The video feedback viewing in novice weightlifters: Total control strategy improves snatch technique during learning

Abstract

This study aimed at comparing the efficiency of two viewing control strategies, total control versus partial control, in correcting the snatch technique in school-aged boys (10–12 years old). Thirty-nine participants, with 2 months of weightlifting training experience, were divided into three groups: total control, partial control, or a control group. The Kinovea version 0.8.15 software was used to measure the kinematic parameters of the snatch technique before (T0) and after six learning sessions (T1). Following the learning sessions, total control group showed greater improvements for all kinematic parameters compared with the partial control and control group (e.g., the horizontal displacement (i) in the first pull (Δ Dx2: 18.17 ± 26.75%, p < 0.01, d = 0.83), (ii) between the first and the second pulls (Δ DxV: 25.97 ± 18.02%, p < 0.001, d = 1.52) and from the most forward position to the catch position (Δ DxL: 19.98 ± 21.60%, p < 0.01, d = 1.36), while the partial group improved only on the Dx2 (Δ Dx2 = 21.53 ± 20.40%, p < 0.01, d = 0.86). The present results indicate that the intensive use of the interactive features (e.g. pause, play, forward, and backward) and the asked questions during the first learning phase were essential for the improvement of the snatch technique. These findings have potential practical implications for coaching and physical education teaching.

Keywords

Barbell children error detection kinematics motor learning

Introduction

In the current digital era, video technology has become, more than ever, highly valued as means of effective and creative teaching in physical education (PE).¹ One of the common ways in which such technology is used during PE classes is the video feedback intervention. A recent study carried out by Souissi et al.² found that video feedback from previous trials allows learners to monitor, evaluate and adjust their performance in subsequent trials. Similarly, it has been shown that video feedback can improve performance and brain activity during training compared to the video viewing of another person performing the same task.³ A previous study, reported that the combination of video demonstration by an expert and the video feedback, during learning of gymnastics skills, induces more optimized performance than video feedback only.⁴ In addition, Aiken et al.⁵ and Souissi et al.⁶ argue that the delayed combination between video demonstration and self-controlled video feedback improves the technical performance in complex tasks.

In the recent years, motor learning researchers have invariably sought optimal conditions of video feedback use to enhance motor skill acquisition. Today, there is a growing body of evidence to suggest that giving learners the possibility to control some features of their learning environment enhances motor learning in adults,^7,8 as well as in children.^6,9,10 In fact, giving a learner control over some specific features during observational learning including the simultaneous presentation of video feedback and video demonstration on split screen,¹¹ the delivery of video demonstration^12,13 and the delivery of video feedback^6,8,14 yield superior motor learning compared to externally imposed condition (i.e. a condition in which a learner is yoked to a self-controlled counterpart). These motor learning advantages are quite rugged given they have been demonstrated for a variety of learning variables.

Even though there is clear evidence of the advantage of this strategy of self-controlled feedback schedule based on the hypotheses that it allows participants to respond to their psychological need for autonomy, which results in higher levels of self-efficacy¹ and intrinsic motivation.^15,16 It also favors a further active engagement in the acquisition phases^6,17 and a deeper cognitive commitment during the task. This is translated in terms of higher levels of preparation,¹⁸ performance errors identification capacity,^6,8,19 task recall,^11,20 and feedback processing,¹⁴ which altogether contribute to optimal learning.

According to the above arguments, one of the main advantages of self-controlled video feedback group over the other-imposed feedback group lies in the possibilities to intentionally arrange, shape, and optimize relevant information with regard to the cognitive apparatus of learners. However, Mayer and Moreno²¹ revealed that the information presented in complex or fast-pace video is frequently too transient to enable essential and thorough cognitive processing. The uninterrupted transiency of video may not provide learners with sufficient time to process all of the relevant elements.^22–24 According to Schwan and Riempp,²⁵ a video presentation must be well suited for the mental apparatus and cognitive needs of the learner. Here, control features of video come into play whereby tasks of shaping the information presentation are returned to the learner in order to accommodate it to their cognitive requirements. According to Wouters et al.²⁶ and Mayer et al.,²⁷ the pacing principle is imposed to reduce cognitive load during the observation by adding pauses to video dynamic representations, thus relieving learners of the need to permanently process new information. In addition, during pauses, learners have the opportunity to detect relevant information and structure their knowledge.^28,29 In addition, Cheon et al.³⁰ showed that offering learners an engaging instructional activity, like asking questions during a video pause during the instructional animation about the formation of lightning, is likely to make the pause phase more efficient, because this activity should enhance the cognitive processing of information from the previous segment. Encoding and retrieving information during this instructional activity enhance learners’ schema construction and produce higher scores in both recall and transfer tests in active pause group.

