Critical factors analysis of student-athletes learning training contradiction via AHP

Abstract

This study aims to develop a model that identifies the factors influencing the Learning Training Contradiction (LTC) among student-athletes. The goal is to provide a theoretical foundation for educators, administrators, and researchers to effectively address this issue. Initial indicators affecting LTC were identified from existing literature. An optimized system of indicators was established based on evaluations from 14 experts in relevant fields. This system includes four criterion-level factors, 15 factor-level elements, and 52 alternative-level measures. Using the Analytic Hierarchy Process (AHP), pairwise comparisons were conducted to determine the weight values of each indicator relative to the decision-making objective. AHP results revealed significant variations in the importance of factors contributing to LTC among student-athletes. Based on the weight distribution, this study suggests that resolving LTC in the future requires a collaborative mechanism involving multiple stakeholders to maintain a balance between learning and training, ultimately promoting the holistic development of youth athletes.

Introduction

Since the emergence of competitive sports, numerous countries have emphasized developing young athletes’ athletic skills to achieve superior sporting outcomes while neglecting their cultural knowledge acquisition. This has given rise to the conflict between learning and training in long-term sports practice.

Since the advent of competitive sports, many nations have prioritized the development of athletic skills in young athletes, often at the expense of academic education. This has led to the widespread phenomenon known as the “learning-training contradiction” (LTC) during prolonged athletic practice.

Scholars define LTC as the conflicting relationship between academic learning and athletic training in the cultivation of competitive sports talent¹. Specifically, LTC emerges from various factors that create conflicts between training schedules and coursework, competition timelines and academic calendars, as well as the systems governing sports and education. As societal demands continue to change, addressing this contradiction has become increasingly urgent². School athletic teams, which serve as the primary platform for nurturing competitive sports talent, are integral to fostering well-rounded youth athletes. Therefore, effectively tackling LTC among student-athletes is essential for the future of competitive sports.

Current academic research on LTC focuses on four main areas:

1. A comprehensive analysis of the causes of LTC through a national system perspective^3,4.

2. Recommendations for addressing LTC based on dual-factor theory and subjective factors^5,6.

3. Examination of LTC within training systems^7–12.

4. Studies on LTC in collegiate sports education^13,14.

Although existing research provides valuable insights from multiple perspectives, it lacks a holistic and systematic investigation into the factors influencing LTC among student-athletes. To address this gap, the current study employs the Analytic Hierarchy Process (AHP) and the Delphi method to analyze the factors affecting LTC, assess their relative importance for student-athlete development, and propose practical pathways for improvement. The specific objectives of this study include:

1. Identifying key factors that affect LTC through a synthesis of the literature and consultation with experts.

2. Offering strategic priorities for school athletic teams to educators and administrators.

3. Providing researchers with evidence-based strategies for optimizing LTC.

4. Developing an AHP-based model of LTC influencing factors to establish a core indicator system aimed at fundamentally resolving LTC issues, improving youth sports development mechanisms, and advancing competitive sports programs.

Literature review

The utilization of the AHP in sports science

The Analytic Hierarchy Process (AHP), proposed by Saaty, is a mathematical method designed to address unstructured decision-making problems. By integrating qualitative and quantitative approaches, AHP simplifies complex issues through hierarchical structuring, which makes it widely applicable in sports research¹⁵.

In the context of collegiate physical education (PE), evaluating teaching quality presents several challenges, including fuzzy or unquantifiable factors, imperfect evaluation systems, and methodological limitations¹⁶. To tackle these issues, researchers have employed AHP to assess the quality of PE teaching, providing fresh insights into evaluation practices^17–21.

High-quality sports education is heavily dependent on teachers, who are key figures in the dissemination of knowledge and innovation²². Studies have shown the effectiveness of AHP in evaluating teacher performance^23,24 and developing standardized assessment systems²⁵.

As sports events gain importance in health promotion and urban culture, multi-level attractiveness index systems help shape sustainable development strategies for amateur competitions^26,27. Additionally, AHP is beneficial in identifying critical risk factors related to event management^28,29.

The Analytic Hierarchy Process (AHP) serves as a robust decision-making tool specifically designed to address complex problems characterized by ill-defined structures, uncertain expert judgments, multiple stakeholders, and measurement errors in existing data¹⁵. In the domain of athlete selection, the implementation of AHP-based evaluation systems has demonstrated significant advantages in identifying competitive sports talents with greater efficiency and accuracy^30,31. Recent research by GÖK³² further substantiates the value of AHP by systematically consolidating expert opinions from national team coaches across various countries, thereby enabling precise quantification of selection criteria weights and facilitating the development of streamlined decision-making frameworks.

