Introduction

Sailing competitions have been an integral part of every Olympic Games since 1908. This prominence of sailing as an Olympic discipline underscores its complexity and the high level of technical skill required. For young athletes, learning these skills early is critical, as their cognitive and motor abilities are highly adaptable during developmental years, making it an ideal time to establish foundational skills in complex sports1,2. Navigating a sailboat demands a set of specific and complex technical skills for optimal performance. Sailing involves numerous performance parameters, including the ability to understand and anticipate weather conditions, optimal equipment usage, and both technical and tactical proficiency3,4. Previous studies have emphasized that the acquisition of complex technical skills in sports typically begins between the ages of nine and thirteen1,2. During this crucial period, it is essential for young athletes to master techniques correctly, as inadequately acquired skills are challenging to rectify later and may result in higher costs5. Researchers in motor learning and control extensively investigate teaching and learning conditions to find optimal strategies for significant improvements in motor and cognitive skill acquisition6,7. One notably effective condition is the integration of new technology into the teaching and learning process7,8,9,10. Among widely adopted techno-pedagogical tools in physical education and training, video demonstrations and video feedback play a crucial role. Video demonstrations, illustrating motor actions performed by an expert, offer a comprehensive visual representation of success criteria11. This approach ensures a better mental understanding of the required action for learners, surpassing other demonstration methods12,13. Furthermore, a study conducted by Gapin and Herzog14 on video imagery in sailing showed significant improvements in visual and kinesthetic imagery abilities among competitive college sailors. This approach, compared to traditional imagery, offers similar benefits, highlighting the value of integrating video into motor learning in sailing. Video not only enhances the observation of movements but also develops essential cognitive skills for mastering actions in navigation. Similarly, video feedback allows learners to observe their movements, facilitating the detection of errors and providing an opportunity to correct them in subsequent attempts. This form of feedback has demonstrated significantly superior results compared to other feedback types7,15. However, Tarnas, et al.16 revealed that auditory feedback provided through a smartphone application significantly assisted the tactical decision-making of windsurfers and enhanced skill acquisition during sailing training. Advanced research has also explored the integration of technological tools in blended learning, combining face-to-face and online learning17,18,19,20.

Previous studies have revealed that the blended learning approach is more effective than traditional learning methods in teaching the long jump18 and the javelin throw technique19 to primary school students. Furthermore, Bokau and Rima21 demonstrated that blended learning, combined with an online learning management system, significantly enhances the interest and motivation of students in maritime training, thereby promoting increased engagement in the learning process. In addition, blended learning offers several advantages to learners during physical education sessions, such as the ability to work at their own pace, better control of their time, and meeting individual needs22. Learners can also strengthen cognitive processes23, mastery and application of knowledge24, engagement25, and promote active learning26. Additionally, Al Musawi and Ammar27 affirmed that using mixed learning methods, with varying proportions of online components (e.g., 25% online and 75% in-person; 50% online and 50% in-person; or 75% online and 25% in-person), enhances students’ understanding and reflection compared to the control group using conventional methods in teaching the ‘Introduction to Educational Technology’ course. On the other hand, Chang, et al.28 and Yick, et al.29 demonstrated that no difference was observed in test success scores and actual student grades, respectively, between blended learning and traditional learning groups.

However, a recent study by Kyriakidis, et al.19 compared different blended learning approaches. This study involved two experimental groups following a blended learning approach with a rotational component based on the Lab-Rotation model. Participants in both groups alternated between online learning (in a computer lab) and in-person learning (on a practice field). The results revealed that the group that viewed part of the lesson online based on video demonstrations in the computer lab, then performed exercises in the field with in-person video feedback, displayed superior performance compared to the group where participants watched part of the lesson online based on video demonstrations and then performed exercises taught in the field only. This study highlights the effectiveness of incorporating in-person video feedback alongside online instruction, suggesting that combining various learning modalities can enhance performance outcomes in physical education settings.

