1 Preface

In the past four decades, China has made remarkable strides in athletic growth. The nation’s world-class athletes have grown into fruition. Recreational sports in particular, and an increasing variety of activities, have been enhancing Chinese people’s fitness awareness and increasing children’s options for extracurricular activities which has resulted in teenagers having growing interest for sports and an upsurge in their participation in sports. According to a study conducted by kinematics scholars, sport can boost the development of the human nervous system and shape a more sophisticated neural network, allowing the brain to work faster and more efficiently and make the mind sharper and more coordinated. Golf can sharpen the mind as it requires keen responses, focus, and perceptual ability. It is a sport suitable for restless teenagers. This article will explore the correlation between adolescent cognitive development through playing golf.

2 Research Object and Method

2.1 Research Objective

80 questionnaires for the survey were given randomly to teenagers taking part in golf training at the Guangzhou Feng Shanshan Golf Academy. The research subjects include 42 boys and 38 girls aged at 15.3±3.2 years old.

2.2 Research Method

2.2.1 Questionnaire

2.2.1.1 Sport Activities Scale

Sport Activities Scale revised by Liang Deqing and other scholars at the Wuhan Institute of Physical Education, is a piece of research that studies the amount of physical activities from intensity and duration of an exercise and to frequency. A score based on the amount of physical activity=exercise intensity×(exercise duration－1) ×frequency. Each perspective is classified into five grades (ranging from 1-5). The amount of exercise is scored numerically on a scale of 0-100. According to the result of this survey, research subjects were ranked from lowest to the highest based on these scores. Those ranking from 1st to 30th reported small amounts of exercise, while 31st to 60th place respondents reported moderate amounts of exercise, and 61st to 80th place respondents reported large amounts of exercise.

2.2.1.2 Questionnaire on Learning Efficiency

This questionnaire was designed based on the Motivated Strategies for Learning Questionnaire for Secondary School Students developed by Pintrich and other American scholars, as well as advice from relevant experts. Each question is graded on a score from 1 to 7, from “completely unlike me” to “it’s exactly me”.
2.2.2 Mathematical Statistics

SPSS software was employed to conduct factor analysis of learning efficiency as well as the variance analysis and correlation analysis of exercise amounts and learning efficiency.

3 Research Result and Analysis

3.1 Decide on Learning Efficiency Questionnaire

The writers consulted several psychological and educational experts and asked them to mark the 44 questions of the Motivated Strategies for Learning Questionnaire for Secondary School Students edited by Pintrich and other American scholars. Questions were given scores of 1, 2, 3 or 4, representing whether they could not reflect, could generally reflect, could better reflect or could completely show a correlation between exercise amounts and learning efficiency, respectively. According to the results, 29 questions with scores above 3 were selected as the questions for this learning efficiency questionnaire.

3.2 Decide on Factor Reflecting Learning Efficiency

Table 1 KMO and Bartlett Test of Sphericity

Kaiser-Meyer-Olk in Measure of Sampling Adequacy 0.798
Bartlett Test of Sphericity Approx. Chi-Square 295.551
P 0.000

This table shows the result of the KMO test is 0.798 and the result of the Bartlett Test of Sphericity is positive, indicating this questionnaire is suitable for factor analysis.

Table 2 Total Variance Explained

Component Eigenvalue % of Variance Cumulative %
1 4.427 38.271 38.271
2 3.126 26.878 65.149
3 2.094 17.226 82.375
4 1.018 8.373 90.748
5 0.534 4.389 95.137

The result of table 2 shows that the percentage of variance accounted for by component 1 is 38.271%, the percentage of variance accounted for by component 2 is 26.878% and that of component 3 is 17.226%. The percentage of variance accounted for by the first 3 components reaches 82.375%, which suggests they could be used to reflect learning efficiency. According to the Rotated Component Matrix, component 1 made up of questions 10,11,12,13,14,15,19, 20, 21 and 24 is named the learning strategy factor; component 2 formed by questions 3, 6, 7, 8, 25, 26 and 29 is called the factor of emotions; component 3 from questions 1-18 and 22 is entitled the motivation factor. Component 4 made up of questions 23 and 27, as well as component 5 with question 28, have been abandoned due to their low varience percentage.

