Athletic Insight - The Online Journal of Sport Psychology

The Effect of Self-Modeling on Skill
Learning and Self Efficacy of
Novice Female Beach-Volleyball Players

Eleni Zetou, Thomas Kourtesis, Katerina Getsiou,
Maria Michalopoulou, & Efthimis Kioumourtzoglou

Department of Physical Education and Sport Sciences
Democritus University of Thrace, Komotini.

ABSTRACT

Introduction

Method

Results

Discussion

References

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ABSTRACT

The present study examined the effect of self-modeling οn beach volleyball skill learning and self-efficacy. Participants were 32 novice female beach volleyball players (mean age = 12.8 years). All athletes followed an eight-week practice program regarding the skills of “setting” and “passing.” The program consisted of two practice units per week. Prior the beginning of the program, participants were randomly assigned into two groups: the self-modeling group (SMG) and the traditional group (TG). Participants of SMG watched their recorded execution and received verbal instructions twice during each practice unit (in the beginning and the middle). Control group received feedback traditionally, that is knowledge of performance and knowledge of results were provided by the instructor. “Setting” and “passing” were assessed by an instrument which was created by Zetou, Giatsis, and Tzetzis (2005) based upon Bartlett, Smith, Davis, and Peel’s (1991) test for setting and AAHPERD’s (1984) test for passing accordingly. There were three measurement sessions: a pre-, a post- and a retention test (one week after post-test). Self-efficacy was assessed before the first and after final practice, by a 5-item questionnaire (Theodorakis, 1996). The results indicated that participants of the SMG improved both skill learning and performance as well as their self-efficacy. The above findings are discussed in terms of theoretical as well as practical implications for understanding the relationship between self-modelling and self-efficacy in real practice settings.

Introduction

       According to Bandura (1969, 1977, 1986), the majority of human behaviors are learned through model observation. The observer memorizes model’s behavior in a pattern of representation and constitutes the appropriate information. This information assists the learner to develop a conceptual scheme in order to regulate the representation of motion and lead towards the correction of mistakes (Caroll & Bandura, 1990). Efficient observation is a basic method directed by four sources: attention, retention, reproduction and motivation (Bandura, 1969, 1971, 1986, 2001). Bandura (1977) has also explained that model observation evokes self-efficacy’s activation. Self-efficacy reflects the extent to which the person has the self-confidence that he/she will succeed in a given result, by exerting influence both on behavior and knowledge (Bandura, 1977, 1986, 2000). Self-efficacy is influenced by four sources of information: mastery experiences, vicarious experiences, verbal persuasions and affective or psychological states. Mastery experiences are suggested to provide the strongest increase in self-efficacy during skill-learning process (Bandura, 1986).

       Self-modeling is the process during which the individual observes himself /herself executing the skill (Dowrick & Dove, 1980). Alternatively, it could be the observation only of the successful attempts (Dowrick, 1991). Dowrick and Biggs (1983), as well as Dowrick (1999), focused their studies firstly on the area of self-observation (self-modeling) and secondly, on the changes in behavior due to the recurrent observation of someone on videotape presenting only the desired behavior towards achieving a goal (Dowrick & Dove). However, a number of studies were conducted by recording all movements, not merely the desired ones (Caroll & Bandura, 1990; McCullagh, Burch, & Siegel, 1990).

       A certain number of relevant studies demonstrated that self-modeling had a negative impact on the learner’s performance. Alkire and Brunse (1974), as well as Kimball and Cundick (1977), indicated that when the learners emphasize on the negative aspects of their performance, self-modeling has negative consequences. Moreover, Rothstein and Arnold (1976) identified that less than half of 50 reviewed papers presented self-modeling as an effective teaching method.

