**Preface to "Motor Control and Learning in Childhood and Adolescence: Interactions with Sports and Exercise"**

The production and control of human movement is a progressive and complex process that is developed across the lifespan. New motor patterns can be learned using movement and considering the interactions with the environment. Through affordances, the human is challenged and interacts with the environment to solve problems and to improve decisions. Therefore, motor control and motor learning are two core topics that should be considered in children and young populations. Motor control is dedicated to analyzing the mastering of voluntary movement, while motor learning is more related to the process of acquiring a skill or producing a new motor task. Many types of research are produced annually on these topics. However, within the pandemic context, changes in the stimuli provided to children are the reason for concern for their development. Less free play or structured practice (e.g., sports, physical education) may have consequences for the learning and mastering of human movement. Not exclusively focusing on this situation, but opening a window to its discussion, this Special Issue "Motor Control and Learning in Childhood and Adolescence: Interactions with Sports and Exercise"aims to be a space for publishing innovative articles or systematic reviews dedicated to motor control and learning in the field of sports and exercise.

Considering that more research should be done and published about such important topics, the aim of the Special Issue "Motor Control and Learning in Childhood and Adolescence: Interactions with Sports and Exercise"was to publish high-quality original investigations, as well as narrative and systematic reviews in the field of motor control and learning in sports and exercise.

> **Filipe Manuel Clemente, Ana Filipa Silva** *Editors*

## *Article* **Learning Basketball Tactical Actions from Video Modeling and Static Pictures: When Gender Matters**

**Ghazi Rekik 1,2 , Yosra Belkhir 3,4 , Nourhen Mezghanni <sup>5</sup> , Mohamed Jarraya 1,6, Yung-Sheng Chen 2,7,8,\* and Cheng-Deng Kuo 2,9,10,\***


**Abstract:** Recent studies within the physical education domain have shown the superiority of dynamic visualizations over their static counterparts in learning different motor skills. However, the gender difference in learning from these two visual presentations has not yet been elucidated. Thus, this study aimed to explore the gender difference in learning basketball tactical actions from video modeling and static pictures. Eighty secondary school students (Mage = 15.28, SD = 0.49) were quasi-randomly (i.e., matched for gender) assigned to a dynamic condition (20 males, 20 females) and a static condition (20 males, 20 females). Immediately after watching either a static or dynamic presentation of the playing system (*learning phase*), participants were asked to rate their mental effort invested in learning, perform a game performance test, and complete the card rotations test (*test phase*). The results indicated that spatial ability (evaluated via the card rotations test) was higher in males than in female students (*p* < 0.0005). Additionally, an interaction of gender and type of visualization were identified, supporting the ability-as-compensator hypothesis: female students benefited particularly from video modeling (*p* < 0.0005, *ES* = 3.12), while male students did not (*p* > 0.05, *ES* = 0.36). These findings suggested that a consideration of a learner's gender is crucial to further boost learning of basketball tactical actions from dynamic and static visualizations.

**Keywords:** video modeling; static pictures; motor learning; gender difference; basketball; physical education

#### **1. Introduction**

In recent years, technology has been gaining increasing importance in different learning environments. Within the physical education (PE) domain, technology has also become an integral part of the curriculum and instruction [1]. In fact, while highly advanced and sophisticated forms of technology are generally not available during PE lessons [2], dynamic visualizations such as video modeling examples remain the more readily available didactical tools for teachers to present and explain various motor skills [3]. Video modeling involves showing the student a recording of the expert performance of a motor skill [4,5]. According to Hoogerheide et al. [6], video modeling examples seem to be very

**Citation:** Rekik, G.; Belkhir, Y.; Mezghanni, N.; Jarraya, M.; Chen, Y.-S.; Kuo, C.-D. Learning Basketball Tactical Actions from Video Modeling and Static Pictures: When Gender Matters. *Children* **2021**, *8*, 1060. https://doi.org/10.3390/ children8111060

Academic Editor: Niels Wedderkopp

Received: 18 September 2021 Accepted: 15 November 2021 Published: 17 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

well suited for motor learning, due to their capability to deliver the information concerning how to perform a skill in an accurate way. Inspired by Bandura's social learning theory [7], a consistent body of research carried out in the PE setting has shown the effectiveness of observational learning through model-based videos. For example, it was found that video modeling examples enable inexperienced climbers to perceive and accomplish new possibilities for action and facilitate their climbing performances [8]. Moreover, a model-based video has been observed to be better than a videotaped replay of one's own performance for the acquisition and retention of the set and serve skills in volleyball [5]. Furthermore, viewing a skilled model was more effective than oral explanations for the acquisition of the shooting skill in handball [9]. In the same vein, Barzouka et al. [10] showed that observing a skilled model performing the skill of a pass in volleyball was more beneficial than oral explanations. According to Carroll and Bandura [11], observing a model would provide a perceptual blueprint of symbolic codes of the observed action. These blueprints will guide subsequent performance. In addition, Pollock and Lee [12] explained that modeling is an effective teaching method because actions which are tricky to describe verbally often can be demonstrated visually.

Despite these claims, based on the cognitive load theory [13] (a theory that considers how visual or auditory information impacts working memory and learning), Wong et al. [14] argued that videos modeling examples may not always be effective for learning, as they can become subject to transience effects. The transient information effect can be observed with videos or animations that provide a non-permanent flow of information that disappears from the computer screen [13]. Transient information requires that learners have to maintain previously presented information in WM in order to integrate it with later information [15,16]. These mental activities can overload the memory system and cause an overflow of the WM capacity [17]. The negative transient information effect provides a possible explanation as to why empirical research in the scope of cognitive load theory has suggested that replacing videos with a set of permanent/static pictures, describing the essential states of the dynamic event, may reduce the extraneous cognitive load. This instructional strategy allows learners to benefit from sufficient time to identify and process relevant information and effectively integrate it in long-term memory [18]. Moreover, viewing a series of static pictures offers the possibility to revise and compare different parts of the display as frequently as desired [19].