With the advancement of competitive sports, physical fitness has emerged as a crucial indicator for evaluating athletes’ functional capacities and directly influences their competitive performance. The implementation of novel physical examination assessment systems enables comprehensive tracking and analysis of athletes’ skill-related morphological changes, thereby providing valuable data-driven insights for optimizing talent development practices³³. However, current physical fitness evaluation systems face challenges including ambiguous indicator definitions and unclear weighting mechanisms.

The Analytic Hierarchy Process (AHP), as an effective multi-criteria decision-making method, is particularly suitable for addressing uncertainty and ambiguity in complex decision-making scenarios. Its application in constructing hierarchical models for athlete physical fitness assessment has gained significant traction. To enhance the efficiency of fitness evaluation, researchers have employed AHP to develop structured assessment frameworks for various sports disciplines, including handball³⁴ weightlifting³⁵ and soccer³⁶. These systematic approaches provide a scientific foundation for evidence-based talent selection, ultimately improving the accuracy and efficiency of athlete identification and development.

Moreover, with the continuous improvement of socioeconomic conditions, public health sports issues and national physical fitness have emerged as prominent societal concerns, serving as key drivers for research on traditional physical education and its impact on health concepts. This development necessitates comprehensive adjustments in school physical education curricula and requires physical education teachers to actively respond to evolving societal demands³⁷. As the foundation of national development, the physical health status of adolescents exerts profound influences on China’s socioeconomic progress, making the establishment of scientific and effective physical fitness assessment systems particularly crucial.

The AHP method demonstrates considerable application potential in the domain of youth physical health. By constructing hierarchical models for adolescent fitness evaluation, researchers can systematically analyze the relative importance of various physical indicators, thereby providing scientific foundations for developing targeted training programs and enhancing youth physical health outcomes^38,39. These models facilitate evidence-based decision-making in physical education policy and practice, bridging the gap between theoretical research and practical implementation.

In summary, AHP-based hierarchical modeling offers sports policymakers and practitioners scientifically robust decision-making tools, contributing significantly to the advancement of sports science. Building upon successful applications of AHP in sports research, this study aims to explore its suitability and effectiveness in analyzing influencing factors of the “academic-training conflict” among student-athletes. The findings are expected to provide actionable insights for optimizing the balance between academic and athletic development in sports education systems.

Research on “Learning-Training contradiction”

Research on the Learning-Training Contradiction (LTC) among student-athletes is extensive^40,41, with thorough investigations into its causes^42–46. The prevalence of LTC in the development of youth athletes can be traced back to historical and systemic issues^47–49, which explains why reasonable strategies often yield limited results. Antonacopoulou, E. P. notes that the paradoxical nature of organizational life leads to reductive approaches that treat the relationship between learning and training as if it were a linear causality⁵⁰.

Countries such as Russia and Germany have implemented integrated models—like combining sports clubs with educational programs—to address LTC, thus enhancing both athletic performance and academic quality⁵¹.

The growing prominence of the LTC has led scholars to increasingly focus on existing issues within educational systems and regional development opportunities^47,52 prompting systematic exploration of scientific solutions to this dilemma. In addressing the concerning phenomenon of low GPA rates among student-athlete populations, researchers have proposed comprehensive reform measures tailored to educational system characteristics, aiming to optimize the balance between academic and athletic commitments^43,53,54. Concurrently, emerging scholarship has shifted attention to student-athletes’ support networks-encompassing coaches, teachers, parents, peers, and support staff. Groundbreaking studies demonstrate how predictive modeling of support personnel effectiveness, when aligned with athletes’ actual needs, can significantly enhance athletes’ journey toward autonomy and self-discipline^55,56.

A systematic review of current research demonstrates that major sporting nations including the United States, Germany, Japan, and Australia have each established distinctive educational models for student-athletes. While implementation approaches differ across these countries, they consistently maintain an “academics-first, athletics-second” philosophy, with the fundamental objective of promoting comprehensive personal development. These nations particularly emphasize safeguarding student-athletes’ academic performance through specialized policy measures aimed at achieving balanced development between education and sports training, fundamentally addressing academic-athletic conflicts, and cultivating high-quality sports reserve talents⁵⁷.