All these previously presented studies were conducted in quiet learning environments. However, learning to sail introduces multiple demands and challenges, especially for children, in a dynamic and often noisy environment such as the sea. In water sports, particularly sailing, the limitations on direct interaction arise from various environmental obstacles. Substantial distances between the instructor and the learner, often necessary for safety reasons, hinder direct and immediate communication of corrections. Ambient sounds like water lapping and wind in the sails, along with visual obstacles such as other vessels or waves, complicate the clear transmission of verbal instructions and real-time movement observation30. Additionally, changing weather conditions, including strong winds or rough waves, further add complexity to the learning process. In this demanding context, learners must maintain intense focus on navigation, making it difficult to receive and implement advice immediately30. Therefore, this study aims to explore technological solutions to enhance learning in these challenging conditions, specifically examining the effectiveness of blended learning strategies in teaching tactical sailing techniques.

While research has explored motor skill learning in changing environments and real conditions, few studies have focused specifically on the challenges associated with learning at sea, where factors such as water movement and noise variations significantly influence performance. This study aims to address this gap by examining the effectiveness of blended learning strategies in teaching tacking techniques during sailing. The objective of this study is to compare the effectiveness of two blended learning strategies, namely video feedback combined with online written instructions (BLIV) and online written instructions only (BLI), and their effects on improving tacking technique during sailing with sailboats in a natural environment (sea) among students aged 11–13. This research is grounded in the existing literature that highlights the advantages of video feedback in motor skill learning, particularly in complex environments. While video-based learning is a well-established technique in sports education, its application in sailing, a sport with complex motor and perceptual demands, remains underexplored. This novelty highlights the unique contribution of our research in examining blended learning strategies in a maritime context. We hypothesize that adopting a blended learning strategy based on video feedback combined with online written instructions (BLIV) would be more suitable for correcting technical errors in tacking, a critical skill in sailing, than the BLI strategy during the post-test. Additionally, it was expected that the BLIV group would reduce its execution time for the tacking exercise and increase its theoretical knowledge level during the post-test. By investigating these strategies, this study aims to contribute valuable insights into the application of blended learning in real-world maritime education contexts.

Methods

Participants

A total of 31 school-age children, including 12 girls and 19 boys, voluntarily participated in the current study. All participants had undergone one month of sailing training in Optimists at a promotion center affiliated with the (blinded). Prior to their inclusion in this study, parents provided written informed consent for their children’s participation. Furthermore, the study was conducted in accordance with the principles outlined in the 2013 Helsinki Declaration. The experimental protocol received approval from the local Research Ethics Committee (CPP SUD: N° 0551/2023).

Participants were selected based on the following inclusion criteria: (i) They had to be between 11 and 13 years old, (ii) no history of musculoskeletal or orthopedic disorders, injuries, or surgeries in the six months preceding the tests that could have affected physical abilities, (iii) no visual problems, (iv) proficiency in swimming, demonstrating proven skills in this sport, and (v) were required to regularly attend sailing training sessions in Optimist upon their entry into the training center.

The exclusion criteria included: (i) additional sailing experience beyond the one month of training provided in this study, (ii) health conditions that could limit physical participation, such as uncontrolled severe asthma or cardiac disorders, (iii) significant cognitive or learning disabilities that could hinder the ability to understand and follow instructions, and (iv) a history of trauma related to aquatic activities or a fear of water.

A post hoc power analysis was conducted using the G*Power software31 for the main ANOVA split-plot design (three groups, two measurements). Using an effect size of f = 0.25, which aligns with commonly accepted benchmarks in social sciences research32, and setting alpha at 0.05 with sphericity assumed (ϵ = 1), the analysis indicated that the sample size of 31 had a power of 0.84. This surpasses the conventional 0.80 threshold, confirming that the study has sufficient power to detect a medium effect size.