However, previous research in learning showed that widening video control fields increases phenomenon assimilation during the video presentation that explained (i) the causes of day and night²⁸ or (ii) how to tie nautical knots.²⁵ With a toolbar, learners can pause an educational video when they need further information processing time and also replay phases that are more intricate. In contrast, the lack of adaptation with the video may lead to shallow information processing or cognitive overload.^25,31

To date, however, no research has been conducted to determine whether there are optimal strategies for manipulating video in the field of sport. The aim of this research was therefore to examine the effects of combining a self-control of video feedback delivery (frequency = 25%) with two different control strategies of video feedback viewing (total or partial control) on the correction of technical errors in the weightlifting Olympic snatch movement learning process. For that purpose, we compared the technical performance of the snatch in weightlifting for a group of children who have the possibility of totally controlling the video playback through the use of pause, play, forward, and backward keys (TC) with (i) a group of children who have the possibility of partially controlling the video playback through the use of the pause/play key only (PC) and (ii) a group of children who have no control over the video-based content (CONT). We hypothesized that self-control of video feedback delivery with a total control of video feedback viewing would be more appropriate for the technical error correction in snatch exercise learning in youth. Additionally, the TC group was expected to increase their use of the control keys and questions for clarification during the first learning phase due to increased error detection, followed by a decrease in the second learning phase due to decreased errors in the snatch exercise.

Methods

Participants

Forty-eight boys were identified as potential participants for this study. Four were excluded because they failed to meet the inclusion criteria, two did not provide informed consent form, and three withdrew from this experiment (Figure 1).

Figure 1.

Flow diagram of the study.

A total of 39 healthy boys (age = 10.9 ± 0.6 years; body height = 143.2 ± 5.7 cm, body mass = 39.3 ± 5.4 kg, body mass index = 19.1 ± 1.3 (kg/m²) voluntarily participated in the present study. All children practiced weightlifting at the same school with the same instructor and had an experience of 2 months in practicing this sport prior to the experiment. During this period, technical errors in weightlifting were corrected only through verbal feedback for all learners. The participants and their parents were informed of the experimental procedure and signed informed consent was provided by the parents. In addition, the present study was conducted according to the Declaration of Helsinki. The protocol was approved by the local Research Ethics Committee (CPP: N° 0102/2019). Children were classified as prepubertal (stage 1) by a pediatrician according to Tanner‘s criteria.³²

A participant was included to the present study if his: (i) age was between 10 and 12 years, (ii) had no history of musculoskeletal, neurological, or orthopedic disorders and had no history of lower extremity and back/spine injury or surgery in the 6 months prior to testing (these might have affected their physical ability), (iii) was able to understand and follow simple instructions, and (iv) had no visual or cognitive problems.

After the pretest, subjects were randomly assigned to one of three feedback strategy groups. The groups were as follows: (i) Total Control group (TC = 14)—in this group, learners had the possibility to pause, jump forward or back in the video feedback content; (ii) Partial Control group (PC = 13)—in this group, learners only had the possibility to pause the video and, then play it again at their convenience using the pause/play key; and (iii) the control group (CONT = 12)—in this control condition, learners had to watch the entire video feedback, from beginning to end, without having any sort of control over the displayed visual content. Initially, participants from the three groups had the same levels of technical performance, body mass index, body height, and body mass. Descriptive characteristics of the participants of each group are shown in Table 1.

Table 1.

Descriptive characteristics (mean ± standard deviation) of the participants.