3.3 Correlation between Sport Activity Amount and Learning Efficiency

3.3.1 Variance Analysis of Sport Activity Amount and Learning Efficiency

The statistics of variance analysis on the amount of sporting activities and learning efficiency in table 3, following, shows that teenage golf players differ from one another in learning efficiency depending on their exercise amounts. Taking learning strategy, learning emotion, and learning motivation as dependent variables, the results of variance analysis of different sporting activities amounts reveal that the three above-mentioned factors relating to learning efficiency are influenced by the amount of physical activities partaken in by respondents. Many related studies also prove that frequent involvement in physical exercise remarkably improves the working efficiency of the teenagers’ minds.

Table 3 Sport Activities Amount and Learning Efficiency

Factor Sport Activities Amount F p
Small Moderate Large
Learning Strategy 35.2±10.5 42.4±10.8 36.5±8.6 8.145 0.000
Learning Emotion 25.8±7.1 30.5±8.4 25.4±6.8 5.645 0.000
Learning Motivation 31.8±8.7 36.5±8.9 29.3±9.4 6.548 0.000

3.3.2 Correlation Analysis of Learning Strategy and Sports Activities

With learning strategy as a dependent variable, the analysis result of table 4 states that there is no significant relation between learning strategy, activity amount scores and the amount of sporting activities. It illustrates that playing golf does not influence learning strategy as the latter is shaped by long-term accumulation and self-learning

Table 4 Correlation Analysis of Learning Strategy and Sports Activities

Independent Variable Learning Strategy

Score of Activity Amount 0.512 0.078
Activity Intensity 0.486 0.125
Activity Duration 0.495 0.098
Activity Frequency 0.532 0.056

3.3.3 Correlation Analysis of Learning Emotion and Sport Activities Scale

Table 5 is a correlation analysis based on learning emotion as a dependent variable. The result indicates that learning emotion, and activity amount score, along with the combined amount of sports activities are significantly related, which proves that the effect of golfing on teenagers’ emotions is conspicuous.

During golf training, teenagers relax and alleviate their stress from school and study. Playing golf plays a distinctive role in improving teenagers’ mental health, mood stabilization and personality development. It helps adolescent self-cognition, stimulates their potential, boost their confidence and resilience to failures, and arouses their determination and perseverance to overcome difficulties and cultivate their willpower. As an outdoor sport, golfing connects teenagers to nature. They get to enjoy sunshine, breathe in fresh air and blend their emotions with nature, which is beneficial to relieving anxiety, adjusting to emotions and relaxation.

Table 5 Correlation Analysis of Learning Emotion and Sports Activities Scale

Independent Variable Learning Emotion

Score of Activity Amount 0.774 0.001
Activity Intensity 0.705 0.014

Activity Duration 0.768 0.000

Activity Frequency 0.804 0.000
3.3.4 Correlation Analysis of Learning Motivation and Sport Activities Scale

The result of table 6 of the analysis with learning motivation as a dependent variable shows that there is a significant relationship between learning motivation, score for amount of sporting activities and sport activity levels, which proves that golfing has a noteworthy effect on learning motivation. During training, junior golfers receive positive influences brought on by sport physiologically and psychologically, hence transferring the good motivation during golf play to study activities.

Table 6 Correlation Analysis of Learning Motivation and Sports Activities Scale

Independent Variable Learning Motivation

Score of Activity Amount 0.688 0.000
Activity Intensity 0.654 0.001
Activity Duration 0.667 0.000
Activity Frequency 0.684 0.000

4 Conclusion

This thesis is based on a survey of teenagers taking part in golf training at Feng Shanshan Golf Academy in reference to two questionnaires, Sport Activities Scale and Motivated Strategies for Learning Questionnaire. Using these statistics the researchers conclude results based on three influential factors: learning strategy, learning emotion and learning motivation.

Variance analysis is employed to analyse different amounts of golf exercise and the results show that the three factors are under the influence of the amount of exercise. In addition, the respective correlation analyses of sports activity amount scores and the three factors, and the sporting activity scale and the three factors both reveal that except for learning strategy, the other two factors are related to the scale of sporting activity. From the above discourse, it can be arrived at that appropriate golf training enhances teenagers’ learning efficiency. Therefore, proper golf playing boosts junior golfers’ school studies.

Zixuan Zheng, 11th Grader of Huafu International, Guangzhou, Guangdong of China. Her pieces have been featured in magazines, newspapers and on websites since the 7th Grade. Three-time junior golf champion in California. Making friends from all around the world by playing golf and writing powerful words are her greatest passions.