In comparison to these international models, China has developed its own integrated approach through the collaborative release of the Guidelines on Deepening the Integration of Sports and Education to Promote Youth Development by the General Administration of Sport and the Ministry of Education. This groundbreaking policy document establishes a systematic framework that not only provides solutions for mitigating student-athletes’ academic-training conflicts but also enhances school sports programs through institutional innovations and creates sustainable pathways for developing well-rounded competitive athletes.The implementation of this policy has generated substantial academic interest and discussion within China’s sports scholarship community. Researchers have conducted extensive examinations of various aspects including the conceptual foundations of sports-education integration⁵⁸ key influencing factors and implementation strategies, as well as practical applications of successful models^42,59. This growing body of research has made significant contributions to both theoretical understanding and practical implementation, particularly in adapting international best practices to China’s unique educational and sports development context. The scholarly discourse reflects an increasingly sophisticated approach to addressing the complex relationship between athletic training and academic achievement within diverse cultural and institutional frameworks.

Current research findings suggest that while different countries employ varying strategies, the common goal remains achieving optimal balance between academic and athletic development^60–62. The Chinese model, with its emphasis on institutional integration and policy support, represents an important evolution in addressing this universal challenge in sports education. These comparative studies provide valuable insights for ongoing policy refinement and practical implementation in the field of athlete education worldwide.

Methodologies

Delphi method

Step 1: Formation of the Expert Panel.

An expert panel was established with clearly defined research objectives. Experts were selected based on the necessary scope of knowledge for the study. The panel size, typically consisting of 8 to 20 members, was determined by the scale and complexity of the research. Priority was given to the authority of the experts and the convergence of their opinions rather than simply increasing the sample size⁶³. A total of fourteen experts, each with at least five years of experience in sports education, were recruited, in line with literature recommendations and current standards for educational management research.

Step 2: Delphi Rounds.

Round 1: The preliminary indicator system was distributed to 14 experts from universities and secondary schools for evaluation. Key metrics were calculated, which included Full-score frequency, Arithmetic mean, Opinion concentration degree, Coordination coefficient (Kendall’s W), and Authority coefficient. Based on statistical analysis and expert feedback, the indicators were refined through processes of addition, deletion, or modification.

Round 2: The revised system was redistributed to the same group of experts. Final indicators were established through an analysis of the coordination coefficients derived from the consolidated feedback.

Step 3: Final System Determination.

In the third step of the process, the final determination of the system was established following two Delphi rounds. Through expert consensus, the outcome consisted of a comprehensive indicator system comprising four criterion-level factors, fifteen factor-level elements, and fifty-two alternative-level measures. This structured framework is essential for evaluating and implementing the system effectively.

AHP analysis

The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty and colleagues, is a methodology that derives priority scales through pairwise comparisons based on expert judgments^64–66. As one of the most widely applied multi-criteria decision-making (MCDM) tools, AHP aims to identify pivotal research driving its evolution and map its application domains. Its three fundamental functions are: structuring complexity, measurement, and synthesis. Consequently, AHP’s hierarchical framework enables the measurement and synthesis of diverse factors within complex decision-making processes, facilitating the integration of components into a coherent whole¹⁵. This study employs AHP to address the academic-athletic conflict among student-athletes, with three primary objectives: Identify key influencing factors of the conflict; Analyze the relative importance of each factor and Establish a core indicator system for conflict analysis. Ultimately, this approach seeks to fundamentally resolve the academic-athletic conflict, optimize talent cultivation mechanisms for young athletes, and advance competitive sports development. Prioritizing factors impacting this conflict represents a critical complex MCDM challenge in athletic development⁶⁷. The AHP implementation follows four essential steps¹⁵: (1) Construct the decision hierarchy: A top-down structure comprising target, criterion, factor, and indicator layers. (2) Develop pairwise comparison matrices: Establish relative importance weights for elements within each level relative to the superior criterion. (3) Determine indicator weights and conduct consistency validation: Calculate eigenvector-based weights and verify Consistency Ratio (CR) thresholds. (4) Synthesize global priorities: Compute overall weight rankings across the hierarchy.

Results

Participant characteristics

The demographic profile of the 14 Delphi experts is presented in Table 1. Key characteristics reveal a strategically sampled cohort:

Table 1.

Expert panel profile for the learning-training contradiction analysis.