Groups	Total control (n = 14)	Partial control (n = 13)	Control group (n = 12)
Age (years)	10.92 ± 0.58	10.97 ± 0.61	10.93 ± 0.66
Height(cm)	142.79 ± 6.6	143.62 ± 5.44	143.33 ± 5.40
Body mass (kg)	39 ± 5.35	39.69 ± 5.65	39.25 ± 5.64
Body mass index (kg/m²)	19.04 ± 1.32	19.14 ± 1.38	18.99 ± 1.39

Procedures

During the week before the experiment, all participants were familiarized with the equipment and the experimental procedures to minimize the learning effect during the course of the study. Then, they were tested at two times: a week before (pretest: T0) and one day after (posttest: T1) the 3-week training period. All test sessions were performed in the afternoon between 2 and 5 pm. The pretest and the posttest were focused on the evaluation of the snatch movement technique using a kinematic analysis. During the test sessions, no video demonstration or video feedback or correction type provided to learners.

The participants from each group performed two weightlifting training sessions per week for 3 weeks. Each training session comprised 2 blocks of 12 repetitions. All subjects have the same request frequency of the video feedback delivery (25%) but with different control strategies of video feedback viewing between groups as previously described. Furthermore, participants from the TC group were instructed to use the play, pause, rewind, and forward keys. On the other hand, participants from the PC group were instructed to only use the play/pause key. Participants from the CONT group were forbidden to use any of the control keys while viewing the video feedback. Each learner from all three groups was given the space to ask questions immediately after the video feedback viewing is complete.

In the present study, a professional coach in weightlifting defined all of the snatch success criteria as well as technical errors correction during snatch motor skill sessions using all of the currently available biomechanical research materials.^33,34

During the training sessions, the professional coach was also involved in qualitative analysis of the snatch movement and to answer learners’ questions in all groups about video feedback and correcting errors. Among these questions we find for example: is the bar pulled away from my body during the second pull? Is the triple extension complete during the pull?

At the beginning of each training session, the learners performed a 15-min warm-up that included games, dynamic stretching exercises, and weightlifting movements, such as pulls and squats.

During each training session, at the beginning of each series of twelve trials, each participant from all three groups, was invited in a separate room to watch a slow motion video demonstration of the snatch, carried out by an expert model, accompanied by a detailed description of the success criteria. All the success criteria of the snatch movement were verbally narrated in the video (i.e., using an audiovisual video). Once viewing is finished, the learner is tasked to step onto the weightlifting platform and perform the number of repetitions required.

At the beginning of the first trial, all participants from the three groups were informed that they would be allowed to access their video feedback after three trials at their request. They were also told that if they did not ask for self-observation in the first nine trials of a block, feedback would be inevitably provided in the last three repetitions on that block. No participants opted to request video feedback in the last three trials of the block. After each repetition, learners had the right to a 30-s break to decide on whether they wanted to see their video feedback. They were also informed that they would not receive verbal correction, unless they requested it. The verbal corrections of common errors are presented in Table 2. Each learner was allowed to watch his requested video feedback from start to finish only once.

Table 2.

Errors committed by the participants during the learning phase and their corrections.

Phase	Errors	Correction
At start position	The learner‘s chest was behind the bar (toward the heels).	The shoulders and knees should be slightly forward of the bar, the shins are in contact with the bar, the hands extended, and the back fixed.
At first pull	Elbow flexion starts from the first pull and the bar moves away from the body.	Keep your hands stretched so you have to combine a triple extension (ankle–knee–hip extension) with a shrug of the shoulders.
At second pull	The bar moves backward and an imperfect triple extension	Pull the bar vertically toward the chin.
The turnover under the barbell	A jump higher, the bar moves further back.	Lightly discard the feet on the sides followed by deep flexion of the knees (squat). Back under the bar by unfolding the arms.
Stabilization under the bar	No balance.	Clear the chest, block the shoulders, the bar should be right behind the head.

The video sequence requested by learner was replayed 25 s later in slow motion replay mode (25% of the real speed). In addition, during all learning sessions, participants executed all trials in isolation from one another to prevent interaction and, in turn, avoid all sorts of peer-assisted learning.

Data collection

Technical performance

The learner performed the snatch task with a 5 kg bar placed on two 21 cm height supports in a weightlifting hall. Two landmarks (o, i, j) (100 cm) were placed on the vertical plane for each end of the barbell, allowing to convert the displacement measurements into centimeters.

Two digital cameras [(Sony HXR-MC2500) HD: 50 frames per second (50 Hz frequency)] were placed on each side plane at a distance of 5 m and of 1.5 m height from the ground. One video camera was placed on the right and another one on left of the participant. Two markers were placed on the extremities of the bar as shown in Figure 2.

Figure 2.