In this context, examining the effectiveness of video modeling vs. static pictures in learning different motor skills has raised considerable interest among sport didacticians/psychologists [20]. These scientific works dovetail nicely with findings from observational learning research. For example, Rekik et al. [21] showed that viewing skilled models performing tactical actions in basketball was more effective than viewing a series of simultaneous static pictures in terms of cognitive load, game comprehension, and attitudes (e.g., attention and enjoyment). Additionally, Rekik and his colleagues [22] recommended, whatever the complexity of the playing system, the use of video modeling examples (rather than static pictures) to teach and/or learn tactical actions in basketball. More recently, it was established that observing a skilled model performing a judo technique (through video) generated better recall-performances and guaranteed better motivation levels than different presentations of static pictures in university PE students [23,24]. The activation of the mirror neuron system has been particularly adopted by these scientists to argue the superiority of model-based videos over static visualizations (i.e., a series of photographs) in learning sport-motor knowledge/skills. This system was originally identified in primates, representing a neurophysiological circuit distributed across the pre-motor cortex that is automatically activated when someone is observing another person performing an action [25,26]. Additionally, as humans' actions are part of primary knowledge, their acquisition is very easy and requires little cognitive effort [27]. Consequently, viewing dynamic visual tools involving motor skills does not require excessive cognitive resources, because humans are biologically evolved to effectively acquire such kinds of knowledge. The phenomenon of learning human actions through video modeling examples is referred to as "the human movement effect" [27].

The major limitation of the above-mentioned studies examining the relative effectiveness of video modeling versus static pictures in learning is that the gender of learners has not been taken into consideration. Indeed, the gender difference in learning from dynamic and static visualizations has been reported in previous educational research carried out in non-sporting domains, yielding to discrepant results [28]. On the one hand, some studies have shown that animations were especially helpful for males in geographic and problem-solving learning, indicating that while males outperformed females with the animated presentation, both genders performed similarly under static and animated presentations [29,30]. This group of scientific works supports the "ability-as-enhancer hypothesis", indicating that high spatial ability learners benefited particularly from dynamic visualizations, and low spatial ability learners did not [31,32]. Indeed, it well established that spatial ability is higher in males than females [33–37]. On the other hand, another group of studies have shown that instructional animations were particularly helpful for females in learning chemistry and physical science topics [38,39]. Similarly, it was found that there is a significant presentation–gender interaction when learning a manipulative motor skill (i.e., Lego construction task), indicating that while female students outperformed males at the completion test with video modeling, no gender differences were found with the static presentation [40]. This second group of scientific works supports the "*the ability-as-compensator hypothesis*", indicating that low spatial ability learners (i.e., females) profited mainly from dynamic visualizations, and high spatial ability learners (i.e., males) did not [31,32,41].

While the gender difference in learning from dynamic and static visualizations was explored across a broad range of instructional domains, no explicit investigation has been conducted to examine the relationships between these two visual representations and learners' gender in learning motor skills in the PE/sport domain (i.e., motor skills requiring the whole body). We attempted to fill this knowledge gap via the present experiment, by exploring the gender difference in learning basketball tactical actions from video modeling and simultaneous static pictures in secondary school students. It was hypothesized that video modeling would be more beneficial than static pictures for learning tactical actions in basketball. It was also hypothesized that there would be a gender–instructional visualization interaction (it was an open question as to which gender would benefit most in this study due to the mixed results of previous non-sporting studies).

#### **2. Materials and Methods**

#### *2.1. Participants*

Eighty students (Mage = 15.28, SD = 0.49; 50% females) from a public secondary school in Tunisia completed the experimental procedure of the current study. They were selected based on sample convenience and school administrative support. The required sample size was calculated as 80 with alpha level of 0.05, the power of 0.80, and the effect size of 0.46 derived from a previous study [3]. The G\*Power software (Version 3.1; Düsseldorf, Germany) was used for sample size calculations [42]. A questionnaire was used to determine the participants' demographic information and their familiarity with basketball activity and/or any other related team sports. The inclusion criteria included: (i) registered in the secondary school supporting this study; and (ii) chronological age between 14–16 years. Exclusion criteria included: (i) playing basketball or any other team ball sports in a club (this criterion was adopted to prevent transferring effects across sports [43]); and (ii) current visual impairment. Participants were informed about the study's scope, and their written informed consent to participate in this study obtained thereafter. This study was approved by the ethics committee of the Ministry of Education, Tunisia (approval code: 2173/2017). This study was undertaken in accordance with the Declaration of Helsinki and its later amendments in 2013.

#### *2.2. Design*

A 2 × 2 mixed design with factors "Condition" (video modeling vs. static pictures) and "Gender" (male vs. female) was used to investigate the hypotheses in this study. Participants were quasi-randomly (i.e., matched for gender) assigned to a dynamic condition (20 males, 20 females) and a static condition (20 males, 20 females).

#### *2.3. Apparatus and Stimulus Information*

The experiment was conducted using an HP Pavilion dv6 Entertainment PC placed at a distance of 30 cm from the participants. The stimuli (via PowerPoint software) were presented on a 32 × 20 cm screen, with a 45◦ viewing angle.

Participants were requested to learn how to perform different tactical actions in basketball. A structured zone attack scene was developed in collaboration with two qualified teacher/basketball coaches (with over 12 years of experience). This playing system included three players (a playmaker ①, a winger ③, and a pivot ④) who carried out a coherent tactical combination which was composed of three passes before a basket was taken through a layup. Each pass corresponded to a new step made up of multiple offensive actions achieved by the players (e.g., lateral movements, screening, and layup). Next, this game executed by three expert players (Mage = 21.7 years, SD = 1.26), serving as models, was filmed from a camera (using Samsung Galaxy Tab 3 SM-T211) placed above the ground from the middle of the field in an elevated position (approximately 2.5 m high). The recording position was set to film the entire field of play and all players' actions. The recorded footage was transferred onto a computer via a fire-wire connection, and then was presented into a PowerPoint page. For the static presentation version, the continuous recording was divided into four static pictures, which depicted the key steps of the playing system. Photographs were captured using FastStone Capture 6.7 software (Barcelona, Spain), and play actions were denoted by the yellow numbered arrow-symbols. A dotted arrow refers to a simple pass; a solid arrow refers to a play movement; a double solid arrow refers to a layup; and a short perpendicular line at the end of a movement line refers to a screen. These static pictures enriched with arrows (820 × 972 pixels) were displayed simultaneously in one row into a PowerPoint page (see Figure 1). The dynamic and static presentations lasted for 12 s before vanishing from the screen, and they were system paced and purely visual (i.e., without any written/spoken commentary) in order to avoid a confounding occurrence of modality, redundancy, and temporal continuity effects [44]. 

**Figure 1.** Sequence of four static pictures showing the four key steps of the basketball playing system.

#### *2.4. Measurements*

This study incorporated a control variable (i.e., spatial ability), and three dependent variables including, mental effort, game performance, and learning efficiency.