After the completion of the pre-test, participants were randomly assigned to one of the three following groups: Blended Learning Group based on video feedback combined with online written instructions (BLIV = 11), Blended Learning Group based on online written instructions only (BLI = 10), and Control Group (CONT = 10). Randomization was performed using computer-generated random numbers to ensure unbiased allocation of participants to the groups. This method was implemented to ensure a balanced distribution of participant characteristics across the groups and to reduce potential biases in the study. At baseline (T0), a within-group comparison revealed no significant differences between the BLIV, BLI, and CONT groups in terms of age, height, and weight. Additional details are available in Table 1.

Table 1 Descriptive characteristics of the participants.
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Task and material

The task to be performed was the tacking maneuver, a fundamental maneuver carried out when sailing an Optimist. It involves a change in the boat’s direction when the wind shifts from one side to the other. To execute a tacking maneuver, a dinghy must turn the boat sharply into the wind from a close-hauled position, so that it momentarily points directly into the wind with the sail(s) luffing, then continues until the sail(s) fill again with wind from the opposite side. The process involves turning the boat into the wind until the wind blows on the other side of the sail and requires the athletes to change the boat’s course to maintain balance4. An optimal performance must meet al.l the success criteria and be completed within a short time frame. It is a complex and fundamental maneuver in the context of sailing, requiring both technical skill and a keen understanding of the dynamics of the marine environment, particularly the interactions between wind and water33, Fig. 1.

Fig. 1
figure 1

Tacking maneuver of an optimist.

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The equipment used for this study comprised eleven single-handed dinghies of the Optimist type, each equipped with a single sail. The technical features of this boat directly impact its performance and behavior during maneuvers at sea. The Optimist stands out with its rectangular-shaped hull made of reinforced polyester resin and fiberglass, with a width of 1.20 m, a length of 2.18 m, and a total weight of 35 kg. The sail, with a trapezoidal shape, includes the tack, clew, and halyard attachment points, along with a fourth point held by a vang. An Optimist vang is a pole just over 2 m long, with one end attached to the sail and the other to the middle of the mast at a height of 2.26 m. The sail area is 3.25 square meters. The choice of this particular dinghy is justified by its popularity as a learning platform for sailing and its suitability for this age group.

A 4-meter-long Zodiac equipped with a 9.9-horsepower outboard motor, carrying a captain guiding the boat and a coach overseeing the sessions. The coach intervenes (i) to correct incorrect movements through verbal feedback or (ii) in case of emergencies.

Eleven video cameras ([GoPro HERO8 Black] HD: 60 frames per second), each mounted on the lower part of the Optimist mast and facing the student, were used to record their technical movements during learning and testing sessions. This positioning allows the camera to capture the entire area of the tacking action. The recorded video of each student in the BLIV group was sent individually to them via the Messenger Kids app after each learning session. Twenty life jackets were worn by participants before the start of each session as a safety measure during navigation.

Procedure

Participants underwent test sessions to assess technical performance, execution time of the tacking maneuver, and the level of theoretical knowledge one day before (T0) and one day after (T1) a four-session tacking motor learning period. All test sessions were scheduled in the morning, between 10 a.m. and 11 a.m., when the water current speed ranged from 0 to 0.2 knots, and the wind speed was 10 knots from the north. Water current speed was measured using the Acoustic Doppler Current Profiler (ADCP WorkHorse Monitor 1200 kHz RDI TeledyneTM) (Epler, 2010), while wind speed was measured by the Global Forecast System, a worldwide meteorological forecasting model developed by the National Weather Service34.

Subjects in each group followed a tacking learning program consisting of two sessions per week over a two-week period. The duration and structure of each learning session were established in accordance with the recommendations of professional sailing coaches, who confirmed that this time frame was sufficient to adequately learn the tacking maneuver, considering the participants’ prior skills in basic sailing techniques. Learning sessions were planned based on weather forecasts for the Tunis region obtained from the Windfinder website. Sessions took place on days when the wind speed was in the range of 8 to 12 knots. Each learning session in this study comprised two blocks of ten repetitions.