       Many relative experiments took place inside the laboratory while others were conducted in real world settings. Zetou, Fragouli, and Tzetzis (1999) compared two guiding methods using expert-modeling and self-modeling in volleyball. The researchers demonstrated that the practitioners observing the expert-model performed the skills better than those observing themselves. Tzetzis, Mantis, Zachopoulou, and Kioumourtzoglou (1999) compared the effect of observing expert athletes to self-modeling and to traditional teaching on novice skiers. The results indicated that the novices performed better under a combination of observing (expert model or themselves) and receiving verbal instructions than under a traditional teaching method. Kernodle, Johnson, and Arnold (2001) also suggested that when the difficulty of the execution is high, it is more useful for athletes to get information for both errors and their correction. On a similar line of research, Zetou, Vernadakis, Tzetzis, and Kioumourtzoglou (2000) investigated the effect of the aforementioned combination on skill learning regarding novice volleyball players. Learning was examined on both the result and skill technique. The results revealed that the group observing the expert athlete improved both in result and in technique.

       The use of model observation affects not only performance and learning but the psychological factors as well, such as the learner’s self-confidence (Dowrick, 1999). Buggey (1995) referred extensively to the important role of self-observation as a guiding process that influences self-confidence and behavior. Schunk, Hanson, and Cox (1987) indicated that observation of a novice model helps the learner to achieve self-efficacy and performance improvement better than with other modeling types. Furthermore, McCullagh and Weiss (2001) have underlined the importance of the model’s similarities to the learner regarding performance and psychological responses. Winfrey and Weeks (1993) compared the effect of self-modeling and the traditional teaching method on athletes’ improvement of performance and on parallel increase of their self-efficacy. According to the results, participants of both methods performed equally well and their corresponding self-efficacy was on similar levels. Similarly, Ram and McCullagh (2003) examined whether self-modeling increases the athletes’ serving performance and self-efficacy in medium-level volleyball players, aged 19 to 27 years. The results revealed that self-modeling had a positive impact only on the outcome of the skill and not on the improvement of the technique or on the athletes’ self-efficacy. In another study, Starek and McCullagh (1999) examined the effect of self-modeling as well as the novice-model observation on learning the “crawl” in swimming, as well as on self-efficacy. The participants were 10 novice adults, aged between 20 and 58 years. The results indicated that the learners who observed themselves performed the skill of “crawl” in swimming better, but no differences were found in either self-efficacy or the level of the participants’ arousal.

       Therefore, it sounds logical that the learning process, during which the learner observes himself/herself, may be considered as an effective learning method (Ram & McCullagh, 2003). Furthermore, it seems that self-modeling also helps towards, not only the perfection of the technique, but the performance as well (Dowrick, 1999; McCullagh & Little, 1990). Finally, whether self-modeling has a positive effect on self-efficacy, or not, is an issue open to further investigation.

       Beach Volleyball is a relatively new sport, thus it has been rather understudied. It is, by nature, a demanding, rigorous and difficult sport to play. So we believed that self-modelling through video should improve the players’ performance faster and easier compared to other feedback methods. Therefore, the aim of the present investigation was to conduct an empirical study on the effect of self-modeling on performance and skill learning in real-practice environment. Based on the social-cognitive theory (Bandura, 1977, 1986) as well as on relative empirical studies (Bradley, 1993; Dowrick & Dove, 1980; Ram & McCullagh, 2003), it was hypothesized that self-modeling would improve skill learning, and self-efficacy. The basic volleyball skills of “setting” and “passing” were used and participants were novice beach-volleyball female players.

Method

Participants

       Thirty two novice female beach volleyball players (Mean age = 12.8 years, SD = .53) with 12-14 months practice experience (M = 13.2, SD = 0.2) participated at the present study. Participants were randomly assigned into two groups, the self-modeling group (SMG, n = 16) and the control group (CG, n = 16). Verbal instructions on how to perform the skills was given before the beginning of the program. The instructor, the initial instructions and facilities in the practice sessions were the same for both groups. Before group assignment, a consent form (Ethical Principles of Psychologists and Code of Conduct) was distributed to all of the participants’ parents (American Psychological Association, 1992). The participants had not used video in practice before and they were asked to not practice in addition to the two practice session per week. To avoid the interaction between athletes of two groups, the practice sessions were adjusted to be in different days of week, so they could not discuss their sessions with each other.