#### 2.4.1. Spatial Ability

Students' spatial ability was evaluated through the card rotations test (CRT) [45]. CRT is a true–false test including two parts of 10 questions; it was developed to evaluate an individual's ability to see similarities and differences between the shapes. One point is given to each true answer, and the total scores could range from 0 to 160. Figure 2 shows one of the CRT items.

**Figure 2.** Example of a card rotations test item.

#### 2.4.2. Mental Effort

A 9-point scale ranging from (1) *very, very low mental effort* to (9) *very, very high mental effort*, was used to measure the mental effort invested during the study phase. This selfrating measure is valid and reliable for estimating cognitive load [46].

#### 2.4.3. Game Performance

A game performance task was performed in an outdoor basketball half-court, with two other male players (Mage = 16.22, SD = 1.2; semi-professional level with over 6 years of experience) who already knew the learning material. This test was conceived based on the recall–reconstruction paradigm [47]. Each tested student was instructed to reproduce as accurately as possible the tactical actions performed by a randomly chosen player from the learning material (i.e., playmaker, pivot, or winger). To guarantee the smooth running of the test, one of the semi-professional basketball players (used as teammates) was instructed to intervene by providing verbal corrective feedback each time the student performed a wrong action. A digital camera was used to record the students' game performance. Then two independent raters (qualified teacher/basketball coaches) scored the total number of correct and incorrect positions/actions. One point was awarded for each correct position/action; otherwise, participants received 0 points (see Table 1). The scores could range from 0 to 8. The inter-rater reliability was excellent and satisfactory (Cohen's κ = 0.91).


κ **Table 1.** Different steps of the basketball playing system and their related success criteria.


#### **Table 1.** *Cont.*

#### 2.4.4. Learning Efficiency

Learning efficiency was calculated based on Kalyuga and Sweller's computational approach: Efficiency = Game performance/Mental effort [48]. These combined indicators have been seen as an optimal tool to evaluate learning from instructional visualizations [13], and have been used in previous studies assessing the effect of external visualizations on tactical learning in PE [3,4] and the sports coaching domain [49,50]. According to this computational approach, a lower mental effort investment combined with higher performance scores (and a same mental effort investment combined with higher performance scores; or vice versa) would provide evidence of a more efficient learning condition.

#### *2.5. Procedure*

The experiment was run in groups of 10 students in an outdoor basketball court. In each group, students were tested individually with the experimenter observing (±90 min), and no participant had the opportunity to observe the performance of another participant. First, each student was quasi-randomly assigned to one of the visual conditions and was instructed to memorize as precisely as possible the evolution of the scene of the play (*learning phase*). The scene of the play was shown twice resulting in a total duration of 24 s (Figure 3). Students exposed to the static pictures condition were initially informed of the functions of the arrows before watching the game situation. Immediately after watching either a static or dynamic visualization of the playing system, the student was given 30 s to indicate his/her mental effort investment level, 1 min to perform the game performance test, and 7 min to complete the card rotations test (*test phase*). The time was controlled by the experimenter using a handheld stopwatch.

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**Figure 3.** Learning phase.

#### *2.6. Statistical Analyses*

Statistical tests were processed using STATISTICA Software (StatSoft, Hamburg, Germany). Mean and SD (standard deviation) values were determined for each variable. After verifying that the assumptions required for parametric tests were not violated using the Shapiro–Wilk test for distribution normality, a two-way ANOVA [2 Conditions (video modeling vs. static pictures) × 2 genders (female vs. male)] with repeated measures was used to analyze the student's spatial ability, mental effort investment, game performance, and learning efficiency. When ANOVA revealed a significant difference, a post-hoc Bonferroni was applied. The qualitative magnitudes were reported as partial eta squared (*n* 2 *p* ) and Cohen's mean standardized differences (*ES*) for post-hoc comparisons. The level of significance was set at *p* < 0.05. Following H'mida et al. [24], exact *p* values were reported, except when alpha level was >0.05 and/or <0.0005.

#### **3. Results**

Descriptive statistics for the control variable (i.e., spatial ability) for female and male students as a function of experimental conditions are presented in Table 2.

**Table 2.** Means and (standard deviations) for spatial ability, as a function of condition and gender.


† Significant difference between male and female students in the same condition.

#### *3.1. Spatial Ability*

The results showed a non-significant effect of condition [*F* (1.19) = 0.07, *p* > 0.05, *n* 2 *<sup>p</sup>* = 0.003], a significant effect of gender [*F* (1.19) = 47.89, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.99], and a significant interaction between these two factors [*F* (1.19) = 11.90, *p* = 0.0026, *n* 2 *<sup>p</sup>* = 0.38]. Post-hoc analyses showed that the male students had significantly better spatial ability scores than the female students in the video modeling condition (*p* < 0.0005, *ES* = 12.69), and in the static pictures condition (*p* < 0.0005, *ES* = 11.77). Further analyses revealed no significant differences between the two conditions (video modeling/static pictures) for the female students (*p* > 0.05, *ES* = 0.60), and for the male students (*p* > 0.05, *ES* = 0.97). Consequently, the data analysis showed that spatial ability is higher in males than in female participants.

#### *3.2. Game Performance*

The game performance scores recorded for female and male students as a function of experimental conditions are presented in Figure 4.

The results showed a significant effect of condition [*F* (1.19) = 71.55, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.79], a significant effect of gender [*F* (1.19) = 14.94, *p* = 0.0010, *n* 2 *<sup>p</sup>* = 0.44], and a significant interaction between these two factors [*F* (1.19) = 15.68, *p* = 0.0008, *n* 2 *<sup>p</sup>* = 0.45]. Post-hoc analyses for the female students showed significant differences between the two conditions (*p* < 0.0005, *ES* = 2.30). It was found that females performed significantly better in the video modeling condition than in the static pictures condition. However, post-hoc analyses for the male students revealed no significant differences between the two conditions (*p* > 0.05, *ES* = 0.39). The males performed at the same level regardless of the instructional visualization (video modeling/static pictures) in which they were exposed. Further analyses showed that the female students had significantly better game performances than the male students in the video modeling condition (*p* = 0.0010, *ES* = 1.41). Otherwise, it was found that female and male students had similar game performances in the static pictures condition (*p* > 0.05, *ES* = 0.30).

**Figure 4.** The game performance scores recorded for female and male students as a function of experimental conditions. # Significant difference between conditions for female students. † Significant difference between female and male students in the video modeling condition.