Placement of the cameras during the test sessions.

The collected video sequences were treated by Kinovea 0.8.15 software to provide the horizontal and vertical displacements of the bar (Dx2, DxL, DxV, DxT, VTR, and Diff Tr) (Figure 3). Hoover et al.³⁵ established a number of important kinematic factors that contribute to successful snatch as follows: horizontal (rearward) displacement of the bar in the first pull with respect to the starting position (Dx2), the amount of the looping of the bar in the catch phase (DxL), horizontal displacement of the bar between the receiving position and the reference line (DxT), horizontal displacement of the bar between the first and second pulls (DxV) and maximal vertical displacement of the bar at catch position (VTR), and the difference between the left and the right side distances of the bar trajectory in absolute value (Diff Tr).

Figure 3.

Description of bar path kinematic variables used to assess quantitative changes in bar positions.

Several previous studies have shown that the decrease in values of horizontal and vertical displacements of bar improves the snatch technique,^2,36 increases power and reduces energy loss in weightlifters.^37–39

The mean of interactions with video feedback viewing (MI)

The use of control features in both groups was assessed at each training session by calculating the number of interactions of learners with video feedback. We calculated the mean of interaction in the first learning phase (the first three sessions) and the second phase (the last three sessions) in both groups.

The mean of questions number (MQ)

We calculated the number of questions asked by the learner to the trainer concerning video feedback in each session. Next, we calculated the mean of questions number in the first (the first three sessions) and the second learning phases (the last three sessions) in the three groups.

Statistical analyses

All statistical analyses were performed using Statistica 10 software (StatSoft, Cracow, Poland). Data are presented as (i) means and standard errors (mean ± SE) for technical performance and (ii) means and standard deviation (mean ± SD) for the mean of question number (MQ) and the Mean of interactions number with video feedback viewing per practice phase (MI). G*power software was used to calculate the required sample size. Values for α a were set at 0.05 and power at 0.95. Based on a previous study of Souissi et al.² and discussions between the authors, effect size was estimated to be 0.63. The required sample size was 12 for each group. Distributions normality was checked using Shapiro–Wilks test.

Data of technical performance, the mean of question number (MQ) and the Mean of interactions number with video feedback viewing per practice phase (MI) were normally distributed. Therefore, a two-way ANOVA [3 Groups × 2 Times: pre and posttests] was performed for the technical performance and post hoc comparisons were made using the Bonferroni test. Likewise, a two-way ANOVA [3 Groups × 2 Times: the first three and the last three sessions] was performed for the mean of question number and post hoc comparisons were made using the Bonferroni test. In addition, a two-way ANOVA [2 Groups × 2 Times: the first three and the last three sessions] was performed for the MI number with video feedback viewing per practice phase and post hoc comparisons were made using the Bonferroni test.

Effect sizes were calculated as partial eta-squared (η_p²) to estimate the meaningfulness of significant findings. The effect sizes were calculated as Cohen‘s d.⁴⁰ Significance for all analyses was set at p < 0.05.

Results

Technical performance

Descriptive statistics presented as means (±SE) are summarized in Figure 4. At baseline (pretest), single-factor ANOVA revealed no significant difference between groups for age, height, weight, body mass index, and all kinematic variables (p > 0.05).

Figure 4.

Kinematic parameters (means ± standard error) analysis.

Analysis of variance ANOVA (3 Groups × 2 Times) of mixed model (see Figure 4) with repeated measurement of the second factor showed (i) a significant time effect for all kinematic parameters: Dx2 [F(1,36) = 31.66, p < .001, η² = .46], DxV [F(1,36) = 18.73, p < .001, η² = .34], DxL [F(1,36) = 19.04, p < .001, η² = .35], VTR [F(1,36) = 12.41, p < .01, η² = .27], DxT [F(1,36) = 16.01, p < .001, η² = .31], and Diff Tr [F(1,36) = 14.34, p < .001, η² = .28] and (ii) a significant group by time interaction effect for DxV with [F(2,36) = 3.97, p < .05, η² = .18] and for Diff Tr [F(2,36) = 3.26, p < .05, η² = .15].

For TC group, compared with pre-test_, the post hoc test showed lower values at posttest for Dx2 (Δ −18.17 ± 26.75%, d = .83), DxV (Δ −25.97 ± 18.02%, d = 1.52), DxL (Δ −19.98 ± 21.60%, d = 1.36), and Diff Tr (Δ −36.44 ± 25.66%, d = 1.18).