Throughout all learning sessions, participants maintained their regular training activities. In this context, a professional sailing coach defined success criteria and was involved in qualitative analysis and verbal correction of incorrect movements. Additionally, this coach was not informed of the participants’ assignment to the groups. To ensure consistency across learning sessions, the coach used standardized phrases and instructions for all participants, including key directives during the maneuver. Each participant, regardless of the group, received verbal feedback after three attempts of the maneuver in each block.

The day of the pre-test and post-test, none of the participants had access to techno-pedagogical video assistance or pedagogical guidance. During each test session, all participants performed three attempts, being simply instructed to ‘do their best.’ After completing the tacking maneuver, each child completed the theoretical knowledge level test.

Learning sessions Before the start of each learning session, participants from each group gather in a room to watch a video demonstration of the tacking maneuver exercise displayed on a 42-inch Sony television screen. This video includes verbal instructions (audiovisual sequence). Subsequently, students move to the beach where they engage in a general warm-up for the lower and upper limbs, as well as a specific warm-up involving preparatory exercises tailored to the session. During these learning sessions, the operating modes of the groups were as follows:

Control Group (CONT): Receives only simultaneous verbal corrections from the coach while practicing the tacking maneuver exercise.

Blended Learning Group based on Written Instructions (BLI): Similar to the control group, also benefits from an online written instruction sheet containing a technical description of the movements to be executed. Each participant is responsible for carefully reading these instructions sent via the ‘messenger kids’ application at home. These written instructions were sent to the participants by the researcher after each learning session. Participants in this group were asked to read these instructions, focusing on key technical aspects such as body positioning and sail handling. Each participant in this group had the opportunity to consult the written instructions at any time as needed. This approach aims to deepen individual understanding of the movements online and enhance participants’ engagement in the learning process.

Blended Learning Group based on Video Feedback and Written Instructions (BLIV): Similar to the BLI group; however, each participant receives individual video feedback along with online written instructions via the ‘Messenger Kids’ application after each learning session to identify technical errors and correct their mental representation of the action. Participants in this group were asked to analyze their video feedback, focusing on key technical aspects such as body positioning and sail handling. Each participant in this group had the opportunity to review the video feedback multiple times at their own pace, using all the video display control buttons. They could also verify the presence of the success criteria described in the paper by reading them and checking them against the video. The video feedback and written instructions were sent to the participants by the researcher.

Test sessions During the test sessions, each participant is required to navigate a loop between two buoys, sailing in a zigzag pattern with three tacks to maneuver around the windward buoy. Subsequently, they must undergo a computer-based test, error detection test based on viewing a video showing a novice child performing the tacking maneuver, to assess their level of theoretical knowledge.

Data collection

The technical performance score The collected video sequences are processed to determine the technical performance score of each participant in the three groups. The final technical performance score for the tacking maneuver task during each test session was established by calculating the average of the scores of three tacks. Each tack score was determined by summing up the points awarded based on criteria for successful execution of the maneuver. Each criterion was scored as follows: 0 points if the criterion was not met, indicating that the execution was incorrect or incomplete to a degree that compromised the success of the maneuver; 0.5 points if the criterion was partially met, with certain aspects technically aligned but errors present that partially affected the quality of the maneuver; and 1.5 points if the criterion was fully met, with the execution being correct and complete, fully meeting technical requirements and contributing positively to the maneuver’s success. Consequently, each participant was assigned a score ranging from 0 to 9 points, reflecting their performance. The technical criteria covered the following aspects:

  1. (a)

    Leaning the boat slightly to the windward side by pushing the tiller (lofer: pushing the tiller towards the sail) to the wind.

  2. (b)

    As soon as the boat turns to face the wind, placing the rear foot on the opposite side (windward side).

