (12-point scale).

**Letter name knowledge.** Each of the 6 letters was written on an individual card and presented in the same random order to each child. The child was asked to give the name of each letter. The number of correct answers on letter name was counted (6-point scale).

**Letters written under dictation.** Each child had to write the 6 letters on a white sheet of paper when the experimenter said each letter name out loud. The number of correct answers was counted (6-point scale).

**Letter script stroke direction.** Each child was asked to copy the 6 letters one by one with a printed model of the letter on the sheet. For each of the six letters, the stroke direction was
rated. One point was awarded when the stroke direction corresponded to the conventional stroke in cursive writing (6-point scale).

**Letter script overall legibility.** Using the same copied letters as for the stroke direction test, an overall assessment of letter quality was performed and each letter was rated between 0 and 3 (0 being an unrecognizable letter, and 3 being a letter considered to be perfectly handwritten for a child of that age; 18-point scale). To enhance standardized ratings, a common rating scheme was used based on examples for each category of rating on each of the 12 letters.

**Composite measure.** In addition to raw measures, a composite measure of letter knowledge and script was constructed to assess the overall effects of the experiment on these acquisitions. Thus, a measurement model was built in which a second-order latent factor of general skills loaded on latent factors of letter knowledge and letter script, each defined by its respective indicators. To ensure identification as well as commensurability across pre-tests and post-tests, invariance constraints were imposed on the factor loadings of letter knowledge and letter script factors across T1 and T2. The model for this solution showed satisfactory fit to the data ($\chi^2_{31} = 57.0, p = .003$, CFI = 0.961, TLI = 0.944, RSMEA = 0.072, SRMR = 0.070), strong and balanced factor loadings ($\beta = [.535; .538]$) and a reliable measure of students' overall letter knowledge and script skills on both pre-tests and post-tests (i.e., $\omega_{composite} = [.767, .810]$); see Supplemental Material—SM). Based on this model, factor scores standardized to zero means and unit variances were extracted and used as indicator of global acquisitions in letter knowledge and script on pre- and post-tests.
Control Variables

The analyses controlled for the potential confounding effects of background and
experimental variables, including age, gender, set of letters (set 1 vs. set 2), and baseline levels at
pre-test for the outcome under consideration.

Analytic Strategy

**Modeling of experimental effects.** Although embodied learning studies usually rely on analyses of variance (ANOVAs), multiple regression models were preferred instead of ANOVAs because they enable adjusting for the effects of all confounding factors (both categorical and continuous variables) and are also recommended in the case of categorical outcomes (Davis, 2010; Jaeger, 2008). Ordinal regression models (Agresti, 2002; Christensen, 2015) and linear regression models (Cohen et al., 2003; R Core Team, 2020) were therefore used to predict variations, respectively, in the categorical outcomes associated with raw measures of tests (i.e., letter recognition, letter-name knowledge, letters written under dictation, letter script stroke direction, overall letter legibility), and in the continuous outcome of their composite measure.

For each outcome, a series of models were performed to select significant confounding variables (including baseline levels in the outcome, i.e., pre-tests), before estimating the main effects of experimental conditions (RQ1) and their interaction effects with baseline levels (RQ2). Effects were considered valid for selection (control variables) or for making inferences (treatment and interaction effects) if their inclusion contributed significantly (at *p* < .05) to decrease the deviance of the model fit relative to a model without the effect, using a likelihood ratio test (LRT) following the chi2 distribution, an approach especially recommended for testing main and interaction effects in nonlinear models (Agresti, 2002; Greene, 2010). For ordinal regressions, a logit link function was used, so that effects could be interpreted as the unit-change in the
propensity (raw coefficients b, or logits) or in the odds (exponentiated coefficients, or odds ratio—OR) of scoring high on the outcomes (Agresti, 2002). Cohen's *d* effect sizes were then computed based on the obtained odds ratios (Borenstein et al., 2021; Lenhard & Lenhard, 2022). Interaction effects were interpreted in terms of outcome probabilities (equation 7.2, Agresti, 2002, p. 271), while assuming average or reference values on continuous and categorical covariates. For ease of interpretation, pre-test scores were standardized to zero means and unit variance prior to analysis.