For PC group, a significant difference was found only between posttest versus pretest for the Dx2 (Δ −21.53 ± 20.40%, d = 0.86) with decreased values at posttest In addition, the post -hoc test showed significant lower value for the TC group compared with the CONT group at posttest for the DxV (p < 0.05).

The average number of interactions with video feedback viewing per practice phase (MI)

Analysis of variance (2 Groups × 2 Times) of mixed-model with repeated measurement of the second factor showed (i) a significant group by time interaction effect with [F(1,25) = 13.72, p < 0.01, η² = 0.35] and (ii) a significant time effect for the mean of interactions number with video feedback viewing: MI [F(1.25) = 19, p < 0.001, η² = 0.43] (Table 3).

Table 3.

Number of questions and interactions in relation to video feedback.

	TC		PC		CONT		ANOVA
	T_f	T_la	T_f	T_la	T_f	T_la	Time^a group (p)	Time (p)	Group (p)
MI	8.26 ± 2.27	5.74 ± 1.41^b	6.74 ± 1.93	6.54 ± 1.64	_	_	<0.01	<0.001
MQ	5.52 ± 1.95^c	3.71 ± 1.23^a	4.41 ± 1.61	4.28 ± 1.28	3.75 ± 0.91	4.08 ± .075	<0.01	<0.05

CONT: control group; MI: the number of interactions with video feedback; MQ: The mean of questions number; PC: partial control group; TC: total control group; T_f: mean of the first three sessions; T_la: mean of the last three sessions; P-values adjusted for multiple testing by the Holm–Bonferroni method. Data are reported as means (± standard deviation).

Significant difference is compared with T_f (p < 0.01).

Significant difference is compared with T_f (p < 0.001).

Significant difference is compared with CONT at T_f.

For TC group, compared with the first three sessions, the post hoc test showed lower values during the last three sessions for the mean of interactions with video feedback (Δ −27.94 ± 17.40, d = 1.33).

The average of question number (MQ)

The average of question number during the first and last three sessions is presented in Table 3. Analysis of variance (3 Groups × 2 Times) of mixed-conception with repeated measurement of the second factor showed a significant time effect for the mean of question number with [F(1,36) = 4.59, p < 0.05, η² = 0.11] and a significant group by time interaction effect with [F(2,36) = 6.99, p < 0.01, η² = 0.28].

For TC group, compared with the first three sessions, the post hoc test showed lower values for number of questions asked by learners during the last three sessions (Δ −28.54 ± 24.62, d = 1.11). In addition, the post hoc test showed significant higher values for the TC group compared with the CONT group at the first three sessions (p < 0.05, d = 1.16).

Discussion

The aim of the present study was to examine the efficacy of total control of the video feedback viewing (TC) in weightlifting learning and to compare its relative effectiveness with PC strategy and with a control condition without interactivity. After six practice sessions, despite each of the groups were combined with an expert model as well as verbal instructions on how to correct errors, findings provided evidence that the TC strategy was more beneficial for improving most of the snatch technical parameters than the PC strategy in youth beginners. Results of comparisons between the participant groups showed that the technical performance of TC group was better for DxV kinematic variable at posttest compared with CONT.

Regarding the horizontal (rearward) displacement of the bar in the first pull with respect to the starting position (Dx2), the TC and PC groups showed significant decreases of (−18.2%, −21.5%, respectively) at posttest The diminution of this displacement is an important indicator of the likelihood of success of the lift in snatch movement.^35,41 Obtained results are similar to those of Hasler et al.²⁸ who confirmed a positive effect of interactive video on learning a lesson in life science in primary school students. In addition, during this phase, the speed of the bar was low and represented around 69% to 71% of the speed of the second pull.^42–44 A possible reason for this improvement in the PC group is the fact that low-paced video feedback with pauses provides novice children with additional time to detect and analyze the errors compared to the information presented in the normal speed video.