  3. (c)

    Then, as the boat begins to turn, quickly crossing the boat by moving under the boom to the side opposite the sail.

  4. (d)

    Sitting on the opposite side of the boat and retrieving the sheet from the other side to trim the sail.

  5. (e)

    Pushing the tiller towards the mast in the new direction (adjusting the course again) to straighten the boat.

  6. (f)

    Adjusting the sail on the opposite side and regaining speed once the maneuver is completed.

These evaluation criteria were defined based on established research in motor learning and sailing didactics, as well as recommendations from experts in sport navigation, incorporating the critical phases of the movement and motor actions that optimize performance in this maneuver. In the assessment, three expert instructors in tacking maneuvers participated in evaluating the participants’ movements. The evaluators involved in the scoring procedure were indeed blinded to the participants’ group assignments. Each evaluator independently and individually analyzed the video sequences of each participant, allowing for multiple viewings and the option to use slow-motion playback as needed. They had access to all video display control buttons, including pause, play, fast forward, and rewind. This method was adopted based on the findings of a recent study conducted by Souissi, et al.9, highlighting that the use of these control buttons enhances error detection in observed movements. To strengthen the reliability of the scoring process, the intraclass correlation coefficient (ICC) was calculated to assess the consistency between the evaluations of the three instructors before any consensus discussions. The ICC obtained in this study was 0.89, reflecting very good inter-rater reliability. This coefficient confirmed that the initial divergences between the judges were minimal. In cases of inconsistency, the three experts proceeded to a collective review of the videos to reach a consensual agreement, as mentioned in the description of the scoring process. This method ensures that scoring discrepancies were resolved collaboratively and that each participant received a reliable and objective assessment.

Execution time of tacking The execution time of the tacking maneuver was measured using a GoPro HERO8 Black video camera at 60 frames per second. The initial time corresponds to the moment when the tiller turns backward, and the final time is when the rudder returns to its initial position. The measurement of execution time was chosen as a performance indicator to evaluate the participants’ responsiveness and fluidity in performing this complex maneuver. In sailing, a quick execution is crucial for adapting to variations in the marine environment, but it is essential that it is accompanied by precision to avoid excessive deviation from the trajectory or imbalance of the boat.

Theoretical knowledge level A video sequence featuring a beginner sailor performing a tacking maneuver was presented to all learners. This video sequence included seven technical errors, previously identified by three sailing coaches. During the pre-test, each learner watched the sequence at 30% of real speed using the Kinovea software. They were allowed to pause the video to detect the maximum number of errors. Each learner had the opportunity to make seven attempts to identify errors in the sequence, and each reported error was recorded by the coach. The score for theoretical knowledge for each learner was assessed from 0 to 7, assigning 1 point for each detected error and 0 points for each declaration of a non-existent error. It is important to clarify that if a learner reported a non-existent error, it did not negatively affect their score. On the contrary, each incorrectly reported error was simply considered an incorrect attempt and did not result in a penalty to the final score. The same sequence was replayed during the post-test.

Statistical analyses

The statistical analyses were conducted utilizing Statistica 10 software (StatSoft, Krakow, Poland), with a significance level set at p ≤ 0.05. The normality of the data was checked using the Shapiro-Wilk test. All variables met the normality assumption with p > 0.05, indicating no need for data transformation. Baseline data (T0) of descriptive characteristics of the participants, scores of the technical performance of the tacking maneuver, execution time, and theoretical knowledge level were compared using a one-way analysis of variance (ANOVA).