Materials

       One camera (PANASONIC VSH), one video recorder (SONY 22΄΄) and one monitor (VESTEL 20΄΄) were used. Cloth ribbons, toe nailed in the sand, were used to mark off lines were in the court. Finally, 10 beach volleyball balls (MIKASA VLS, 200), one beach volleyball net (height = 2.43 m.), two poles and one rope (placed 3 m. high) were also used.

Measures

       There were three measurement sessions. A pre-test took place during the first practice session. “Setting” and “passing” were assessed, by according procedures which were planned and developed upon the Bartlett et al. (1991) test for “setting” and AAHPERD’s (1984) test for “passing” in indoor volleyball. Test-retest reliability of the tests has been examined and found to be quite satisfactory (r = .94 for setting and r = .97 for passing, Zetou et al., 2005). After the eight-week practice program, a post-test took place followed by a retention test, one “no-practice” week later (Schmidt, 1991).

Skill evaluation

       Evaluation of “setting.” The goal was to assess the participants’ ability to set a free-throw ball, effectively. The participant stood at the right side of the court, inside a square (1.5 Χ 1.5 m), which was located about 0.50 m from the net and 2.5 m from the side line. Each participant received a high throw from the assistant, who was standing in the middle of the court, and executed a setting towards the left side, so that the ball went over the rope and into the target area. Throws that did not fall into the participant’s area were repeated. Each participant performed 10 trials. A trial was counted as valid, but no points were credited if the ball touched the rope, if the ball did not fall into the target area, or if it went over the net into the opponent court. Points from “1” to “4” were awarded for each setting that went over the rope and landed in or hit any part of the target area, including lines. The maximum possible score was 40 points. Given the fact that in beach volleyball there are only two players on each team, each player must be able to perform passing, equally well, towards either the left or the right side of the court. Therefore, the same procedure took place for the evaluation of setting towards the right side of the court. The final score was the sum of left and right test score, divided by two.

       Evaluation of “passing.” The goal was to assess the participants’ ability to pass a free-throw ball, effectively. The participant stood at the left or the right side of the court, on a marked point located 4 m from the side line and 2 m from the end line. The participant received a high throw from the assistant (who was standing within the opponent court) and executed a passing so that the ball went over the rope and into the target area. Throws which did not fall into the participant’s area were repeated. Each participant had to perform 10 trials (5 to each side). A trial was counted as valid, but no points were credited if the ball touched the rope, if the ball did not fall into the target area, or if it went over the net into the opponent court. Points from “1” to “4” were awarded for each passing that went over the rope and landed in or hit any part of the target area, including lines. The maximum possible score was 40 points.

Self-efficacy evaluation

       Based on previous research (Theodorakis, 1995) self-efficacy was evaluated using a pre and post test design, whereas learning was evaluated three times. A questionnaire created by Theodorakis (1996) was used for the assessment of the participants’ self-efficacy. Following pre-testing for each skill the participants were informed regarding their score, and they were asked to fill out the self-efficacy questionnaire, stating the possible score they would try to reach. The questionnaire included five questions and the participants had to answer using a 10-point scale. For each question, participants circled either “yes” or “no” depending on how sure they felt about their answer.

Design and Procedure

       The duration of training period was eight weeks. It is considered to be enough time for model-observation learning (“at least 5 weeks of model observation” Magill, 1993; Rose, 1997). There were two practice sessions per week giving a total of 16 60-minute practice sessions.

       All participants of the SMG were videotaped with a professional camera from a six-meter distance and a 45-degree angle. Then they watched their own movements in a color monitor for two minutes and received standard verbal cues from the instructor, who was trained to watch and correct the important errors. There were five crucial points regarding skill technique. The most important point for correct execution of the technique was noted first and the less important last (Fishman & Tobey, 1978). After the demonstration, participants performed the same drills for 15 minutes and were videotaped again. Then they watched their own movements again and the previous procedure was repeated. The above procedure was applied twice during each practice unit (in the beginning and in the middle) (Landers, 1975; Zetou et al., 1999) and four types of drills were performed (10 trials per drill).