Moreover, the findings of this study showed significant improvement as regards the horizontal displacement of the bar between the first and the second pulls (DxV: −26%) and the horizontal displacement of the bar between the second pull and the catch position (DxL: −20%) only in the TC group at posttest. The results are similar to those reported by Schwan and Riempp²⁵ who showed that total control of the video sequence improves the learning of complex skills. One possible reason for this improvement is that participants of the TC group made intensive use of the interactive features and the instructional activity during the first learning phase. In addition, the TC group asked a higher number of questions and received more verbal feedback (instructions on how to properly correct their movement) compared to CONT group in the first three sessions. Hence, it is possible that the verbal instruction contributes to improving technical performance. Indeed, the intensive use of features (pause, forward or backward) in the video display facilitates the understanding and detection of errors (with reference to the gesture performed and the expert model). Also, the privilege of rewinding the video feedback could have a played a key role in the learning process. In addition, the use of clarification questions concerning the information transmitted by the video feedback decreases cognitive effort and increases the rate of corrections of gestural errors during posttest.² During the second learning phase, the use of interactive features as well as the number of questions asked by the learners was decreased by 27.9% and 28.5%, respectively. The learners have already detected the errors and corrected them by encoding and retrieving the correct information.⁴⁵ This consolidation of information thus strengthens the construction of higher-level schema for the TC group.

In this study, it is interesting to note that only the TC group improved the symmetry of trajectory between the right and left sides of the barbell (−36.4%). Indeed, the TC strategy improves the intra-limb coordination. In addition, it can be used to guide the actions of novice learners who find it difficult to interpret intrinsic feedback and/or who have less stable movement patterns. It provides learners with new intrinsic feedback and stimulates the functions of perceptive categorization and the conceptual and symbolic elaboration of the received information, therefore improving the athlete‘s error detection capability.

Previous studies, using functional magnetic resonance imaging, suggested that cingulate motor area in particular is involved in error detection.⁴⁶ The action of this system is significantly improved according to the error correction strategy.³⁶ In TC strategy, the mental comparison process between the video feedback and the video demonstration would be expected to yield a signal. The amplitude of this signal depends on the degree to which the two representations differ.³⁶

The total control of video feedback viewing allows therefore the learner to better understand what to avoid, thereby enhancing the correction of the motor errors. In contrast, a feedback video without learner control does not lead to a significant improvement in the learning of complex gestures. On the other hand, the regulation of complex skills can easily overload the cognitive system of learners, because it requires deliberate attention to one‘s behavior to make a comparison with the successful gesture criteria.⁴⁷

To our knowledge, this is one of the first studies to use two different control strategies of video feedback viewing to correct technical errors during motor learning in sport. This study demonstrates the effectiveness of learners’ intervention and compares the relative effectiveness of TC as compared to PC in a complex skill that depends on several degrees of freedom, namely weightlifting snatch. However, some limitations are noteworthy. First, although the horizontal and vertical displacement of the barbell is a valid and accurate measurable parameter for snatch performance, other variables such as the speed of the bar should be assessed in order to measure the finer intricacies of improved performance. Second, the durability of learning was not measured through a retention test at later times. Third, the lack of measurement in the number of times TC group went back and forth during the viewing of their video feedback needs further attention. Specifically, it should be followed up with the potential effects of this on the results. It is possible that the TC group viewed themselves doing their squat more times (rewind – play, rewind-play, etc.) than the other groups and, thus, the number of viewings may have influenced the results. Future research should consider adding a yoked group to such a study in order to confirm whether the viewingsnumber factor can influence the learning process. In addition, a retention and transfer test can be implemented to add further reliability to the findings. Another avenue for future research is to investigate the impact of implementing peer-assisted learning methods in similar learning settings (i.e., learning through video feedback and self-correction).

The results obtained with the TC may potentially influence the teaching and coaching methods in the future. The active participation of the learner in the learning process certainly improves the acquisition of complex skills. The findings have potential significance for PE teaching because, while practice is obligatory for improving learning, the efficiency of the learning process is fundamental in guaranteeing a successful long-term accuracy. In fact, the misuse of technological tools may limit the learning rate, as it could inhibit the learner‘s ability or willingness to explore and exploit information available in the learning environment. By illustrating how technology may complement the learner‘s learning under the guidance of the theoretical framework of ecological dynamics, it is intended that coaches and PE teachers may gain a better understanding of how technological tools can be used more strategically to enhance complex movements learning in young children.

In summary, the findings of this study showed that the TC strategy was more beneficial for self-evaluation and the technique error correction during the learning of snatch than PC in youth learning the weightlifting snatch technique.