Name	Gender	Professional Rank	Affiliation
Tang **	M	Professor	Hunan University of Science and Technology
Wang **	F	Associate Professor	Hunan University of Science and Technology
Huang **	F	Lecturer	Hunan University of Science and Technology
Huang **	F	Assistant Lecturer	Hunan University of Science and Technology
Wang **	F	Lecturer	Hunan University of Science and Technology
Ge **	F	Lecturer	Guangdong Technology College
Wu **	F	Associate Professor	Hebei Sport University
Zhang **	M	Professor	Xizang Minzu University
Li **	F	Lecturer	Xizang Minzu University
Guo **	M	Grade 2 Teacher (Secondary School)	Xidongting No.1 Middle School
Wu **	M	Grade 2 Teacher (Junior High School)	Xidongting No.1 Middle School
Long **	M	Grade 2 Teacher (Junior High School)	Changde Changying School
Dai **	M	Grade 2 Teacher (Junior High School)	The Experimental School attached to Xiangtan University
Shu **	M	Senior Teacher (Secondary School)	Changjie Long Term Nine Year School

1. Institutional Representation: Universities: 9 experts (64.3%); Secondary schools: 3 experts (21.4%); Elementary schools: 2 experts (14.3%).

2. Gender Distribution: Male: 7 (50.0%); Female: 7 (50.0%).

3. Experience Profile: All participants had ≥ 5 years of professional experience (M = 11.3 years, SD = 4.2) Academic Seniority: Full Professors: 35.7% (n = 5); Associate Professors: 35.7% (n = 5); Senior Lecturers: 28.6% (n = 4).

4. Age Range: All participants were ≥ 30 years (M = 42.6, SD = 6.8; range: 32–58 years).

Analysis results of the Delphi method

Indicator selection

The China National Knowledge Infrastructure (CNKI) database was searched for Chinese literature (1990/01/01–2024/11/01) using “Learning Training Contradiction” (LTC) in titles/abstracts/keywords. Web of Science Core Collection was searched for English literature using “learning and training contradiction” (1900–2024, article type, English language). Additional sources included Google Scholar, Sci-Hub, and PubMed. Retrieval yielded 279 Chinese and 582 English publications (Tables 2 and 3).

Table 2.

CNKI publications on “learning training contradiction” (1990–2024).

Document Type	Academic Journals	Dissertations	Conference Papers
LTC	153	53	37

Table 3.

Web of science publications on “learning training contradiction” (1990–2024).

Document Type	Articles	Conference Papers
LTC	314	178

Initial factors influencing long-term care (LTC) in student-athletes were identified through a review of literature and refined with input from experts via face-to-face, phone, and digital consultations. Four core categories emerged: Coach-related Factors, Student-related Factors, Management/Support Factors, and Family Environment Factors. This led to the development of a preliminary indicator system comprising 4 criterion-level indicators, 13 factor-level indicators, and 62 alternative-level indicators. Fourteen experts from universities and secondary schools evaluated these indicators using the Delphi method (Table 4).

Table 4.

Preliminary indicator framework.

Target Layer (A)	Reference Layer (B)	Factor Layer (C)	Indicator Layer (D)	Target Layer (A)
Factors infecting the LTC (A)	Coach Factors(B1)	Training Research(C11)	Research papers (D111)	Tao, R. C1. Chen, Z. F2. Qin, L6. Greenberg, J7. Jones, D. F8. Mahony, D. F9. Zheng, J11. Yang, Y. W12. Wang, P40. Zhang, C. H42. Zhou, X. D45. Huang, H68. Wan, B. J69.
			Research Project(D112)
			Published Monographs(D113)
			Physical fitness research (D114)
			Technical and tactical research (D115)
		Coaching Competency (C12)	Academic Qualifications (D121)
			Training Experience (D122)
			Competition Experiences (D123)
			Team Performance Records (D124)
			Training Plan Development (D125)
			Training Methodology Selection (D126)
			Development of Training programs (D127)
			Selection of training approaches (D128)
		Coaching Motivation (C13)	Winning Honor for the School (D131)
			Personal Interest (D132)
			Professional Title Evaluation (D133)
			Salary Improvement (D134)
	Students Factors (B2)	Training motivation (C21)	Academic Advancement (D211)	Tao, R. C1. Jones, D. F8. Zhang, C. H42. Zhou, X. D45. Wan, B. J46. Myakinchenko, E51. Seong, E. S57. Wan, B. J69.
			Personal Interest (D212)
			Avoidance of Academic Studies (D213)
		Sports proficiency (C22)	Competition Awareness (D221)
			Training Habits (D222)
			Personal Willpower (D223)
			Match Consciousness (D224)
			Exercise Habits (D225)
			Personal Willpower (D226)
		Cognition of learning and training (C23)	Student’s Attitude Toward Athletic Training (D231)
			Student’s Attitude Toward Academic Learning (D232)
			Students’ Attitudes towards Acadenic Learning (D233)
			Students’ commitment to academic subjects (D234)
		Cultural and knowledge level(C24)	Academic Performance (D241)
			Academic Learning Habits (D242)
			Academic Planning (D243)
	Management Support(B3)	Management & Support Factors(C31)	Leadership’s Attitude Toward Student Training (D311)	Tao, R. C1. Zheng, J11. Yang, Y. W12. Wang, P40. Gomez, J41. Zhou, X. D45. Wan, B. J46. Barros, C. P48. Wan, B. J69.
			Leadership’s Attitude Toward Student Academic Learning (D312)
			Number of Coaches（D313）
			Credit Management（D314）
			Management of the Physical Education Department（D315）
			Whether a Dedicated Tutor is Assigned (D316)
			Scheduling of Academic Training (D317)
			Student Attendance (D318)
		Facilities and equipment (C32)	Provision of Dedicated Academic Tutors (D321)
			Student Attendance Records (D322)
			Training-Academic Scheduling (D323)
		Training funds (C33)	Training Venue Area (D331)
			Training Equipment Quantity (D332)
			Sport Discipline Type (D333)
			Cost of sports nutrition products (D334)
		Cultural course guarantee (C34)	Competition Fees (D341)
			Apparel Costs (D342)
			Sports Injury Treatment (D343)
			Teaching Methods Implementation (D354)
			Curriculum Content Selection (D355)
			Academic Assessment Standards (D356)
	Family Environment Factors(B4)	Parental attitude (C41)	Parental Attitudes toward Athletic Training (D411)	Wang, C. Y3. Yang, X. Q4. Chen, Z. F5. Qin, L6. Shi, L10. Zhang, C. H42. Wan, B. J46. Wan, B. J69.
			Parental Attitudes towards Academic Learning (D412)
			Parents’ Educational Level (D413)
			Parenting Style (D414)
		Family conditions (C42)	Family Economic Status (D421)
			Climate of family relations (D422)
			Family residence (D423)
			Family Relationship Atmosphere (D422)
			Family Location (D423)