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#### *3.3. Mental Effort*

The mental effort scores recorded for female and male students as a function of experimental conditions are presented in Figure 5.

**Figure 5.** The mental effort scores recorded for female and male students as a function of experimental conditions. # Significant difference between conditions for female students. † Significant difference between female and male students in the static pictures condition.

 ଶ ଶ ଶ The results showed a significant effect of condition [*F* (1.19) = 76.12, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.80], a significant effect of gender [*F* (1.19) = 32.62, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.63], and a significant interaction between these two factors [*F* (1.19) = 61.73, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.76]. Post-hoc analyses for the female students showed significant differences between the two conditions (*p* < 0.0005, *ES* = 3.25). It was found that females invested more mental effort in studying the static pictures condition than in the video modeling condition. However, post-hoc analyses for the male students revealed no significant differences between the two conditions (*p* > 0.05, *ES* = 0.15). The males invested the same amount of mental effort regardless of the instructional visualization (video modeling/static pictures) in which they were exposed. Further analyses showed that the female students had invested more mental effort than the male students in studying the static pictures condition (*p* < 0.0005, *ES* = 2.38). Otherwise, it was found that female and male students invested the same amount of mental effort in the video modeling condition (*p* > 0.05, *ES* = 0.23).

#### *3.4. Learning Efficiency*

The analysis showed a significant effect of condition [*F* (1.19) = 80.67, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.81], a non-significant effect of gender [*F* (1.19) = 0.67, *p* > 0.05, *n* 2 *<sup>p</sup>* = 0.44], and a significant interaction between these two factors [*F* (1.19) = 38.99, *p* < 0.0005, *n* 2 *<sup>p</sup>* = 0.67]. Post-hoc

analyses for the female students showed significant differences between the two conditions (*p* < 0.0005, *ES* = 3.12). Therefore, for females, learning was more efficient in the video modeling condition than in the static pictures condition. However, post-hoc analyses for the male students revealed no significant differences between the two conditions (*p* > 0.05, *ES* = 0.36). The males achieved similar learning outcomes regardless of the instructional visualization (video modeling/static pictures) in which they were exposed. Further analyses showed that in the video modeling condition, learning was more efficient for the female students than for the male students (*p* = 0.0050, *ES* = 1.07). Otherwise, it was found that in the static pictures condition, learning was more efficient for the male students than for the female students (*p* = 0.0006, *ES* = 1.58). Representations based on Kalyuga and Sweller [34] of the learning efficiency measurement are illustrated in Figure 6.

**Figure 6.** Learning efficiency per experimental condition for high-content complexity, based on mental effort and comprehension performance. The diagram is a representation modeled after Kalyuga and Sweller [48], where VM-G = video modeling girls, VM-B = video modeling boys, SP-G = static pictures girls, SP-B = static pictures boys, GP = game performance, ME = mental effort, E = efficiency, Ecr = critical efficiency.

#### **4. Discussion**

The study reported in this paper was designed mainly to explore how students' gender could affect tactical learning in the PE domain, when processing an offensive basketball scene from video modeling and simultaneous static pictures. To examine the relationships between these two factors (i.e., visual representations and gender), a game performance test and a self-report of mental effort scale were used.

In line with the first hypothesis, the results showed that the video modeling does not lose its effectiveness in learning basketball tactical actions (whatever the gender of the learners), despite the addition of arrow-symbols which can allow learners to improve motor learning from static pictures [24,51]. These results are in line with a consistent body of research carried out either in sports or in other instructional domains, showing the positive effects of dynamic visualizations in learning when the content to be learnt is realistic and involves procedural motor knowledge [20,21,24,52]. Consequently, the human movement effect [27] has again been supported in learning sport skills requiring the whole body, indicating that video modeling examples were found to be within the working memory constraints of the students, and were not too long and/or complex to become subject to transient effects (see Wong et al. [14] for a discussion of how the transient nature of digital videos can sometimes hinder motor learning). As mentioned in the Introduction section, the instructional/cognitive benefits of dynamic visualizations (in comparison with statics) in learning/memorizing motor skills, were principally due to the activation of the mirror neuron system [25,26].

An additional crucial finding of the current study was the significant interaction between gender and visualization formats, indicating that while females performed better with the video modeling than with static pictures (i.e., they achieved higher game perfor-

mances with less mental effort investment), males did not gain particular benefits from dynamic presentation (i.e., they achieved the same game performances with the same amount of mental effort investment). These results are consistent with previous research indicating that females profited particularly from dynamic visualizations (and males did not) in learning descriptive knowledge [38,39]. More importantly, our results are in accordance with Wong et al. [40] showing that females outperformed males with the video modeling, and that no gender differences were found with the static pictures, in learning a manipulative motor skill (i.e., Lego construction task). To explain these results, it is indispensable to refer to the student's individual spatial ability that was evaluated as a control variable in our study through the CRT developed by Ekstrom et al. [45]. Indeed, it is interesting to note that males achieved better scores than females in the CRT, confirming the results of previous works indicating that spatial ability is higher in males than in females [33–36]. Consequently, our findings could support the "*ability-as-compensator hypothesis*", indicating that low spatial ability learners (i.e., females) profited mainly from dynamic visualizations, and high spatial ability learners (i.e., males) did not [31,32,41]. Following this hypothesis, dynamic formats can further boost the learning of students with low spatial ability by offering an explicit and continuous representation of the spatio-temporal elements of the system, and thereby, avoiding the process of mental inference from static presentation formats. Yet, dynamic and static visualizations can have similar effects on learning among students with high spatial ability as they are more cognitively prepared to generate an adequate mental representation of the learning content whatever the presentation format [32]. Another plausible explanation for the superiority of female over male students in learning from video modeling could be related to the anatomical differences in the mirror neuron system. It is well known that the female brain has a higher proportion (compared to male brain) of gray matter in the prefrontal cortex [53,54], which play a crucial role in human attention [55]. As attention is the first necessary condition in any form of observing and modeling behavior [7], it is logical to have found that females achieved higher learning outcomes than males when processing a basketball playing system from video modeling examples. However, it should be cautioned that this study did not include any anatomical brain data, leaving it partially speculative. Further research is needed to explore this issue by using objective measures (e.g., [56]).