       On the other hand, participants of the CG watched only the instructor’s demonstration of the skills and received verbal feedback (knowledge of performance and knowledge of results) every 10 trials. Feedback referred to incorrect execution as well as to information for the correct technique (there were 5 crucial points regarding skill technique).

Results

Skill learning

       In the beginning of the study we hypothesized that self modeling method would have a more positive effect on skill learning compared to the traditional method. In order to test the above hypothesis, a 2 X 3 analysis of variance with repeated measures on the second factor was conducted to compare scores of groups with different treatment conditions and the three measurement periods (pre-test, post-test and retention test) for each skill. Pretest was used as a baseline for participants’ volleyball skill. Table 1 shows means and standard deviations of the two groups in skill performance and self-efficacy.

Table 1 Self Modeling

       An independent-samples t-test was conducted to compare the scores of two groups in pretest of setting and passing. There was no significant initial differences between groups in setting skill (M = 22.31, SD = 1.35) versus (M = 22.50, SD = 1.41, t(30) = -.38), neither in passing skill (M = 15.56, SD = 2, versus M = 15.63, SD = 1.89, t(30) = -.91). The magnitude of the differences in the means was small (eta squared = .01) for setting and bigger for passing (eta squared = .03).

       Results of two-way repeated measures analysis of variance indicated that there was a significant interaction between the measurement period and the group using the multivariate criterion of Wilk’s lambda, Λ = .031, F(2, 60) = 140.28, p<.01, multivariate eta squared = .97 for beach volleyball setting skill. There was also a significant main effect for measurement period Wilk’s lambda, Λ = .111, F (2, 60) = 604.71, p<.01, multivariate eta squared = .89 and a significant main effect for group F (2, 30) = 164.30 p<.01, multivariate eta squared = .85.

       Three paired-samples t-tests were conducted to follow up the significant interaction. We controlled for family wise error rate across these tests using Holm’s sequential Bonferroni approach. Differences in mean ratings of scores between the two groups were significantly different between measures 1 and 2 (pretest-postest), t(15) = 29.26, p<.01, between measures 2 and 3 (postest-retention test), t(15) = 2.70, p<.01 and measures 1 and 3 (pretest-retention test), t(15) = 32.67, p<.01, with the self-modelling group performing better than the control group in setting skill (Fig. 1).

Figure 1 Self Modeling

       Results of two-way repeated measures analysis of variance indicated that there was a significant interaction between the measurement period and the group using the multivariate criterion of Wilk’s lambda, Λ = .064, F (2, 60) = 51.20 p<.01, multivariate eta squared = .63, for beach volleyball passing skill. There was also a significant main effect for measurement period Wilk’s lambda, Λ = .32, F (2, 60) = 359.83, p<.01 multivariate eta squared = .92 and a significant main effect for group F (2,30) = 69.85, p<.01, multivariate eta squared = .70.

       Three paired-samples t-tests were conducted to follow up the significant interaction. We controlled for family wise error rate across these tests using Holm’s sequential Bonferroni approach. Differences in mean ratings of scores between the two groups were significantly different between measure 1 and 2 (pretest-postest), t(15) = 17.92, p<.01, between measure 2 and 3 (postest-retention test), t(15) = 8.9, p<.01 and measure 1 and 3 (pretest-retention test), t(15) = 18.8, p<.01, with the self-modelling group performing better than the control group in passing skill (Fig. 2).

Figure 2 Self Modeling

Self-efficacy

       In the beginning of the study we hypothesized as well, that self modeling method would have a more positive effect on self efficacy compared to the traditional method. In order to test the above hypothesis, the following process was performed. Initially, an independent-samples t-test was conducted to compare the scores of two groups in pretest of self-efficacy in setting and passing. There was no significant initial differences between groups in setting skill efficacy (M = 13.49, SD = 2.42) versus (M = 12.67, SD = 2.77, t (30) = -.38, p = .7), neither in passing skill efficacy(M = 6.96, SD = 1.38, versus M = 7.37, SD = 1.32, t (30)= -.91, p = .9). The magnitude of the differences in the means was small (eta squared = .01) for setting and bigger for passing (eta squared = .03).