Statistical analysis and methods

1. Expert Positive Coefficient (Response Rate).

The response rate reflects experts’ engagement level. Higher rates indicate greater reliability. This study achieved:

Round 1: 14/14 questionnaires returned (100%).

Round 2: 14/14 questionnaires returned (100%).

This confirms high expert commitment and data reliability.

• (2)Expert Opinion Concentration.

Expert opinion concentration is quantified using the Arithmetic Mean (M_j) and Full-Score Frequency (K_j).

• A.Arithmetic Mean.

The importance of indicator “j” is measured by its arithmetic mean, calculated as

M_j represents the number of experts evaluating indicator “j”, C_ij represents the score assigned to indicator “j” by expert “i”. A higher M_j value indicates greater perceived importance of indicator “j” by the expert panel.

• B.Full-Score Frequency.

The consensus on critical importance is assessed by

m_j represents the number of experts evaluating indicator “j”, n_j represents the number of experts assigning the maximum score to indicator “j”. Higher K_j values reflect stronger expert consensus on the essentiality of indicator “j”.

Table 5 (presented in subsequent sections) details the ranges of M_j and K_j cross all hierarchical levels based on the two Delphi rounds.

Table 5.

Expert consultation results: Mean scores and full score-frequency ranges.

Evaluation Dimension	Round 1 Consultation		Round 2 Consultation
	Mean Range	Full Score Freq.	Mean range	Full Score Freq.
Representativeness of Tier-1 Indicators	3.21 ~ 4.86	0.21 ~ 0.86	2.71 ~ 4.68	0.14 ~ 0.86
Relevance of Tier-2 Indicators	3.21 ~ 4.71	0.14 ~ 0.86	3.29 ~ 4.86	0.21 ~ 0.93
Rationality of Tier-3 Indicators	3.21 ~ 4.93	0.14 ~ 0.93	2.57 ~ 4.93	0.07 ~ 0.93

Note: Expert ratings were collected using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Full score frequency denotes the percentage of experts assigning the maximum score (1 points).

• (3)Consensus Level of Expert Opinions.

Expert consensus was assessed using the Coefficient of Variation (L_j) and Coefficient of Concordance (χ²).

A. Coefficient of Variation

n_j represents the number of experts rating indicator j, C_ij represents the score assigned by expert i to indicator j, M_j represents the arithmetic mean of indicator j, S_j represents the population standard deviation of indicator j, L_j represents the coefficient of variation of indicator.Lower L_j values indicate higher consensus for indicator j (L_j< 0.15 typically denotes strong agreement) (Table 6).

Table 6.

Coefficient of variation (CV) ranges for expert ratings.