Some limitations should be mentioned as is the case for all experimental research. First, in the current study we focused solely on spatial ability as potential gender cognitive variables that can influence the learning from dynamic vs. static visualizations due to its large documented impact. However, it was established that there are both biological and social differences in the ways that males and females process external stimuli [57]. Second, the participants' age may be a limiting factor for the generalization of our results' interpretation. Additional research on this topic should perhaps explore this pattern of results with children or older participants (e.g., university PE students). Third, the current investigation was based on the conventional learning-and-recall experimental procedure [3]. In other words, participants were asked to perform the recall-performance test immediately after they had just watched one of the two visual supports. It would be worthwhile in future studies to examine findings of this study in a real-world setting (i.e., during regular PE lessons). Lastly, all participants undertook the experimental procedure at the same time of the day (i.e., in the morning). Further studies are required to investigate the effects of the time of the day and instructional visualizations (i.e., static vs. dynamic) in learning about basketball tactical skills, because it was established that cognitive abilities such as reaction time, attention, and executive functions depend heavily on the time of the day [58].

#### **5. Conclusions**

The current study extends the existing research examining the relationships between instructional visualizations and gender, indicating a significant interaction between these two factors in learning sport-specific motor skills (basketball tactical actions). For female secondary school students, learning was more efficient with video modeling than with static pictures. However, male secondary school students achieved similar learning performances with both types of instructional visualizations. In summary, this study suggests that a consideration of learners' gender is crucial to further boost learning of basketball tactical actions from dynamic and static visualizations in adolescent students.

**Author Contributions:** Conceptualization, G.R.; methodology, G.R.; software, G.R.; investigation, G.R. and Y.B.; statistical analysis, Y.B.; data interpretation, G.R.; writing—original draft preparation, G.R. and Y.B; writing-review and editing, G.R., N.M., Y.-S.C. and C.-D.K.; supervision, M.J., Y.-S.C. and C.-D.K.; project administration, G.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Taipei Veterans General Hospital, grant number V95S1-013.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the ethics committee of the Ministry of Education, Tunisia (approval code: 2173/2017; Date: 9 October 2017).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data are available upon request to the corresponding author's email.

**Acknowledgments:** We would like to thank the students involved for their efforts, commitment, and enthusiasm throughout the study. We would also like to thank the PE teacher "Fahmi Ellouze" for his cooperation and technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

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#### *Article* **Gender Differences in Attention Adaptation after an 8-Week FIFA 11<sup>+</sup> for Kids Training Program in Elementary School Children**

**Chia-Hui Chen <sup>1</sup> , Ghazi Rekik 2,3 , Yosra Belkhir 2,4,5 , Ya-Ling Huang <sup>6</sup> and Yung-Sheng Chen 7,8,\***


**Abstract:** School-based exercise intervention is recognized as an optimal tool for enhancing attentional performance in healthy school children. However, gender differences in the training adaptation regarding attentional capacities have not been elucidated clearly in the current literature. This study aimed to investigate the effects of an 8-week Fédération Internationale de Football Association (FIFA) 11<sup>+</sup> for Kids training program on attentional performance in schoolboys and girls. Based on a quasi-experimental design, fifty-two children registered in year five of elementary school were assigned into the following groups: training boys (*n* = 13), training girls (*n* = 13), control boys (*n* = 13), and control girls (*n* = 13). The training groups undertook an 8-week FIFA 11<sup>+</sup> Kids intervention with a training frequency of five times per week, whereas the control groups were deprived of any exercise during the study period. All the participants maintained their regular physical activity and weekly physical education (PE) lessons (two 50-min lessons per week of school curriculum) during the training period. The Chinese version of the Attention Scale for Elementary School Children (ASESC) test was used for attentional assessment at the baseline and one week after the interventional period. The Kruskal–Wallis H test was used for between-group comparison, whereas the Wilcoxon signed-rank test was used for within-group comparison. Significant differences in total scale, focused attention, selective attention, and alternating attention were found in group comparisons (*p* < 0.001). Furthermore, the training children significantly increased their values in relation to total scale, focused attention, sustained attention, and selective attention (*p* < 0.05). Only training girls significantly improved their divided attention after the training period (*p* < 0.001, MD = −0.77, ES = −0.12). In conclusion, the FIFA 11<sup>+</sup> for Kids is an effective school-based exercise intervention for attentional improvement in school children. The schoolgirls demonstrated a positive outcome regarding divided attention after the interventional period.

**Keywords:** school-based exercise; pediatric health; concentration; gender difference; exercise intervention

#### **1. Introduction**

A fundamental aspect of school education is related to the attention and learning efficiency of students. Two questions that are always uppermost in all educators' and parents' minds are as follows: "Does this child have attention problems? How do I improve the attention span for children learning at school?" In the 1970s, Posner [1] stated that an

**Citation:** Chen, C.-H.; Rekik, G.; Belkhir, Y.; Huang, Y.-L.; Chen, Y.-S. Gender Differences in Attention Adaptation after an 8-Week FIFA 11<sup>+</sup> for Kids Training Program in Elementary School Children. *Children* **2021**, *8*, 822. https://doi.org/ 10.3390/children8090822

Academic Editor: Zoe Knowles

Received: 14 July 2021 Accepted: 15 September 2021 Published: 18 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

attentional process involves multiple cognitive functions in the central nervous system. Later, a framework proposed by Posner and his colleagues [2] outlined a holistic representation of attention networks including alerting, orienting, and executive control aspects. Neurophysiological studies, particularly in neuroanatomic, neuroimaging and neurobiological evidence support the role of cerebral functions associated with attentional control [3]. For example, biogenetic studies have reported that biological factors, such as dopamine D4 receptor (DRD4), dopamine transporter (DAT1), catecholamine-O-methyl transferase (COMT), and monoamine oxidase (MAOA), are related to psychological behavior and attention functions [4,5].