Footnotes

Acknowledgments

The research team would like to thank the students and the teaching staff who generously shared their time, experience, and materials for the purposes of this work.

Declaration of conflicting interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding statement

This research received no specific grant from any funding agency in the public, commercial, or non-profit sectors.

ORCID iD

Mohamed Abdelkader Souissi

References

Kok

Komen

Capelleveen

, et al. The effects of self-controlled video feedback on motor learning and self-efficacy in a physical education setting: an exploratory study on the shot-put. Phys Educ Sport Pedagogy 2020; 25: 49–66.

Souissi

Elghoul

Souissi

, et al. The effects of three correction strategies of errors on the snatch technique in 10-12-year-old children: a randomized controlled trial. J Strength Cond Res. Epub ahead of print, 2020. DOI:10.1519/JSC.0000000000003707

Hiyamizu

Maeoka

Matsuo

, et al. Effects of self-action observation on standing balance learning: a change of brain activity detected using functional near-infrared spectroscopy. NeuroRehabilitation 2014; 35: 579–585.

Boyer

Miltenberger

Batsche

, et al. Video modeling by experts with video feedback to enhance gymnastics skills. J Appl Behav Anal 2009; 42: 855–860.

Aiken

Fairbrother

Post

. The effects of self-controlled video feedback on the learning of the basketball set shot. Front Psychol 2012; 3: 338.

Souissi

Elghoul

, et al. Information processing and technical knowledge contribute to self-controlled video feedback for children learning the snatch movement in weightlifting. Percept Mot Skills 2021; 128: 1785–1805.

Andrieux

Danna

Thon

. Self-control of task difficulty during training enhances motor learning of a complex coincidence-anticipation task. Res Q Exerc Sport 2012; 83: 27–35.

Carter

Carlsen

Ste-Marie

. Self-controlled feedback is effective if it is based on the learner‘s performance: a replication and extension of Chiviacowsky and Wulf (2005). Front Psychol 2014; 5: 1325.

Ste-Marie

Carter

Law

, et al. Self-controlled learning benefits: exploring contributions of self-efficacy and intrinsic motivation via path analysis. J Sports Sci 2015; 34: 1–7.

10.

Ste-Marie

Vertes

Law

, et al. Learner-controlled self-observation is advantageous for motor skill acquisition. Front Psychol 2013; 3: 56.

11.

Post

Aiken

Laughlin

, et al. Self-control over combined video feedback and modeling facilitates motor learning. Hum Mov Sci 2016; 47: 49–59.

12.

Bund

Wiemeyer

. Self-controlled learning of a complex motor skill: effects of the learners’ preferences on performance and self-efficacy. J Hum Mov Stud 2004; 47: 215–236.

13.

Wulf

Raupach

Pfeiffer

. Self-controlled observational practice enhances learning. Res Q Exerc Sport 2005; 76: 107–111.

14.

Grand

Bruzi

Dyke

, et al. Why self-controlled feedback enhances motor learning: answers from electroencephalography and indices of motivation. Hum Mov Sci 2015; 43: 23–32.

15.

Chiviacowsky

Wulf

Ávila

LTG

. An external focus of attention enhances motor learning in children with intellectual disabilities: focus of attention and intellectual disability. J Intellect Disabil Res 2013; 57: 627–634.

16.

Wulf

. Self-controlled practice enhances motor learning: implications for physiotherapy. Physiotherapy 2007; 93: 96–101.

17.

Chen

Hendrick

Lidor

. Enhancing self-controlled learning environments: the use of self-regulated feedback information. J Hum Mov Stud 2002; 43: 69–86.

18.

Post

Fairbrother

Barros

JAC

, et al. Self-controlled practice within a fixed time period facilitates the learning of a basketball set shot. J Mot Learn Dev 2014; 2: 9–15.

19.

Carter

Ste-Marie

. An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules. Psychol Res 2017; 81: 399–406.

20.

Post

Fairbrother

Barros

JAC

. Self-controlled amount of practice benefits learning of a motor skill. Res Q Exerc Sport 2011; 82: 474–481.

21.

Mayer

Moreno

. Nine ways to reduce cognitive load in multimedia learning. Educ Psychol 2003; 38: 43–52.

22.