More specifically, experimental effects were modeled via zero-sum contrasts, a technique that tests the main effects of training conditions against a (reference) grand mean, the latter representing the average gains expected for each students (net of experimental and covariate effects; Brehm & Alday, 2022; Davis, 2010; Overall & Spiegel, 1969). To do so, two contrasts were entered in the model (i.e., c1 = [1,0,-1], c2 = [0,1,-1]) that corresponded to the main effects of the first two groups; the third main effect could be computed as minus the sum of the first two main effects (Overall & Spiegel, 1969) or, as we did, by estimating a new model after substituting the second group by the third. Results therefore report 3 main effects (one for each group; see Table 3), but the underlying models were based on two contrasts. This technique was extended to interaction effects by multiplying contrasts by baseline levels (see Table 4; Overall & Spiegel, 1969). Zero-sum contrasts were privileged over other forms of contrasts that require testing effects relative to a reference group (e.g., Helmert or polynomial contrasts), because the latter would have implied using different contrasts to test alternative hypotheses (e.g., polynomial contrast for H1 vs. Helmert contrast for H2), can result in Type II errors (i.e., incorrectly rejecting a true effect) if inappropriate reference groups are chosen, and provide simple effects instead of the main effects of interest (Brehm & Alday, 2022). That being said, for
some outcomes (i.e., letter-name knowledge, letters written under dictation), some training conditions did not differ in their main effects and were combined for parsimony, using them as the reference group in the contrast.

**Missing data treatment.** Small amounts of missing data (i.e., for 2 pre-test and 2 post-test measures, from 1% to 6%) were accounted for using 10 multiply-imputed datasets, which were generated using all study variables using the R package *mice* (van Buuren & Groothuis-Oudshoorn, 2011). By using this strategy, the fraction of missing information was low for all model coefficients (< 2.5%), which indicated that results were virtually identical across imputation datasets and, thus, little sensitive to missing data (van Buuren, 2012). All models were estimated on R after adapting available functions for ordinal regression (Christensen, 2015) and linear regression (R Core Team, 2020) to multiple imputation regression, using Rubin's rules for combining parameter estimates (van Buuren, 2012).

## Results

### Descriptive Statistics

Summary statistics showed that the study sample was well balanced in terms of gender and age across conditions (SM), and that students in all groups tended to score higher on the outcomes after the experiment was conducted (see Table 2). Despite the random assignment procedure used, some pre-test differences between groups were apparent. For example, on average, students in the pencil-and-paper training group scored lower on three out of five outcomes on pre-tests (i.e., letter recognition, letter-name knowing, and letter script overall legibility), as compared to the other groups (Table 2). Moreover, students in the arm-body training group scored higher than those in the arm training group on pre-tests for letters written under dictation (Table 2). These observations indicated that controls for baseline levels in pre-
reading skills were warranted in the modeling strategy to consolidate inferences based on experimental effects.

<table><caption>Table 2. Means and Standard Deviations of Pre-test (T1) and Post-test (T2)
Measures in the Full Sample and Across Experimental Groups</caption><thead><tr><th rowspan="2"></th><th colspan="2">Letter recognition</th><th colspan="2">Letter-name knowledge</th><th colspan="2">Letters written under dictation</th><th colspan="2">Stroke direction</th><th colspan="2">Overall legibility</th></tr><tr><th>T1</th><th>T2</th><th>T1</th><th>T2</th><th>T1</th><th>T2</th><th>T1</th><th>T2</th><th>T1</th><th>T2</th></tr></thead><tbody><tr><td>Full sample</td><td>3.01<br>(2.50)</td><td>6.68<br>(3.03)</td><td>1.74<br>(1.44)</td><td>3.37<br>(1.58)</td><td>3.31<br>(1.52)</td><td>3.83<br>(1.33)</td><td>2.49<br>(1.3)</td><td>3.99<br>(1.03)</td><td>5.44<br>(2.89)</td><td>9.14<br>(3.23)</td></tr><tr><td>Pencil-and-paper training<br>(n = 60)</td><td>2.52<sup>A</sup><br>(2.21)</td><td>6.10<sup>A</sup><br>(2.78)</td><td>1.48<sup>A</sup><br>(1.27)</td><td>2.98<br>(1.63)</td><td>3.40<sup>A</sup><br>(1.45)</td><td>3.92<br>(1.37)</td><td>2.33<br>(1.37)</td><td>3.85<br>(1.22)</td><td>5.22<sup>A</sup><br>(2.54)</td><td>8.02<sup>A</sup><br>(2.94)</td></tr><tr><td>Arm training<br>(n = 62)</td><td>2.97<br>(2.57)</td><td>7.03<br>(3.22)</td><td>1.68<br>(1.50)</td><td>3.60<br>(1.48)</td><td>3.06<br>(1.60)</td><td>3.87<br>(1.29)</td><td>2.58<br>(1.25)</td><td>4.00<br>(1.02)</td><td>4.63<sup>B</sup><br>(2.48)</td><td>9.13<sup>B</sup><br>(3.08)</td></tr><tr><td>Arm-body training<br>(n = 38)</td><td>3.87<sup>A</sup><br>(2.66)</td><td>7.00<sup>A</sup><br>(3.02)</td><td>2.24<sup>A</sup><br>(1.51)</td><td>3.61<br>(1.60)</td><td>3.55<sup>A</sup><br>(1.46)</td><td>3.63<br>(1.34)</td><td>2.58<br>(1.29)</td><td>4.21<br>(0.62)</td><td>7.11<sup>A,B</sup><br>(3.38)</td><td>10.90<sup>A,B</sup><br>(3.18)</td></tr></tbody></table>

Note. N = 160 kindergarten students. Subscript letters indicate pairwise differences between groups significant at 5% within each wave (T1 or T2).
**Experimental Effects on Specific Acquisitions in Letter Knowledge and Script**

After controlling for baseline levels on pre-tests and influential background variables (age, gender, letter set), results from ordinal regression modeling showed significant experimental effects on learning for all outcomes on post-tests, except letter script stroke direction (see Table 3).