Attentional modulation and plasticity in the human brain are evidenced by attentional training interventions and a proper learning environment in the early stages of growth [6,7]. During development, preadolescent boys display different characteristics regarding attentional performance in comparison to preadolescent girls; these differences are due to physiological, psychological, and social aspects. For example, Clarke et al. [8] reported that schoolboys showed fewer theta electroencephalography (EEG) brainwaves and more alpha EEG brainwaves than schoolgirls, indicating gender differences in brain function during childhood. Additionally, gender differences in attentional control have been found in children with attention deficit hyperactivity disorder (ADHD) (i.e., girls had lower Conners ADHD rating scales) [9]. It is well documented that daily physical activities and exercise interventions are strongly related to attentional performance in school-aged children [10–13]. In acute exercise intervention, a 12-min continuous running exercise can immediately improve selective attention in children [14]. Additionally, an acute bout of 20-min treadmill walking at a moderate intensity of 60% maximal heart rate results in functional improvement in attentional tasks in 10-year-old children [15]. The modulation of exercise-induced neurotransmitters in the cerebrum (such as adrenaline, dopamine, and brain-derived neurotrophic factors) is considered a primary mechanism to alter post-exercise attentional performance [16,17]. In terms of the chronic effects of exercise intervention, a longitudinal study reported that developing executive function skills before and during school age was strongly associated with elementary school mathematics performance, indicating the important role of attention and motor performance in the early development of school children [18]. Moreover, school-based exercise interventions can contribute to beneficial outcomes regarding attention control and academic performance in school-aged children [19]. Superior cognitive improvements and higher levels of physical engagement were also identified in school children who undertook a 6-week team game in comparison to those who undertook 6 weeks of aerobic exercise [20]. The cognitive benefits of chronic exercise interventions in children have been suggested as being a result of exercise-induced adaptation in cognitive–motor interactions of cerebral regions, such as the prefrontal cortex, motor cortex, and basal ganglia [21]. This assumption has been evidenced by an increase in spatial working memory after an 8-week gymnastics training program in school children [22].

The Fédération Internationale de Football Association (FIFA) 11<sup>+</sup> for Kids is a structured training program with the aim of improving neuromuscular functions in preadolescent children [23,24]. Recent studies have reported on chronological adaptation in enhancing skill-related physical fitness components of youth football players [25–27]. For example, Rössler et al. [25] found that footballers aged 7–12 significantly improved their Y-balance capacity, jumping ability, agility, and dribbling ability after a 10-week FIFA 11<sup>+</sup> for Kids program. However, the optimal benefits regarding the physiological adaptation of the FIFA 11<sup>+</sup> for Kids were only reported in sports trained youth players. Recently, our laboratory [28] conducted an intensive 8-week FIFA 11<sup>+</sup> for Kids training program in elementary school children. Our findings reported the positive benefits of physical fitness (e.g., sit-and-reach, broad jump, sit-up, and 800 m run) and attentional capacity. This finding implies that structure-based exercise interventions can positively improve attentional performance in school children.

To the best of our knowledge, information regarding the influence of gender differences in exercise training adaptations on attentional control has not yet been elucidated in the literature, particularly in relation to school children. Most importantly, we seek to identify which area of attentional performance can help schoolteachers deliver appropriate course curricula to approach the demands of individual children. Therefore, the purpose of this study was to compare gender differences in attentional adaptation after an 8-week FIFA 11<sup>+</sup> for Kids training intervention in elementary school children. The secondary purpose of this study was to identify what attentional capacity could be adaptable to the exercise intervention. It was first hypothesized that the 8-week FIFA 11<sup>+</sup> for Kids would enhance attentional capacities in training children. The second hypothesis was that training girls would be superior in terms of training adaptation in all attentional assessments, compared to training boys and control children.

#### **2. Materials and Methods**

#### *2.1. Participants*

Fifty-two healthy children from a public elementary school voluntarily participated in this study (Sanchong district, New Taipei City, Taiwan). Based on a quasi-experimental design, thirteen children of the same gender were assigned into the FIFA 11<sup>+</sup> for Kids Boys (training boys) group, the FIFA 11<sup>+</sup> for Kids girls (training girls) group, control boys group, or control girls group (see Figure 1).

**Figure 1.** Experimental flow diagram of the study.

The inclusion criteria were as follows: (1) registered in the fifth year of elementary school; and (2) chronological age between 10–12. Exclusion criteria included the following: (1) current neurological or cardiovascular diseases; (2) psychological disorders; (3) taking medicine that affects psychometric status (e.g., benzodiazepines, anticonvulsants, antidepressants).

Prior to the experiment, all children and their parents and schoolteachers were informed of the scope of the study and the experimental procedure. All children were screened and there were no contraindications to participation. All children and parents signed informed consent forms. Subsequently, the children were familiarized with the experimental tests. This study was approved by the Human Research Ethics Committee of University of Taipei (UT-IRB-2020-003). This study was undertaken in accordance with the Declaration of Helsinki and its later amendments in 2013.

#### *2.2. Experimental Procedure*

The study was conducted during the spring semester of the school year. Based on the school curriculum and teaching schedule, one class of 26 children with an equal number

of boys and girls was allocated to the training groups while another 13 boys and 13 girls from another class were assigned to the control groups. During the baseline stage, the participants undertook anthropometrics measurements for height (Seca 213; seca GmbH and Co. KG, Hamburg, Germany) and body weight (Xyfwt382; TECO, Taiwan) in the school health center. Afterwards, the participants performed the Chinese version of the Attention Scale for Elementary School Children (ASESC) test for attentional assessments in their classrooms. The participants were given hard copies of ASESC testing sheets and a pencil to complete the attentional assessment. The duration of the ASESC test was around 50 min in total (including resting intervals and ten tasks). The following week, the training groups began an 8-week FIFA 11<sup>+</sup> kid intervention with a training frequency of five times per week. Conversely, participants in the control groups were deprived of any exercise intervention during the study period. All participants were told to maintain their regular physical activity and physical education (PE) lessons (two 50-minutee lessons per week of school curriculum) during the training period. A post-training ASESC test was conducted a week after the training period, following the same testing procedures as the baseline measurement. The participants were asked to refrain from strenuous exercises 24 h before the baseline and post-training assessments. A research assistant was blinded to conduct the ASESC tests in this study. One research fellow evaluated the score of attentional assessment in accordance with the ASESC test guidelines. Figure 1 shows the experimental flow of the study.

#### *2.3. Training Intervention*

The FIFA 11<sup>+</sup> for Kids exercise program was used as a training intervention in this study. The program consisted of seven types of motor coordination exercises (running, skating jumps, single-leg stance, push-ups, single-leg jumps, spiderman, and sideways roll) with five variations (from basic to advance) [23]. Overall, a total of 35 exercises were included in the exercise program. The details of the FIFA 11<sup>+</sup> for Kids intervention, as used in a school exercise program, are described in our recent publication [28].

During the training period, each training session (lasting 40-min) was conducted by a physical education teacher and a research assistant, both of whom were familiar with the FIFA 11<sup>+</sup> for Kids program. The training boys and girls were instructed with appropriate movement operation and performance skills during training sessions. All training sessions started with a roll call at 8:00 a.m. on school days. All training boys and girls fully attended the training sessions during the training period.