Hegarty

Kriz

Cate

. The roles of mental animations and external animations in understanding mechanical systems. Cogn Instr 2003; 21: 325–360. JSTOR.

23.

Leahy

Sweller

. Cognitive load theory, modality of presentation and the transient information effect. Appl Cogn Psychol 2011; 25: 943–951.

24.

Lusk

Evans

Jeffrey

, et al. Multimedia learning and individual differences: mediating the effects of working memory capacity with segmentation. Br J Educ Technol 2009; 40: 636–651.

25.

Schwan

Riempp

. The cognitive benefits of interactive videos: learning to tie nautical knots. Learn Instr 2004; 14: 293–305.

26.

Wouters

Paas

van Merriënboer

JJG

. How to optimize learning from animated models: a review of guidelines based on cognitive load. Rev Educ Res 2008; 78: 645–675.

27.

Mayer

Hegarty

Mayer

, et al. When static media promotes active learning: annotated illustrations versus narrated animations in multimedia instruction. J Exp Psychol Appl 2005; 11: 256–265.

28.

Hasler

Kersten

Sweller

. Learner control, cognitive load and instructional animation. Appl Cogn Psychol 2007; 21: 713–729.

29.

Spanjers

Van Gog

van Merriënboer

. A theoretical analysis of how segmentation of dynamic visualizations optimizes students’ learning. Educ Psychol Rev 2010; 22: 411–423.

30.

Cheon

Crooks

Chung

. Does segmenting principle counteract modality principle in instructional animation? Br J Educ Technol 2014; 45: 56–64.

31.

Seels

Berry

Fullerton

, et al. Research on learning from television. In: Jonassen

(ed) Handbook of research for educational communications and technology. New York: Macmillan, 1996, pp.299–377.

32.

Manna

. Growth development and maturity in children and adolescent: relation to sports and physical activity. Am J Sports Med 2014; 2: 48–50.

33.

McPherson

. A systematic approach to skill analysis. Sci Period Res Technol Sport 1990; 11: 1–10.

34.

Bartlett

. Introduction to sports biomechanics: analysing human movement patterns. 2nd ed. London, New York, NY: Routledge, 2007.

35.

Hoover

Carlson

Christensen

, et al. Biomechanical analysis of women weightlifters during the snatch. J Strength Cond Res 2006; 20: 627–633.

36.

Milanese

Cavedon

Corte

, et al. The effects of two different correction strategies on the snatch technique in weightlifting. J Sports Sci 2016; 35: –8.

37.

Ammar

Riemann

Masmoudi

, et al. Kinetic and kinematic patterns during high intensity olympic clean movement: searching for optimal load. J Sport Sci 2017; 36: 1319–1330.

38.

Ammar

Riemann

Trabelsi

, et al. Comparison of 2- and 3-minute inter-repetition rest periods on 3D kinematic and kinetic parameters during successive maximal clean and jerk movements. Res. Q. Exerc Sport 2019; 90: 287–296.

39.

Stone

Pierce

Sands

, et al. Weightlifting: a brief overview. Strength Cond J 2006; 28: 50–66.

40.

Hopkins

Marshall

Batterham

, et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41: 3–13.

41.

Stone

O‘Bryant

Williams

, et al. Analysis of bar paths during the snatch in elite male weightlifters. Strength Cond J 1998; 20: 30–38.

42.

Campos

Poletaev

Cuesta

, et al. Kinematical analysis of the snatch in elite male junior weightlifters of different weight categories. J Strength Cond Res 2006; 20: 843–850.

43.

Gourgoulis

Aggeloussis

Antoniou

, et al. Comparative 3-dimensional kinematic analysis of the snatch technique in elite male and female Greek weightlifters. J Strength Cond Res 2002; 16: 359–366.

44.

Lorenzen

Wilson

, et al. Reviewing current knowledge in snatch performance and technique: the need for future directions in applied research. J Strength Cond Res 2014; 28: 574–586.

45.

Hinsz

Tindale

Vollrath

. The emerging conceptualization of groups as information processors. Psychol Bull 1997; 121: 43–64.

46.

Kiehl

Liddle

Hopfinger

. Error processing and the rostral anterior cingulate: an event-related fMRI study. Psychophysiology 2000; 37: 216–223.

47.

Wouters

Tabbers

Paas

. Interactivity in video-based models. Educ Psychol Rev 2007; 19: 327–342.