**Letter recognition.** The main effect on letter recognition of arm training was positive and significant (b = 0.432, OR = 1.540, *d* = .238, *p* = .041), indicating that this group experienced increased acquisitions in letter recognition compared to what was expected from the average student. On the contrary, the effects on letter recognition of pencil-and-paper training (b = -0.212, OR = 0.809, *d* = -.117, *p* = .289) and arm-body training (b = -0.219, OR = 0.803, *d* = -.121, *p* = .353) were not significant at 5% and were very close in size. Combining the two latter groups resulted in a more parsimonious and significant model [chi2(1) = 4.4, *p* = .037], according to which the arm training group was almost twice more likely to score high on letter recognition on post-tests than the other two groups (b = 0.646, OR = 1.908, *d* = .356, *p* = .038).

**Letter-name knowledge.** Similarly, effects on letter-name knowledge were positive and significant for the arm training group (b = 0.614, OR = 1.629, *d* = .269, *p* = .030), non-significant for the arm-body training group (b = -0.090, OR = 0.914, *d* = -.045, *p* = .733), and borderline-significantly (*p* < .07) negative for the pencil-and-paper group (b = -0.398, OR = 1.489, *d* = .219, *p* = .069). Accordingly, students in the arm training group were 63% more likely (vs. other groups) to score high on letter-name knowledge on post-tests, and students in the pencil-and-paper group 49% less likely to do so. The likelihood ratio test for this model was significant at 5% [chi2(2) = 6.5, *p* = .040].
**Letters written under dictation.** Only the effect of arm-body training was significant (b = -0.462, OR = 0.629, *d* = -.256, *p* = .043), indicating that students in this group were 59% less likely to score high on the number of correctly written letters under dictation on post-tests, in comparison to the average student. The effects on letters written of pencil-and-paper training (b = 0.217, OR = 1.142, *d* = .073, *p* = .301) and arm training (b = 0.246, OR = 1.279, *d* = .156, *p* = .243) were not significant and were close in size, suggesting that these groups did not systematically differ in this skill acquisition. Combining these two groups together resulted in a more parsimonious and significant model [chi2(1) = 4.3, *p* = .042], in which the arm-body group appeared two times less likely to score high on post-tests as the other groups (b = -0.694, OR = 0.500, *d* = -.382, *p* = .043).

**Stroke direction.** None of the experimental groups showed significant effects on skill acquisitions in letter stroke direction, and effects were very similar for pencil-and-paper training (b = -0.035, OR = 0.966, *d* = -.019, *p* = .872), arm training (b = -0.060, OR = 0.942, *d* = -.033, *p* = .774), and arm-body training (b = 0.094, OR = 1.099, *d* = .052, *p* = .679). Accordingly, the likelihood ratio test for this model was not significant at 5% [chi2(2) = 0.207, *p* = .902].

**Overall legibility.** The main effect on acquisitions in overall legibility was positive and significant at 5% for the arm-body training group (b = 0.560, OR = 1.751, *d* = .309, *p* = .020), negative and significant at 5% for the pencil-and-paper training group (b = -0.749, OR = 0.473, *d* = -.413, *p* < .001), and positive but not significant for the arm training group (b = 0.189, OR = 1.208, *d* = .104, *p* = .358). Thus, according to odds ratios, students receiving arm-body training (vs. average students) were 75% more likely to score higher on this skill on post-tests, whereas students receiving pencil-and-paper training (vs. other training) were 211% less likely to do so. The likelihood ratio test for this model was significant at 5% [chi2(2) = 14.2, *p* < .001].
Experimental Effects on Overall Acquisitions in Letter Knowledge and Script