#### *2.4. Attentional Assessment*

The Chinese version of the ASESC test developed by Lin and Chou [29] was used as an attentional assessment tool in this study. This scale is a reliable tool for a multidimensional attention test based on the "Clinical Attention Model" proposed by Sohlberg and Mateer [30,31]. The ASESC test consists of ten variants of attentional tasks (from item 1 to item 10) and is divided into (1) focused attention (item 1 and 2, 1 min for each test); (2) sustained attention (item 3 and 4, 5 min for each test); (3) selective attention (item 5 and 6, 1 min for each test); (4) alternating attention (item 7 and 8, 1 min for each test); and (5) divided attention (item 9 and 10, 2.5 min for each test).

Focused attention refers to an individual's ability to directly respond to particular visual, auditory, or tactile stimuli. The subscale includes number-oriented and text-oriented subtests in which participants identify a specific number and Chinese characters. Sustained attention refers to an individual's ability to maintain consistent behavioral responses during continuous and repetitive activities. This subscale includes petal comparison and digital circled subtests. Selective attention refers to an individual's ability to maintain action and cognition in the presence of external stimuli or fierce competition. This subscale includes a map search and symbol recognition subtests. Alternating attention is the ability of an individual to control attentional allocations with the mental flexibility to switch between dissimilar cognitive tasks. This subscale includes alternating symbols and a number of alternating subtests. Divided attention is the ability of an individual to respond appropriately to multiple tasks simultaneously. This subscale includes numerical and monophonic as well as pattern and monophonic detection subtests.

In terms of reliability, the scale scores were between 0.77 and 0.83 for the Cronbach α reliability coefficient, showing its good internal consistency. The test–retest reliability after four weeks was between 0.71 and 0.91. In terms of validity, the correlation between the full scale and each subtest was between 0.63 and 0.77 [29].

#### *2.5. Statistical Analyses*

Descriptive data of the measured variables are presented as mean and standard deviation (SD) or median and interquartile range (IQR, 25%–75% percentiles). The normality of study variables was examined with the Kolmogorov–Smirnov test. One-way analysis of variation (ANOVA) was used to analyze physical characteristics among the groups. A nonparametric test was used to compare all variables of the ASESC test based on the normality examination. The Kruskal–Wallis H test was used for between-group comparisons, whereas the Wilcoxon signed-rank test was used for within-group comparisons. Significant differences between the means or medians were set as *p* < 0.05. Additionally, Cohen's d effect size (ES) was used to quantify the magnitude of the training effect. The level of ES was defined as trivial (0.0–0.2), small (0.2–0.6), moderate (0.6–1.2), large (1.2–2.0), and very large (>2.0) [32]. Statistical analyses were conducted using SPSS® Statistics version 25.0 (IBM, Armonk, NY, USA) and Microsoft Excel 2016 (Microsoft Corporation, Redmond, WA, USA).

#### **3. Results**

#### *3.1. Physical Characteristics*

The physical characteristics of age, height, and body weight in all study groups are shown in Table 1.


**Table 1.** Physical characteristics of the participants.

Data are presented as minimum, maximum, and mean and standard deviation (Mean ± SD).

#### *3.2. Attention Scales for Elementary School Children Test*

As shown in Table 2, qualitative data of each ASESC item are analyzed with the Kruskal–Wallis H test for intergroup comparison and the Wilcoxon signed-rank test for intra-group comparison. For group comparisons, a significant difference was found in item 4 (*p* < 0.019), item 6 (*p* = 0.038), item 9 (*p* = 0.019), and item 10 (*p* = 0.038) of baseline assessment. In the post-training assessment, a significant difference was found in item 1 (*p* < 0.001), item 5 (*p* < 0.001), item 6 (*p* < 0.001), and item 7 (*p* = 0.019). Significant differences of pairwise comparisons between baseline and post-training assessments were identified in items 1, 2, 3, 4, and 5 for the training boys, items 1, 3, 4, 5, 6, and 7 for the training girls, items 3, 4, 6, and 9 for the control boys, and items 6 and 7 for the control girls (*p* < 0.005).


**Table 2.** The Attention Scale for Elementary School Children test items scores before and after the interventional period.

Data are presented as median and interquartile (25–75%). Kruskal–Wallis H test was used for group comparison (significant difference indicated as #); Wilcoxon test was used for baseline and post-training comparison (significant difference indicated as \*). *n*= number; MD = mean difference; ES = effect size.

In the total and subscales of the ASESC test (Figure 2), the Kruskal–Wallis H test demonstrates a significant difference in total scale, focused attention, selective attention, and alternating attention (*p* < 0.001). In comparing baseline and post-training assessments (the Wilcoxon signed-rank test), significant differences in pairwise comparison were found in total scale [*p* < 0.001, mean difference (MD) = −12.77, ES = 0.63], focused attention (*p* < 0.001, MD = −5.85, ES = −0.93), sustained attention (*p* < 0.001, MD = −4.38, ES = −1.14), and selective attention (*p* = 0.019, MD = −1.38, ES = −0.33) for the training boys; total scale (*p* < 0.001, MD = −17.15, ES = −0.90), focused attention (*p* = 0.038, MD = −5.15, ES = −0.96), sustained attention (*p* < 0.001, MD = −0.92, ES = −0.15), selective attention (*p* < 0.001, MD = −5.38, ES = −1.29), and divided attention (*p* < 0.001, MD = −0.77, ES = −0.12) for the training girls; sustained attention (*p* < 0.001, MD = −3.85, ES = −0.68) and selective attention for the control boys (*p* < 0.001, MD = 4.31, ES = 1.16); and focused attention (*p* < 0.001, MD = 2.92, ES = 0.58), sustain attention (*p* = 0.019, MD = −1.92, ES = −0.35), and selective attention (*p* = 0.019, MD = 3.31, ES = 0.56) for the control girls. − − − − − − − − − −

**Figure 2.** Pooled and individual subscales of the Attention Scale for Elementary School Children test before and after the interventional period in the FIFA 11<sup>+</sup> for Kids boys, FIFA 11<sup>+</sup> for Kids girls, control boys, and control girls; (**A**) total scale, (**B**) focused attention, (**C**) sustained attention, (**D**) selective attention, (**E**) alternating attention, and (**F**) divided attention. \* indicates significant difference in the Wilcoxon test.