To examine the technical performance score, execution time, and the theoretical knowledge level, we conducted repeated measures Analysis of Variance (ANOVA) with three groups (BLIV, BLI, CONT) and two test periods (pre-test and post-test). The significance of the observed results was evaluated by calculating the effect size in the form of partial eta squared (ηp2). To further ensure the reliability of our findings, we assessed homogeneity of variances across groups using Levene’s test, which confirmed that the assumption of homogeneity was met (p> 0.05). This supports the validity of comparisons between groups under the ANOVA framework. Subsequently, post-hoc tests were performed using the Bonferroni post-hoc test to assess the significance of mean differences. Regarding effect sizes, they were estimated as Hedge’s g when relevant, with g values ≥ 0.2 indicating a small effect size, ≥ 0.5 indicating a moderate effect size, and ≥ 0.8 indicating a substantial effect size, following Cohen’s classification35.

Results

The technical performance score, execution time, and theoretical knowledge level data during test sessions are presented in Tables 2 and 3, and 4, respectively, as mean (± SE).

Table 2 Evolution of tacking technical scores over time within the three groups.
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Table 3 Evolution of the execution time of the tacking task over test sessions within the three groups.
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Table 4 Evolution of theoretical knowledge level over time in the three groups.
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At the pre-test (T0), within-group comparisons showed no significant differences between the BLIV, BLI, and CONT groups for weight, height, and age. Furthermore, no significant differences were found for the technical performance score, execution time, as well as the technical knowledge level (p > 0.05).

The technical performance scores

Regarding the technical performance of the tacking maneuver, the analysis of variance (group × time) showed (i) a significant Time effect [F(1,28) = 38.83, p < 0.001, ηp2 = 0.58], (ii) a significant Group effect [F(2,28) = 10.91, p < 0.001, ηp2 = 0.44], and (iii) a significant interaction effect (group*time) [F(2,28) = 7.81, p < 0.01, ηp2 = 0.36].

The post-hoc test showed a significant improvement in technical performance in the BLIV group after four learning sessions [T1 vs. T0: p < 0.001, Hedge’s g = 2.71], indicating a large effect size and suggesting both statistical and practical significance in the enhancement achieved through this approach. Furthermore, post-hoc analysis revealed that the BLIV group had significantly higher technical scores at the post-test (T1) compared to both the (i) BLI group (p < 0.01; Hedge’s g = 1.47, large effect size) and (ii) CONT group (p < 0.001; Hedge’s g = 2.58, large effect size), with large effect sizes in both comparisons, highlighting the substantial benefits of integrating video feedback with written instructions over the other groups (see Table 2).

Execution time

Regarding the execution time of the tacking task, the repeated measures ANOVA (3 groups × 2 times) on the last factor showed (i) a significant Time effect [F(1,28) = 12.66; p < 0.01; ηp2 = 0.31], (ii) a non-significant Group effect [F(2,28) = 2.16; p > 0.05], and (iii) a non-significant Time × Group interaction effect [F(1,28) = 3.25; p > 0.05].

The post-hoc test revealed that only the BLIV group achieved a significant decrease in execution time during the post-test (p < 0.01; Hedge’s g = 1.14, large effect size). Regarding the difference between groups, post-hoc testing did not indicate any significant differences during the post-test (see Table 3). Additionally, a correlation analysis between technical performance and execution time revealed a significant correlation (r = 0.71, p = 0.014), indicating that the reduction in execution time may be related to an improvement in technical performance rather than merely increased familiarity with the task.

Theoretical knowledge level

Regarding the level of theoretical knowledge, the repeated measures ANOVA (3 groups × 2 times) on the last factor showed (i) a significant Time effect [F(1,28) = 28.74; p < 0.001; ηp2 = 0.51], (ii) a non-significant Group effect [F(2,28) = 0.91; p > 0.05], and (iii) a non-significant Time × Group interaction effect [F(1,28) = 2.89; p > 0.05].

The post-hoc test revealed that only the BLIV group achieved an increase in the theoretical knowledge level during the post-test (p < 0.001; Hedge’s g = 1.98, large effect size). Regarding the difference between groups, post-hoc testing did not indicate any significant differences during the post-test (see Table 4).