**Overall acquisitions (composite measure).** As reported in the last column of Table 3, results from linear regression modeling showed that the arm training condition had a significant, positive effect on overall acquisitions in letter knowledge and script on post-tests ($\beta$ = 0.148, *d* = 0.404, *p* = .008). On the contrary, the arm-body training condition had a significant, negative effect on overall acquisitions ($\beta$ = -0.138, *d* = -0.383, *p* = .033), whereas the pencil-and-paper training had no apparent effect ($\beta$ = -0.011, *d* = -.022, *p* = .849). In sum, considering all skills acquisitions together, receiving arm training contributed to increasing acquisitions by a 1/2 standard deviation (i.e., +0.404 SD; vs. average students), whereas receiving arm-body training contributed to decreased acquisitions by a 1/3 standard deviation (i.e., -0.383 SD). The likelihood ratio test for this model was significant at 5% [chi2(2) = 8.2, *p* = .016].
<table><caption>Table 3. Models of Experimental Effects on Letter Knowledge and Script</caption><caption>Acquisitions</caption><thead><tr><th rowspan="2"></th><th colspan="3">Letter knowledge</th><th colspan="2">Letter script</th><th rowspan="2">Composite measure of letter knowledge and script skills</th></tr><tr><th>Letter recognition</th><th>Letter-name knowledge</th><th>Letters written under dictation</th><th>Stroke direction</th><th>Overall legibility</th></tr></thead><tbody><tr><td>Boy (vs. girl)</td><td></td><td></td><td></td><td></td><td>0.544*</td><td></td></tr><tr><td>Letter set 1 (vs. set 2)</td><td>2.035*</td><td>2.143*</td><td></td><td>2.599**</td><td>2.385**</td><td>0.247**</td></tr><tr><td>Initial level (pretests)</td><td>3.951***</td><td>4.406***</td><td>2.309***</td><td>2.854***</td><td>2.382***</td><td>0.857***</td></tr><tr><td>Pencil-and-paper training</td><td>0.809</td><td>0.672†</td><td>1.242</td><td>0.966</td><td>0.473***</td><td>-0.011</td></tr><tr><td>Arm training</td><td>1.540*<br>(A1.908*)</td><td>1.629*</td><td>1.279</td><td>0.942</td><td>1.208</td><td>0.148**</td></tr><tr><td>Arm-body training</td><td>0.803</td><td>0.914</td><td>0.629*<br>(B0.500*)</td><td>1.099</td><td>1.751*</td><td>-0.138*</td></tr><tr><td>Chi2 of LRT</td><td>4.4 (A4.4*)</td><td>6.5*</td><td>4.1 (B4.1*)</td><td>0.2</td><td>14.2***</td><td>8.3*</td></tr></tbody></table>

Note. N = 160 kindergarten students. LRT = likelihood ratio test between models with and without experimental effects. For raw measures, effects are reported on the odds ratio scale. For the composite measure, effects are reported on the metric scale of the outcome variable (i.e., factor scores standardized to zero mean and unit variance, M = 0, SD = 1). Background or experimental variables (age, sex, letter set) with non-significant effects were removed for parsimony.

†p < .10; *p < .05; **p < .01; ***p < .001

AEffect of arm training (and LRT) when pencil-and-paper and arm-body training groups are combined

BEffect of arm-body training (and LRT) when pencil-and-paper and arm training groups are combined
Interaction Effects Between Experimental Effects and Initial Skills Level

Complementary models revealed the presence of three interaction effects between
training conditions and students' initial skill levels (see Table 4; other interaction effects were
not significant at 5% and did not contribute to significant increments in model fit.) The first
concerned the positive interaction effect (b = 0.754, OR = 2.215, d = .438, p = .039,
chi2(1) = 4.1, p = .042) between the deleterious effect of arm-body training on acquisitions in
the number of letters written under dictation, and the positive effect of pre-tests levels of this
skill. According to the interpretation of this interaction effect, the relative difference in the
propensities to score high on this skill between the arm-body training condition and the other two
groups (i.e., pencil-and-paper and arm training) was negative and positive for students who
scored ¼ SD below and above the mean on the pre-tests, respectively (see red curve, Figure 2A).
In other words, the effect of arm-body training on acquisitions in letters written under dictation
(as identified before, Table 3) was detrimental for low-skilled students only, and was beneficial
for high-skilled students.

The second and third interaction effects concerned the effects of pencil-and-paper and
arm-body training on acquisitions in overall legibility of letter script, as a function of initial skill
levels (chi2(2) = 6.9, p = .031; Table 4). More precisely, students exposed to pencil-and-paper
training (b = -0.509, OR = 0.601, d = -.280, p = .022) and arm-body training (b = 0.481,
OR = 1.618, d = .265, p = .023) experienced decreased acquisitions on this skill compared to the
other groups, but only when their initial skills level were lower-than-average. Graphical
projections showed that the difference in probabilities of scoring high between arm training and
pencil-and-paper training was positive (i.e., favorable to arm training) and peaked for students
with pretest levels situated at approximately 1 SD below the mean (see green curve, Figure 2B).
On the contrary, the difference between arm-body training and pencil-and-paper training reached a negative peak for students with pretest scores situated at 1 ½ SD below the mean, meaning that arm-body training was unfavorable for very low-skilled students. However, this effect reversed, and arm-body training became mildly beneficial (vs. pencil-and-paper training) for students with mild low pre-tests scores (at around -0.5 SD; see red curve Figure 2B). Overall, relative to pencil-and-paper training, experiencing arm training appeared to facilitate acquisitions in overall legibility for low-skilled students (vs. high-skilled), whereas experiencing arm-body training appeared to undermine them.
<caption>Table 4. Models of Interaction Effects Between Type of Training and Initial Student</caption>

