Abstract
This study examined the relationship between trait anxiety (TA) and self-controlled (SC) frequency of feedback in the acquisition of the overhead volleyball serve. Forty-eight adolescent girls performed 240 acquisition trials, with the provision of knowledge of results (KR). After 48 h, they performed 16 transfer no-KR trials. Although no interactions were found on either acquisition or transfer, the high-anxious girls requested more feedback than the low-anxious ones. Also, feedback was requested more after accurate than after inaccurate trials.
Highlights
With the purpose of reconciling counterintuitive findings in motor learning (ML), Guadagnoli and Lee (2004) proposed a challenge-point framework (CPF) based on the idea that learning is directly related to the level of challenge imposed by a practice or feedback condition
The challenge-point created by any given ML situation is determined by the functional difficulty of the task, which results from an interaction between nominal task difficulty, the learner’s skill level, and the conditions of practice and/or feedback
The results showed no significant interactions between trait anxiety (TA) and SC-knowledge of results (KR)
Summary
With the purpose of reconciling counterintuitive findings in motor learning (ML), Guadagnoli and Lee (2004) proposed a challenge-point framework (CPF) based on the idea that learning is directly related to the level of challenge imposed by a practice or feedback condition. These authors argued that learning is maximized when a person faces an optimal level of challenge during the process of motor skill acquisition. The challenge-point created by any given ML situation is determined by the functional difficulty of the task, which results from an interaction between nominal task difficulty, the learner’s skill level, and the conditions of practice and/or feedback. In the CPF, practice variables have been examined across different types of participants, we believe that investigating people with different levels of trait anxiety (TA) could help to better understand ML processes
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