Furthermore, a significant positive correlation was found between the improvement in theoretical knowledge (error detection ability) and practical performance (r = 0.798, p = 0.003). This suggests that enhancements in theoretical understanding may be strongly associated with technical performance improvements.

These findings indicate that while the BLIV group improved significantly in theoretical knowledge, there was no statistically significant difference in knowledge improvements between the groups BLI and CONT. The significant correlation between theoretical knowledge and practical performance highlights the potential role of cognitive understanding in enhancing motor skills.

Discussion

The aim of this study was to evaluate the effectiveness of a blended learning strategy using video feedback associated with online written instructions (BLIV) in the context of tacking maneuvers and to compare this effectiveness with that of a blended learning strategy based on online written instructions alone (BLI), as well as a control condition with no online activity, in children aged 11 to 13 years. After four learning sessions, the results confirmed the hypothesis that the BLIV strategy could offer more pronounced benefits in terms of improving the technical performance of tacking, as well as the execution time and theoretical knowledge level, compared to the BLI method. Furthermore, the comparison between groups revealed that at the post-test, the technical performance of the group using the BLIV strategy was superior to that of the control group (CONT) as well as the BLI group.

Regarding the control group, which watched a video demonstration by an expert at the beginning of the session, with integrated verbal corrections throughout the face-to-face session, our results showed a slight, non-significant improvement at the post-test after four learning sessions. Previous research suggests that the delayed combination of visual demonstrations and verbal feedback promotes the detection and correction of technical aspects in motor action7,36 in quiet learning environments. However, in our case, the noise produced by the engine and the sail, as well as the distance between the coach and the athlete, could hinder verbal interaction between them, thereby disrupting the quality of motor learning.

Regarding the BLI group, the students’ technical performance showed a statistically non-significant improvement in the post-test. Indeed, this result aligns with studies conducted by Chang, et al.28 and Yick, et al.29, which demonstrated no significant differences in the actual grades of students who used blended learning modules compared to students in the control group. One possible reason for this outcome is that novice children may not possess highly developed cognitive structures, like experts, to comprehend and assimilate written instructions (success criteria) and visualize them in real-life situations37.

However, as highlighted in the results section, the BLIV group demonstrated a more significant improvement in the technical performance score of tacking at T1. Indeed, our findings align with previous studies that have confirmed the effectiveness of the blended learning approach in acquiring complex motor skills, whether in long jump18 and ball throwing19 in children, or in volleyball38 in adults. According to these earlier studies, the use of new technologies in teaching complex physical activities, within a blended learning environment, has led to significant improvements in the technical execution of movements compared to traditional teaching methods.

In fact, the addition of online video feedback has enhanced students’ learning outcomes in terms of the tacking score. Video feedback allows learners to review their performances multiple times. Moreover, the use of video feedback content control buttons (e.g., pause, play, forward, and rewind) enables learners to pace the information dissemination according to their own rhythm9, leading to a reduction in mental effort39. Additionally, this visual control enables learners to detect and correct technical errors present in their execution of the tacking maneuver.

However, previous studies have indicated that novices are not able to detect all technical errors from watching video feedback alone40. Hence, the presence of online written instructions, combined with online video feedback, directs the young athlete’s attention to check for the presence of these success criteria in their movement in the absence of the teacher. This comparison facilitates the identification of the gap between their current performance and what they should be doing. This gap represents the technical errors that need to be corrected later to adjust their motor program and reduce variability between repetitions of the tacking maneuver.

However, in conjunction with this improvement in the technical execution of the tacking maneuver, our results also revealed a decrease in execution time during the post-test. This finding aligns with that of Souissi, et al.7, who demonstrated that the correct execution of complex movements in a shorter timeframe indicates the transition from the associative stage to the automatic stage of motor learning, where movements are performed unconsciously41. Indeed, Ferrel-Chapus and Tahej42 noted that intentional cognitive control decreases, and mental effort diminishes with the automation of movements at this phase.