Level

<table><thead><tr><th rowspan="2"></th><th colspan="3">Letter knowledge</th><th colspan="2">Letter script</th><th rowspan="2">Composite measure of letter knowledge and script skills</th></tr><tr><th>Letter recognition</th><th>Letter-name knowledge</th><th>Letters written under dictation</th><th>Stroke direction</th><th>Overall legibility</th></tr></thead><tbody><tr><td>Boy (vs. girl)</td><td></td><td></td><td></td><td></td><td>0.540*</td><td></td></tr><tr><td>Letter set 1 (vs. set 2)</td><td>2.108*</td><td>2.250*</td><td></td><td>2.628**</td><td>2.664**</td><td>0.248**</td></tr><tr><td>Initial levels (pretest)</td><td>3.369***</td><td>4.808***</td><td>1.952***</td><td>2.790***</td><td>2.365***</td><td>0.856***</td></tr><tr><td>1. Pencil-and-paper training</td><td></td><td>0.615*</td><td></td><td>0.949</td><td>0.501**</td><td>-0.012</td></tr><tr><td>2. Arm training</td><td></td><td>1.862*</td><td></td><td>0.946</td><td>1.347</td><td>0.149**</td></tr><tr><td>3. Arm-body training</td><td></td><td>0.873</td><td></td><td>1.114</td><td>1.482</td><td>-0.137*</td></tr><tr><td>4. Arm training (vs. 1+3)</td><td>2.146*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5. Arm-body training (vs. 1+2)</td><td></td><td></td><td>0.478*</td><td></td><td></td><td></td></tr><tr><td>Initial levels X 1</td><td></td><td>0.613†</td><td></td><td>0.960</td><td>0.601*</td><td>-0.009</td></tr><tr><td>Initial levels X 2</td><td></td><td>1.361</td><td></td><td>1.513†</td><td>1.028</td><td>0.009</td></tr><tr><td>Initial levels X 3</td><td></td><td>1.198</td><td></td><td>0.688</td><td>1.618*</td><td>0.000</td></tr><tr><td>Initial levels X 4</td><td>1.675</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Initial levels X 5</td><td></td><td></td><td>2.125*</td><td></td><td></td><td></td></tr><tr><td>Chi2 of LRT</td><td>2.1</td><td>4.0</td><td>4.3*</td><td>3.8</td><td>6.9*</td><td>0.1</td></tr></tbody></table>

Note. “X” denotes a product term. LRT = likelihood ratio test between models with and without the interaction effect. Other parameters are defined as in Table 3.

†p < .10; *p < .05; **p < .01; ***p < .001
<figure><img src="image_3.png" /><figcaption>Figure 2. Interpretation of Interaction Effects On Acquisitions Between Training Condition and Initial Skills Level</figcaption></figure>

Note. Pre-test scores are standardized to zero mean and unit variance (M = 0, SD = 1). “Post-test acquisitions” correspond to the probability of scoring high on the corresponding post-test, controlling for covariates and pre-test levels (model parameters are reported in Table 4). Green and red curves represent differences in probabilities between groups that are favorable and unfavorable to low-skilled students, respectively.
Discussion

This study assessed to what extent specific forms of embodied learning reinforce
acquisitions in letter knowledge and script, notably by integrating into kindergarten instructional
design alternative bodily movements to reproduce the shape of letters. More precisely, it
investigated the relative impact of training skills combining handwriting movements (pencil-and-
paper training), handwriting and arm movements (arm training), and handwriting, arm, and body
movements (arm-body training), for all students (RQ1) and across initial skills levels (RQ2).

Results from a preregistered experiment suggested that training supporting multiple layers of
sensorimotor processes may enhance the acquisition of pre-reading skills, but that such
innovative instructional designs may reinforce or, on the contrary, compensate initial differences
in student skills depending on the complexity of the design. These findings shed new light on the
relative effectiveness of integrating sensorimotor skill training into kindergarten learning
processes, while also corroborating—albeit indirectly—theories of embodied cognition and
cognitive load in the design of effective instructional designs.

Instructional Designs Integrating Bodily Movements May Enhance Acquisitions in Pre-
reading Skills

A major finding of this study was the observation that students who received training involving handwriting and arm movements (i.e., arm training reproducing the shapes of letters through hand movements, plus pencil-and-paper training) experienced increased acquisitions on two out of three measures of letter knowledge skills (i.e., letter recognition, letter-name knowledge), and across all skills (i.e., composite measure), than what was expected from the average student. Similarly, the training group focusing on handwriting, arm, and body movements (i.e., body training reproducing the shape of the letter through locomotive tracing of
the letter on the ground, plus arm and pencil-and-paper training) appeared to outperform other
groups in terms of letter script legibility, although it was outperformed by them in terms of letter
knowledge (e.g., number of letters correctly written under dictation) and across all skills
acquisitions considered as a whole.