Evaluation Dimension	Round 1 Consultation	Round 2 Consultation
Representativeness of Tier-1 Indicators	0.07 ~ 0.44	0.07 ~ 0.49
Relevance of Tier-2 Indicators	0.13 ~ 0.46	0.11 ~ 0.45
Rationality of Tier-3 Indicators	0.05 ~ 0.46	0.05 ~ 0.47

B. Kendall’s Coefficient of Concordance (W).

Where:

When adjusting for tied ranks:

 represents the number of experts, represents the number of indicators, R _j represents the sum of ranks assigned to indicator j, is the mean of rank sums, T i represents the correction factor for tied ranks in expert i’s ratings, computed as , where g is the number of tied groups and t k is the size of the k-th tied group.Ti is the tied ranks correction for expert i. ranges from 0 (no agreement) to 1 (complete agreement). Higher values indicate stronger consensus. After 2–3 consultation rounds, typically stabilizes below 0.5 with controlled error margins.

C. Significance Testing of Concordance (χ² Test).

 represents the number of experts, represents the number of indicators,  is Kendall’s coefficient of concordance

The degrees of freedom are calculated using the formula:

If p < 0.05, it indicates that experts are in the significant concordance and results are statistically acceptable; if p ≥ 0.05, it indicates that experts are in the non-significant concordance and results require re-evaluation.

The consensus analysis reveals (Table 7):

Table 7.

Expert consensus level assessment.

Parameter	Round 1 Consultation	Round 2 Consultation
Number of indicators	79	71
Kendall’s W	0.399	0.448
χ² Statistic	435.397	439.151
	78	70
p-value	< 0.001	< 0.001

Round 1: Moderate agreement (W = 0.399, χ² (78) = 435.397, p<0.001)

Round 2: Enhanced consensus (W = 0.448, χ² (70) = 439.151, p<0.001), approaching the strong agreement threshold (W≥ 0.5).

Conclusion: Both rounds exhibited statistically significant consensus (p< 0.001), confirming the reliability of expert judgments at 95% confidence.

• (4)Expert Authority Assessment.

The authority coefficient (Cr) of experts is determined by their judgment basis (Cα) and familiarity level with the indicators (Cs).

A. Expert Authority Coefficient.

Cα reflected judgment basis (practical/theoretical/contextual experience) per Table 8, Cs measured subject familiarity per Table 8. Judgment Basis(Cα) derives from four dimensions: Practical experience, Theoretical analysis, Knowledge of domestic/international practices and Intuition. The coefficient (Cα) is the sum of all selected judgment basis values for an expert, with(Cα) ≤ 1. When Cα = 1, the judgment basis has a significant influence on the expert; when Cα = 0.8, the influence is moderate; when Cα = 0.6, the influence is relatively small. The judgment basis is shown in Table 8.

Table 8.

Judgment basis coefficients ().

Judgment Basis	Major Influence (0.5)	Moderate Influence (0.4)	Minor Influence (0.3)
Practical experience	0.5	0.4	0.3
Theoretical analysis	0.3	0.2	0.1
Knowledge of domestic/international practices	0.1	0.1	0.1
Intuition	0.1	0.1	0.1

The self-evaluated expert authority demonstrated robust methodological rigor across both consultation rounds (Tables 8 and 9): Judgment Basis Coefficient () increased from 0.82 (Round 1) to 0.85 (Round 2), indicating major influence of expertise (threshold: > 0.80). This reflects experts’ substantial reliance on practical experience, theoretical analysis, and contextual knowledge during evaluation. Familiarity Level () rose from 0.70 to 0.74, confirming moderate-to-strong subject-matter competence (classification: 0.60–0.80 = “relatively familiar” to “very familiar”). Authority Coefficient () exceeded the 0.75 high-reliability threshold in both rounds (Round 1: 0.76; Round 2: 0.80), validating the content validity of consultation outcomes⁷⁰. The 5.3% inter-round improvement signifies enhanced engagement consistency. Sustained high authority coefficients (> 0.75) confirm that expert judgments were anchored in substantive domain knowledge and experiential reasoning, mitigating subjective bias risks inherent in Delphi techniques (Table 10).

Table 9.

Familiarity level coefficients ().

Familiarity level	(Cs)
Very familiar	1.0
Relatively familiar	0.8
Moderately familiar	0.6
Slightly familiar	0.4
Unfamiliar	0.2

Note: (Cα) = Sum of selected values (max 1.0).

Table 10.

Self-assessment results.

Parameter	Round 1	Round 2	Interpretation
Judgment Basis ()	0.82	0.85	Major influence on expert judgments
Familiarity ()	0.70	0.74	Moderate-to-strong familiarity
Authority()	0.76	0.80	High authority

Note: Self-assessments were collected via structured questionnaires.