Although our results show an improvement in technical performance and a reduction in execution time, their practical significance in real-world navigation situations requires further analysis. Indeed, at sea, environmental conditions (e.g., wind, waves) increase the difficulty of maneuvering. Therefore, the gains observed in a training setting could help athletes better adapt to fluctuating conditions, particularly by reducing errors in quick decision-making and real-time adjustments. Thus, the development of movement automation through the mixed learning program (BLIV) could lead to improved maneuvering efficiency, even in demanding maritime contexts.

The superior effectiveness of the BLIV group compared to the BLI and CONT groups may be explained by the visual feedback mechanism, which allowed children to correct specific technical errors. Indeed, the combined use of videos and written instructions promotes improved error detection through self-assessment, especially when the abstraction abilities of young learners are not yet fully developed. In traditional learning contexts, such corrections are typically guided by an instructor. However, the BLIV approach allowed participants to independently align with success criteria through an accessible visual model, enhancing the detection of discrepancies between actual and optimal execution. In contrast, the BLI and CONT groups, lacking this interactive visual support, showed limited improvement, suggesting that structured video feedback remains a key factor in the development of complex motor skills.

These results indicate that the use of a blended learning approach based on online video feedback associated with online written instructions represents an effective strategy for correcting complex movement techniques within a limited timeframe. The implementation of self-regulation in this approach helps learners optimize the use of intrinsic feedback, while stimulating different functions of perceptual categorization and symbolic and conceptual processing of received information. This approach thus contributes to improving learners’ ability to detect errors8. Significantly, the self-regulation strategy enhances the cognitive processing of errors and their correction, thereby facilitating the automation of correct movements with multiple degrees of freedom.

To our knowledge, this study is the first to explore two blended learning approaches to correct technical errors associated with a complex skill in a noisy environment where verbal communication is challenging. It aims to evaluate the relative effectiveness of the BLIV strategy compared to BLI in performing a complex skill, specifically tacking in sailing with Optimists. Nevertheless, a few limitations should be noted. First, while the technical score serves as a valid measure to assess performance, it is crucial to explore other determinant psychological variables, such as motivation and self-efficacy, to capture the nuances of performance improvement. Second, the short duration of the intervention (four sessions) limits the scope of conclusions regarding the progression of learning. A longer intervention would allow for the evaluation of the stability of performance gains, while retention tests after a certain period would provide additional data on the persistence of improvements. Third, this study did not evaluate the application of the skills acquired through the two blended learning approaches (BLIV and BLI) under more challenging environmental conditions, such as high wind speeds and strong currents, after the post-test, in more realistic and complex navigation scenarios. Fourth, the absence of measuring how often the BLIV group used various online video feedback content control buttons during viewing requires additional attention. In particular, it is essential to monitor potential effects on outcomes. The results obtained open the way for several future research directions. For example, studies could explore varying intervention durations to analyze the effect of frequency and repetition on skill automation in a navigation context. Additionally, expanding the sample size and integrating other learning technologies, such as interactive virtual simulations, could provide complementary perspectives to the blended learning approach for young athlete populations.

The results obtained with the blended learning approach (BLIV) could potentially influence teaching and coaching methods in the future. The adoption of video feedback combined with online written instructions in the learning process certainly enhances the acquisition of complex skills in a non-quiet learning environment, such as the sea in our case. The findings of this study have potential implications for physical education teaching and training in civil clubs because, while practice is mandatory for improving learning, the effectiveness of the learning process is fundamental to ensuring successful long-term accuracy. Ineffectively using blended learning may limit the learning rate as it could inhibit the learner’s ability or willingness to explore and exploit the information available in the learning environment.

In conclusion, the results of this study demonstrated that the BLIV strategy was more beneficial for self-assessment and the acquisition of complex technical skills during tacking learning than the BLI strategy among young learners.