Taken together, these results suggest that most letter knowledge and script learning
processes are consolidated by the integration of intermediate (i.e., arm training) rather than high
levels of bodily involvement (i.e., body training; validation of H2) and, for some specific
acquisitions (e.g., letter script legibility), by high levels of bodily involvement (validation of H1).
They indicate that bodily motor skills not directly related to handwriting—such as arm gesture
and walk simulations of tracing the letter—can enhance the memorization of letters and improve
their actual handwriting, better so than when they are ignored in the traditional handwriting
exercises of pencil-and-paper training. At the same time, the pilot study (see SM-A) found that
these effects were non-existent or were detrimental (instead of beneficial) in the absence of
training in fine-motor skills associated with handwriting exercises (as those provided in all
conditions of the present study), suggesting that non-null amounts of fine-motor skills training
are warranted to observe the beneficial effects of embodied learning in pre-reading skills.

According to principles of embodied learning (Skulmowski & Rey, 2018), these findings
give support to the multisensorial nature of encoding processes in humans, and, notably, the
learning benefits of integrating fine and gross motor skills (e.g., hand, arm, and whole-body
movement) with the more commonly stimulated visual (e.g., monitoring the matching between
one's written letter and a token letter used as a model) and cognitive systems (e.g., remembering
the normative steps in handwriting). In terms of effect size, the enhancement obtained is non-
negligible, being related to 60% to 200% chances of experiencing increased gains or a fifth to
half a standard deviation gain (for specific measures of letter knowledge and script), and to a half
of standard deviation gain (for the composite score), relative to the traditional pencil-and-paper
situation.

From a cognitive load perspective (Sweller et al., 2019), these results may also reflect the
learning gains of experiencing multiple mental representations of the same learning content to
enhance the intrinsic load of the learning task (variability effect; Paas & Van Merriënboer, 1994),
but also of simulating motor movements that are essential to the task of letter script, as opposed
to interpreting them using static cues or oral instructions (human movement effect; Höffler &
Leutner, 2007; van Gog et al., 2009). Stated otherwise, human movements (e.g., letter script) are
best acquired by actual dynamic simulation of the movements (arm movement of tracing) based
on unconscious, biologically primary knowledge, than by pictural representations of these
movements (directional arrows of tracing) based on effortful, biologically secondary knowledge
(Geary, 2008; Paas & Sweller, 2012). At the same time, the fact that arm movements were
generally more beneficial than whole-body movements suggests that such dynamic simulations
need to reflect the kind of motor skills involved in handwriting acquisitions (e.g., fine motor
precision and integration, manual dexterity, upper limb coordination; Botha & Africa, 2020), lest
the motor movements become irrelevant to the learning content (Halvorson et al., 2019; Huff et
al., 2018). More precisely, it is possible that training whole-body movements resulted in a carry-
over effect, by activating the motor cortex associated with such movements even when not
actually performing them during letter script exercises. In turn, the unnecessary activation of the
motor cortex may have contributed to create extraneous cognitive load and undermined letter
script performance. As we discuss hereafter, this carry-over effect of whole-body movements
appeared particularly deleterious for low-skilled students (vs. high-skilled students), who are known to be more vulnerable to cognitive overload (Tricot et al., 2020).

These findings contribute to the consolidation and expansion of knowledge about the role of motor skills in the acquisition of pre-reading skills. Previous experiments (Bara et al., 2013; Bara & Bonneton-Botté, 2018; Damsgaard et al., 2020, 2022, 2023) had shown the benefits of integrating fine- and gross-motor skills (hand, arm, and body movement) in the learning processes associated with letter-name knowledge, letter-sound matching, letter recognition, stroke direction, and legibility of letter script, while also showing its potential drawbacks for handwriting fluency. The present study partly replicated their findings by showing positive effects on letter recognition, letter-name knowledge, and script legibility, but did not find significant beneficial effect on stroke direction (handwriting fluency was not assessed in this study). One major difference in terms of design is that the present study compared the learning benefits of two motor training interventions (arm training, arm-body training) and one ordinary pencil-and-paper training intervention across multiple measures of letter knowledge and script, whereas previous studies compared motor training with a non-ordinary visual training group (Bara et al., 2013; Bara & Bonneton-Botté, 2018; Damsgaard et al., 2020), or ordinary training with motor training, but without informing on the relative difference between types of motor training or their impact on letter script (Damsgaard et al., 2022, 2023). In this sense, the present study offered a more comprehensive test by demonstrating that one kind of motor-skills training group (i.e., arm training) outperformed, for the most part, the kind of training typically used in kindergarten classrooms, both in terms of letter knowledge and letter script.
Augmented Sensorimotor Training May Compensate or Exacerbate Matthew Effects in
Kindergarten