#### **4. Discussion**

As its first experimental initiative, the current study was designed to compare gender differences in attentional performances after an 8-week FIFA 11<sup>+</sup> for Kids training intervention in elementary school children. Participants were assigned to two training groups who participated in the FIFA 11<sup>+</sup> for Kids intervention and weekly PE lessons, and to two control groups who participated solely in weekly PE lessons. To achieve our research purpose, all children were invited to perform the ASESC test before and after the eight-week study period [29].

It is interesting to note that our results show significant increases in total scale, focused attention, sustained attention, and selective attention in both training groups, and divided attention solely in training girls. This finding demonstrated the positive effects of an 8-week structured exercise program on psychophysiological functions in processing focus-related cues in training children. The benefits of supplementary activities via the FIFA 11<sup>+</sup> for Kids intervention on attentional capacities could be explained following the "*cardiovascular fitness hypothesis*" [33]. Accordingly, increased cardiovascular fitness, caused by regular physical activity adopted by an individual over time (i.e., longitudinal physical activity program over several weeks) is thought to improve angiogenesis [34] and neurogenesis [35] in areas of the brain that support memory and learning, subsequently enhancing cognitive performance [13,36]. As attention is a central mediator of cognition and learning performance [37,38], it is legitimate to suppose that an individual's attention capacities can be enhanced by participating in a supplementary chronic physical activity intervention (e.g., the FIFA 11<sup>+</sup> for Kids program in the present study). Hence, the positive effects of an additional school-based program on attentional performances, as observed in the training boys and girls, could also be explained as following the "*cognitive stimulation hypothesis*" [39,40]. Indeed, the FIFA 11<sup>+</sup> for Kids intervention could be classified as a cognitively engaging physical activity [28], as most exercises required attention, anticipation, and spatial orientation, particularly while engaged in dual-tasks [26]. Recently, some researchers have argued that chronic physical activities with a relatively high cognitive engagement (where children have to plan strategically and focus attention) have a larger effect on cognitive functions (including attentional capacity, problem solving, etc.) compared to simple physical activities intended to improve cardiovascular performance [20,28,41].

The second hypothesis in the present study assumed that training girls would benefit more from attentional improvement than training boys after the interventional period. This hypothesis could not be determined in focused, sustained, selective, and alternating attentions but it was identified in divided attention. Notably, we used numerical and monophonic, and pattern and monophonic detection subtests to evaluate divided attention in the present study. The children had to identify the right cues to achieve their tasks. It is interesting to note that divided attention is a type of simultaneous attention that allows an individual to synchronize different information cues and successfully carry out multiple tasks in the same period of time [42]. This evidence was supported by the poor capacity for divided attention observed in school children with ADHD [9]. Superior training adaptation regarding divided attention observed in girls could be related to gender differences in brain anatomy. It is well known that the female brain has a higher proportion of gray matter (densely packed with cell bodies), while the male brain has a higher proportion of white matter (consists of myelinated axons that form the connections between brain cells) in the prefrontal cortex [43,44]. In this context, Kanai and Rees [45] highlighted the important role of gray matter in attention. In fact, having more gray matter may explain why young women are usually more efficient at processing information, and usually excel at juggling several activities [46]. As a result, training girls profit more than boys from their participation in continual physical activity in terms of divided attention. It seems that longterm facilitation of the FIFA 11<sup>+</sup> for Kids intervention could enhance attentional capacity in relation to multiple motor performance tasks in school children. This speculation was supported by the obtained results regarding divided attention in training and/or control girls groups.

Coincidently, participation in both control boys and girls significantly improved sustained attention. This finding indicated an increase in maintaining continuous and repetitive engagement. A possible explanation for this observation is related to daily routine regarding study activities and weekly engagements in school-based PE lessons after the winter vocation in the control groups. A review article conducted de Greeff et al. [36] reported that regular physical activities contribute positively to working memory, sustained attention, and academic performance in preadolescent children. As such, heathy children could possibly benefit their own sustained attention via regular school activities.

Several limitations of this study should be kept in mind, when interpreting the results. First, the small sample size may be a factor for the generalization of our results. This limitation is unavoidable because this investigation was carried out during the first wave of the COVID-19 pandemic in 2020, making it difficult for us to use a larger sample pool. Second, as a result of the same recruitment difficulties, we did not use a control group (inactive) in our experimental procedure. Third, the daily physical activities of both groups were not monitored during the study period. The lack of individual profiles of physical activities may potentially limit the interpretation of our research outcomes. Fourth, although the training children performed exercises according to the FIFA 11<sup>+</sup> for Kids guidelines, individual variation in training intensity and involvement of group activities may be essential factors affecting training adaptation. Future studies should use tools to quantify training intensity (e.g., heart rate monitor or rating of perceived exertion). Lastly, the level of sexual maturation could be a potential factor influencing the attentional performance between boys and girls. Our findings were limited by the absence of biological examination to exclude the effects of age.

In the present study, attentional capacities were evaluated through convenient measurements. Further research is needed to explore brain adaptation regarding the positive effect of an 8-week FIFA 11<sup>+</sup> for Kids intervention on attention in elementary school children. This can be performed through empirical measures, such as EEG or functional near-infrared spectroscopy (fNIRS).

#### **5. Conclusions**

In conclusion, the FIFA 11<sup>+</sup> for Kids intervention is an effective school-based exercise for attentional improvement in schoolboys and girls. Facilitating an eight-week training program during the semester contributes to optimal performance in focused attention, sustained attention, and selective attention in year 5 schoolboys and girls. Likewise, schoolgirls show positive outcomes in divided attention after a supplementary exercise intervention on school days. The efficiency of the FIFA 11<sup>+</sup> for Kids intervention for attentional adaptation in association with academic performance and psychometric health needs to be examined in future studies.

**Author Contributions:** Conceptualization, C.-H.C. and Y.-S.C.; methodology, C.-H.C. and Y.-S.C.; investigation, C.-H.C. and Y.-L.H. data curation, C.-H.C., G.R. and Y.B.; writing—original draft preparation, C.-H.C., G.R., Y.B., Y.-L.H. and Y.-S.C.; writing—review and editing, C.-H.C., G.R., Y.B. and Y.-S.C.; supervision, C.-H.C. and Y.-S.C.; project administration, C.-H.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of University of Taipei (protocol code UT-IRB-2020-003).

**Informed Consent Statement:** Informed consent was obtained from all the subjects involved in the study.

**Data Availability Statement:** The data are available upon request to the corresponding author via email.

**Conflicts of Interest:** No competing interest is reported for this study.

#### **References**