Model modification and refinement

This study implemented a two-round Delphi expert consultation process to refine the evaluation indicators, primarily utilizing the critical threshold method for indicator screening. For each candidate indicator, three statistical measures were calculated: full-score frequency (K_j), the arithmetic mean (M), and the coefficient of variation (CV). The critical thresholds for retention were determined through specific statistical operations. Indicators scoring above the threshold defined as the mean minus the standard deviation (M - SD) were retained for both full-scores frequency and the arithmetic mean. Conversely, for the coefficient of variation, indicators scoring below the threshold calculated as the mean plus the standard deviation (M + SD) were retained. Comprehensive computational outcomes for these criteria are detailed in Tables 11 and 12. To safeguard against the inadvertent exclusion of pivotal indicators, a conservative elimination protocol was adopted: only indicators failing to satisfy all three screening criteria simultaneously were removed. Those failing one or two criteria underwent thorough deliberation by the research team, with decisions regarding their retention or removal grounded in the fundamental principles of comprehensiveness, scientific validity, and operational feasibility. Throughout this iterative refinement process, substantive consideration was consistently given to all modification suggestions and qualitative feedback provided by the expert panel, ensuring the model’s robustness and contextual relevance.

Table 11.

First-round expert evaluation results.

Metric	Average	Standard Deviation	Critical Threshold
Arithmetic Mean	3.94	0.67	3.27
Coefficient of Variation	0.29	0.13	0.42
Ful- score Frequency	0.48	0.29	0.20

Table 12.

Second-round expert evaluation results.

Metric	Average	Standard Deviation	Critical Threshold
Arithmetic Mean	3.88	0.73	3.16
Coefficient of Variation	0.29	0.14	0.43
Full-score Frequency	0.47	0.30	0.18

Following the initial round of expert consultation, eight indicators were eliminated based on the critical threshold principle: “Physical Fitness Research (D114)”, “Technical/Tactical Research (D115)”, “Training Foundation (D221)”, “Physical Fitness Level (D222)”, “Technical/Tactical Level (D223)”, “Credit Management (D314)”, “Management by Physical Education Departments (D315)”, and “Family Member Circumstances (D424)”. These indicators failed to meet the critical thresholds for all three criteria: frequency of full scores, arithmetic mean, and coefficient of variation. Additionally, the expert panel suggested conceptual overlaps: D114 and D115 with indicators D111, D112, D113, and D242 with D422, further justifying their removal. Indicators “Student Investment in Training (D232)” and “Student Investment in Academic Studies (D234)” fell below the thresholds for arithmetic mean and frequency of full scores. Considering that “Student Attitude Towards Training (D231)” encompassed the content of D232 and “Student Attitude Towards Academic Learning (D233)” encompassed D234, both D231 and D233 were subsequently deleted. The first-round consultation also identified problematic indicator phrasing. “Cultural Knowledge Level (C24)” and “Teacher”s Instructional Plan (D341)” were deemed inappropriate, while “Whether Special Tutoring Teachers Are Arranged (D316)” was considered ambiguous. Through discussion with the panel, these were revised to “Academic Proficiency Level (C24)”, “Academic Subject Instructional Plan (D351)”, and “Whether Dedicated Academic Subject Tutoring Teachers Are Arranged (D321),” respectively. Experts further indicated that indicators D125 and D126 were misplaced under “Coaching Motivation (C13)”, and D311 and D312 were inappropriate under “Management System (C31)”, recommending the creation of new sub-factor level indicators. Subsequent analysis, incorporating this feedback, led to the addition of “Academic-Athletic Cognition (C14)” under “Coach Factors (B1)” and “Leadership Attitude (C31)” under “Management and Support Factors (B3)”. This comprehensive revision reduced the total number of indicators from 79 to 71. A revised questionnaire and detailed modification notes were sent to the original 14 experts. Analysis of the second-round responses revealed no indicators falling outside the critical thresholds across all three criteria, and no indicators prompted significant controversy or new suggestions for additions. Consequently, a refined evaluation indicator system was finalized, as presented in Table 13.

Table 13.

Final indicator system for LTC influencing factors.