A second major contribution of this study was our finding that, for some pre-reading
skills, the effects of integrating bodily movements to augment sensorimotor training were not
always distributed equally across learners. Aligning with an expertise reversal effect (Kalyuga et
al., 2012), the beneficial effect of the arm training appeared most beneficial for students with
initially low-level skills in overall legibility, thus compensating for initial differences between
students (validation of H4). On the contrary, the effects of arm-body training on both letter script
legibility (detrimental effect) and letter written under dictation (beneficial effect) appeared to be
unfavorable to students initially low on these skills, thus contributing to exacerbating initial
student differences (validation of H3). This might indicate that the arm-body condition is a more
advanced form of sensorimotor process that only benefits more skilled learners who already have
preliminary knowledge of the target content (sleeper effect; Brooks & Goldin-Meadow, 2016), at
the expense of less skilled learners who would otherwise benefit more from more explicit and
more guided handwriting exercises present in ordinary pencil-and-paper training (worked
example effect; Renkl, 2014) or more accessible sensorimotor traces associated with movements
closer to handwriting gestures (i.e., arm training).

Note that these ability-by-treatment interactions were observed for skills involving
production systems (e.g., kinesthetics for letter written under dictation and overall letter
legibility), but were not observed for other skills involving recognition systems (e.g., vision for
letter recognition, hearing for letter name knowledge). Again, this pattern of findings is coherent
with an embodied view on learning in which specific sensorimotor systems contribute to the
encoding of experiences, particularly those most involved in the learning process at stake
(Barsalou, 2008; Versace et al., 2014). On the contrary, an amodal view of learning processes— i.e., independent from any sensorimotor system—could hardly account for these findings.

In terms of implications, the above interaction effects should inspire future experimenters and practitioners to monitor further potential Matthew effects (increased learning gaps) or, on the contrary, compensatory effects (decreased learning gaps) associated with integrating bodily movements in the pre-reading skills learning process. Such differential effects are rarely considered in the evaluation of embodied learning instruction (e.g., one study found no evidence for differential effects of motor integration on letter knowledge acquisitions; Damsgaard et al., 2023) but may lead to unexpected results, hidden behind main experimental effects. For example, in the present study, focusing on the average, positive effect of arm-body training on letter script legibility might encourage teachers to use this kind of training in their classroom, in the hope of tackling the difficulties of struggling pre-readers. Yet, this approach would overlook the fact that this effect largely benefits more advanced students in the class, thus contradicting teachers’ intentions to tackle pre-reading difficulties. Similarly, future studies should investigate potential interactive effects with initial student skill levels to identify potential drawbacks associated with introducing innovative instructional design (e.g., Matthew effects). Based on the present study, the augmented motor training of the arm training group may be more appropriate for compensating for initial student differences in skills, although the evidence needs to be confirmed with replication studies.

**Limitations and Research Perspectives**

The present research built on the innovative instructional designs developed by Bara, Damsgaard, and their collaborators (Bara et al., 2013; Bara & Bonneton-Botté, 2018; Damsgaard et al., 2020) and proposed to integrate them within a hierarchical framework (see Table 1),
linking specific to general memory traces in embodied cognition processes (Barsalou, 2008; Versace et al., 2014). Further, links between the hierarchical framework and cognitive load theory (Sweller et al., 2019) and embodied learning (Skulmowski & Rey, 2018) were sketched to understand why integrating levels of memory traces might enhance pre-reading skills acquisitions, by assuming that memory traces from distinct sensorimotor processes actually involve independent memory processes that can be streamlined by the integration of bodily engagement, thus alleviating the cognitive load associated with letter knowledge and script acquisitions. Although the data were in line with these theories, it is important to recognize that the experiment did not focus on testing these theoretical processes specifically; rather, it tried to assess the relative effectiveness of alternative sensorimotor training exercises that involved, by construction, complementary memory traces in learning. This means that although the present findings can be viewed as corroborating the theories involved, more research is needed to test specific claims concerning intermediate processes (e.g., on the nature of skills involved, per Table 1) and to consolidate theoretical links. Can indeed memory traces from alternative levels of human experience be considered alternative modalities of learning? Are the training exercises proposed in this experiment likely to create synergies between memory traces, and how can we modify them to augment such synergies? For example, although we might explain the relative ineffectiveness of the arm-body training condition by the extraneous load associated with involving motor movements not closely related to letter script (i.e., walking over the trace of the trained letter), and alternative explanation could be formulated in terms of a working memory resource depletion (Chen et al., 2018). More precisely, it is possible that the efforts spent on the arm training depleted the working memory resources for the body training, thus minimizing the learning benefits of body movements. In this case, spacing out training components of the
present instructional design, for example by doing the arm training in the morning and the body
training in the afternoon, might reveal a more beneficial of the body training on letter knowledge
and script.