       In order to conclude whether participants’ self-efficacy was influenced by self-modeling observation, two additional two-way repeated measures analysis of variance (ANOVA repeated measures, 2 groups X 2 measurement periods) were conducted, to compare scores of groups with different treatment conditions and the two measurement periods (pre-test, post-test) of self-efficacy scores in every skill.

       There was a significant interaction between the measurement period and the group using the multivariate criterion of Wilk’s lambda, Λ = .29, F (1, 30) = 73.7, p< .05, multivariate eta squared = .71 for self-efficacy in setting skill. There was also a significant main effect for measurement period Wilk’s lambda, Λ = 19, F (1, 30) = 120.4, p< .05, multivariate eta squared = .8 and a significant main effect for group F (1, 30) = 7.73 p<.05, multivariate eta squared = .2.

       There was also a significant interaction between the measurement period and the group using the multivariate criterion of Wilk’s lambda, Λ = .11, F (1, 30) = 233.7, p< .05, multivariate eta squared = .8, a significant main effect for measurement period Wilk’s lambda, Λ = .10 , F (1, 30) = 270, p< .05, multivariate eta squared = .9 and a significant main effect for group F (1, 30) = 5.06 p< .05, multivariate eta squared =.14 for the self-efficacy in passing skill. These results demonstrate that the group through the self-modeling procedure improved the participants’ self-efficacy to perform the setting and passing skill more than the control group.

Discussion

       The aim of the present investigation was to conduct an empirical study on the effect of self-modeling on performance and skill learning in real-practice environment. Based on the social-cognitive theory (Bandura, 1977, 1986) as well as on relative empirical studies (Bradley, 1993; Dowrick & Dove, 1980; Ram & McCullagh, 2003), it was hypothesized that self-modeling would improve skill learning, and self-efficacy. The basic volleyball skills of “setting” and “passing” were used and participants were novice beach-volleyball female players.

       The results of the present study seem to be consistent with the relevant bibliography which supports the notion that the use of model observation is an effective method that can change the individuals’ motor behavior (Bandura, 1986; McCullagh, 1993). In the current study, participants of the self-modeling group (SMG) improved their performance much more than their control-group (CG) mates. An increased self-efficacy was also observed regarding SMG participants.

       The current results come in agreement to those of other relative studies (Dowrick & Dove, 1980). Schunk and Hanson (1989) were among the first to study the manner(s) in which self-modeling differs from other teaching mechanisms. The authors reported that in their study, children under self-modeling were more successful in performance than children under the standard teaching method. The present study is in line with such conclusions supporting that self-modeling is an effective teaching method, especially regarding new-skill acquisition.

       The present differences in performance can be attributed to the different type of information provided to different groups. Specifically, both teams received the same amount of verbal instructions but the SMG received additional visual information, which seemed to play the most important role. Magill and Schoenfelder-Zohdi (1996) suggested that observing a model while receiving verbal information regarding performance provides novices with useful feedback for correcting their mistakes. Furthermore, it seems that the augmented feedback gives an additional motive to novices for trying even harder. Providing feedback in the form of verbal cueing facilitates the performance of the task by indicating vital form characteristics (Landin, 1996), by enhancing attention and by providing additional information that may not be available through visual observation (Janelle, Champenoy, Coombes, & Mousseau, 2003). Kernodle and Carlton (1992) have also supported the above notions.

       Feltz, Landers, and Raeder (1979) found that self-efficacy and performance levels of participants who observed a model while receiving instructions were higher than the corresponding levels of those who received other types of demonstration. It also seemed the participant’s actual practice resulted in a higher self-efficacy level compared to only observing other individuals performing (Gould, Hodge, Petersen, & Giannini, 1989). Therefore, it seems that although modeling provides adequate information regarding the cognitive components of the task, skilled execution requires both feedback and practice. Similar results have been reported for various skills such as locomotion (Weiss, 1983; Weiss & Klint, 1987), dance routines (McCullagh, Stiehl & Weiss, 1990) and sport skills (Wiese-Bjornstal & Weiss, 1992).