Another limitation of the study concerns its modest design. According to the lowest
experimental effect size found to be significant 5% (i.e., OR = 1.629 of arm training on letter
name knowledge), the probability of detecting this effect with 90% statistical power and two-
tailed z tests would have required a sample of 285 individuals (Faul et al., 2009). With 160
individuals, and given the effect found, the current study has a power of 78.9%, which might be
considered underpowered. Complementary research is therefore needed to consolidate both the
internal and external validity of present findings, using both larger samples and more diversified
settings (e.g., for pre-reading skills in more or less orthographic consistent languages, or in non-
alphabetic systems). An encouraging aspect of this study was its relative ease of implementation,
being applied over a short period of time (6 weeks) and with practitioners with little experience
in the classroom setting (i.e., trainee teachers), while respecting the time and personnel
constraints of traditional learning in kindergarten (e.g., one teacher, ordinary pencil-and-paper
training sessions in all conditions). This means that replication studies could be more easily
considered and with a greater chance of reproducing the experimental conditions implemented
here. In this endeavor, future studies are invited to assess the reliability of the grid used in this
study to rate overall letter legibility (see ESM 3.1), notably by having at least two teachers or
experimenters rate the same set of letter scripts and compute an interrater agreement index (e.g.,
Cohen's weighted kappa).
## Conclusion

The acquisition of pre-reading skills in kindergarten children coincides with the development of visual and motor skills. At first glance, these parallel developmental processes appear as competing with each other at the risk of overburdening the learning process. Yet, as previous research and the present study have shown, instructional architectures can be designed in a way that integrates these parallel processes into the pre-reading skills learning process, thus producing fruitful synergies in acquisitions. The design and functioning of such architectures merit further investigation, but their potential to facilitate acquisitions among low-skilled students could open promising avenues for creating effective learning environments in kindergarten settings. In this perspective, the visuomotor training developed in this study, notably combining handwriting and arm movements to reproduce the shapes of letters, might be considered in interventions aiming to support educational effectiveness and to reduce student disparities early in the learning process.
Appendix. Training Sessions In Each Condition

<table><thead><tr><th>Pencil-and-paper training</th><th>Arm training</th><th>Arm-body training</th></tr></thead><tbody><tr><td colspan="3"><em>Specific Session</em></td></tr><tr><td><strong>Exercise 0a (5 min.): starting week 2):</strong> students review the letter learned the previous week by writing it on a slate</td><td><strong>Exercise 0b (5 min.): starting week 2):</strong> students review the letter learned the previous week by tracing its shape in the air with their arms</td><td><strong>Exercise 0c (5 min.): starting week 2):</strong> students review the letter learned the previous week by walking over its outline written on the ground</td></tr><tr><td><strong>Exercise 1 (5 min.):</strong> using an erasable marker and a plastic sheet, students re-write over a model of the training letter written in large format</td><td><strong>Exercise 3 (5 min.):</strong> once from the model of the teacher and three times alone, students trace the training letter in the air with their arms</td><td><strong>Exercise 3 (5 min.):</strong> once from the model of the teacher and three times alone, students trace the training letter in the air with their arms</td></tr><tr><td><strong>Exercise 2 (15 min.):</strong> using an erasable marker and a plastic sheet, students re-write over models of the training letter written several times in medium and small format along dotted lines and plain lines, before writing the letter without a model</td><td><strong>Exercise 4 (15 min.):</strong> students trace the training letter with their arms, first in the air one pupil at a time (twice), then in the air with blindfolds while guided by a peer, and finally alone but performing the tracing over their desks</td><td><strong>Exercise 5 (15 min.):</strong> students walk twice over the outline of the training letter written on the ground (large format, e.g., W*L = 60cm*120cm), once with blindfolds while guided by a peer and twice very slowly alone without blindfolds</td></tr><tr><td colspan="3"><em>Common Session (On Days Following Specific Session, During the Same Week)</em></td></tr><tr><td><strong>Exercise 6 (5 min.):</strong> students write the training letter (same as the specific session) on an individual slate, after observing the teacher's verbalized example of writing on the board</td><td></td><td></td></tr><tr><td><strong>Exercise 7 (5 min.):</strong> students write the letter on paper several times along a line, with the letter model at the beginning of the line</td><td></td><td></td></tr><tr><td><strong>Exercise 8 (5 min.):</strong> students identify and circle the letter among 24 letters (5 occurrences of the training letter, 19 other letters)</td><td></td><td></td></tr><tr><td><strong>Exercise 9 (5 min.):</strong> students write the letter on paper from memory</td><td></td><td></td></tr></tbody></table>

All exercises are first illustrated and verbalized by the teacher. The instructions and the materials used for pre-tests and post-tests, and for the training sessions can be found at https://osf.io/c57er/.
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