       According to certain researchers, self-modeling influences some of the learner’s psychological traits as well. That is, the process of observing oneself is a unique method to enhance self-efficacy and/or to reduce stress. This hypothesis relates to the social-cognitive learning theory, according to which human functions are a product of reciprocal interactions between behavior, environmental variables and various cognitive and personal factors (Schunk, 1989). Dowrick (1983) has also supported the above hypothesis.

       According to results of previous studies, self-modeling and verbal feedback help athletes to enhance performance and learning. The new element in this study is if self-modeling and verbal feedback should enhance players’ self-efficacy. Thus, in the present study self-efficacy was actually enhanced through the self-modeling process. It is possible that self-modeling operated towards strengthening of self-efficacy, and via self-regulation the participants performed the skills correctly. Self-regulation, as Schunk (1989) has mentioned, is “learning that occurs from students’ self-generated behaviors systematically oriented toward the attainment of their learning goals” (p.83). Self-regulation consists of three components that interact with each other: self-observation, self-judgment and self-reaction. When humans observe aspects of their behavior on video, they compare their behaviors to the existing standards and respond either positively or negatively. Presumably, the participants in the present study formed a positive judgment of them and benefited from self-modeling. It is clear that verbal instructions on error correction, provided in parallel to self-modeling, aided to this end. Verbal instructions forced the learners to focus their attention on the aspects they had to correct. It seems that the use of verbal instructions increases the efficiency of using models by providing additional information to that supplied via model demonstration.

       It is also possible that the improvement of the participants’ performance was affected by other social-cognitive factors not examined in the present study. It seems that motivation and attributions interact with learning strategies and goal-directed behavior, as well as with the learner’s characteristics. For example, different individuals might perceive the information from self-modeling differently thus resulting in different responses. Furthermore, given the fact that not all participants were equally motivated, different levels of motivation may have resulted in different responses. Many authors have reported that the positive impact of model observation depends strongly on the participants’ motivation levels (Harter, 1978; Weiss & Bredmeir, 1983; Yando, Seitz, & Zigler, 1979,). Little and McCullagh (1989) supported that internally motivated children will focus their attention on how to perform the skill, whereas externally motivated children will be dependent upon external sources of information in order to form a basis for comparison of their execution. Possibly, the use of self-modeling is more effective for participants who are more motivated and more intent on performing better and who could therefore recognize and correct their errors. The athletes’ motivation was not examined in the present study. It was hypothesized that their motivation level was high since they were participated in the study voluntarily.

Limitations

       A possible limitation of the present study was the small sample size. However, Beach Volleyball teams are comprised of two players, so the total number of beach volleyball athletes is rather limited. In any case, the results of the current research should be taken into account within the aforementioned limitation. Another issue may be the fact that participants in the present study were novice female players. Possibly, the self-modeling process might have functioned differently if advanced or elite athletes had participated. Overall, within the above limitations, the results of the present research come in agreement with those of other relevant studies regarding the role of self-modeling in skill learning.

Conclusion and Applications for Practice

       The results of the present study add some useful elements to the issue of model use by trainers and physical education teachers. Coaches and physical education teachers could use self modeling through video in parallel with verbal cues, in the beginning, in the middle or in the end of practice session, to enhance skill-acquisition in novice athletes. Instructions should be given for both correct and incorrect efforts. Verbal cues should be short, concrete, all-inclusive and in an age-appropriate language.

       Nonetheless, further research seems to be necessary regarding the effect of self-modeling on other psychological parameters, such as the athletes’ intrinsic motivation. Moreover, researchers should also examine the effect of self-modeling in relation to the learning phase of the participants and/or to the difficulty or complexity of the skill.

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Correspondence concerning this article should be sent to Eleni Zetou, Department of Physical Education and Sport Sciences, Democritus University of Thrace, 69100 Komotini, Greece. Tel: 2310 675280. E-mail: elzet@phyed.duth.gr

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