Target Layer (A)	Reference Layer (B)	Factor Layer (C)	Indicator Layer (D)
Factors infecting the LTC (A)	Coach Factors(B1)	Training Research(C11)	Research Papers (D111)
			Research Project (D112)
			Published Monographs (D113)
		Coaching Competency (C12)	Academic Qualifications(D121)
			Training Experience (D122)
			Competition Experiences (D123)
			Team Performance Records (D124)
			Training Plan Development (D125)
			Training Methodology Selection (D126)
		Coaching Motivation (C13)	Winning Honor for the School (D131)
			Personal Interest (D132)
			Professional Title Evaluation (D133)
			Salary Improvement (D134)
		Cognition of Learning and Training (C14)	Coach’s Attitude Toward Student Training (D141)
			Coach’s Attitude Toward Student Academic Learning (D142)
	Students Factors (B2)	Training Motivation (C21)	Academic Advancement (D211)
			Personal Interest (D212)
			Avoidance of Academic Studies (D213)
		Sports Proficiency (C22)	Competition Awareness (D221)
			Training Habits (D222)
			Personal Willpower (D223)
		Cognition of Learning and Training (C23)	Student’s Attitude Toward Athletic Training (D231)
			Student’s Attitude Toward Academic Learning (D232)
		Cultural Level (C24)	Academic Performance (D241)
			Academic Learning Habits (D242)
			Academic Planning (D243)
	Management & Support Factors (B3)	Leadership Attitude (C31)	Leadership’s Attitude Toward Student Training (D311)
			Leadership’s Attitude Toward Student Academic Learning (D312)
		Management Systems (C32)	Provision of Dedicated Academic Tutors (D321)
			Student Attendance Records (D322)
			Training-Academic Scheduling (D323)
			Coaching Staff Allocation (D324)
		Facilities and Equipment (C33)	Training Venue Area (D331)
			Training Equipment Quantity (D332)
			Sport Discipline Type (D333)
		Training Funds (C34)	Competition Fees (D341)
			Apparel Costs (D342)
			Sports Injury Treatment (D343)
			Sports Nutrition Costs (D344)
		Academic Support(C35)	Academic Subject Instructional Plans (D351)
			Cultural Teachers’ Attitude toward Students’ Academic Studies (D352)
			Cultural Teachers’ Attitude toward Students’ Training (D353)
			Teaching Methods Implementation (D354)
			Curriculum Content Selection (D355)
			Academic Assessment Standards (D356)
	Family Environment Factors(B4)	Parental Attitude (C41)	Parental Attitudes toward Athletic Training (D411)
			Parental Attitudes towards Academic Learning (D412)
			Parents’ Educational Level (D413)
			Parenting Style (D414)
		Family Conditions(C42)	Family Economic Status (D421)
			Family Relationship Atmosphere (D422)
			Family Location (D423)

AHP analysis results

Step 1: Constructing the decision hierarchy model for the LTC

To identify factors exhibiting greater relative importance within the LTC, the initial phase involves establishing a hierarchical relationship among influencing elements. Figure 1 presents the hierarchical model developed in this study, comprising four criterion layers (B1-B4), fifteen factor layers (C11-C42), and fifty-two indicator layers (D111-D423) across four hierarchical tiers.

Fig. 1.

Hierarchical model of factors influencing the LTC.

Step 2: Constructing pairwise comparison matrices

Following the establishment of the hierarchical structure, the subordination relationships between elements across adjacent levels are defined. The second step involves applying Saaty’s 9-point relative importance scale¹² to perform pairwise comparisons. Indicators are compared in pairs within each hierarchical level to determine their relative importance concerning the superior criterion (Table 14). This study utilized YAAHP (Version number: 12.11.8293) to construct the judgment matrices. The process entailed generating a 5 × 5 matrix centered on the target layer (Level 1) through synthesis of pairwise comparison data and developing 3 × 3 matrices for secondary criteria under each primary criterion layer. The local weight vectors (eigenvectors) of these pairwise comparison matrices were computed to determine Local weights (relative priorities within each matrix) and Local priorities (relative influence of elements within their level). As established by Saaty¹⁵ these local priorities immediately quantify the relative impact of element sets within a given level through the pairwise comparison matrix sets.

Table 14.

Fundamental Scale for Pairwise Comparisons (Adapted from Saaty (1990)).

When comparing  to	Scale Value	Definition	Mathematical Representation
Equal importance	1	Elements contribute equally
Moderate importance	3	Slightly favors one element
Strong importance	5	Strongly favors one element
Very strong importance	7	Dominantly favors one element
Extreme importance	9	Absolute preference for one element

Use Intermediate values when compromise is needed between adjacent scales 2, 4, 6, 8 and Reciprocals for inverse comparisons (vs.)1/3, 1/5, 1/7, 1/9.

Step 3: Determining indicator weights and consistency validation

In AHP, calculating the Consistency Index (CI) and Consistency Ratio (CR) is essential. When assigning values to indicators, strict adherence to judgment matrix principles is required. To effectively prevent bias in matrix consistency, validation against the Random Consistency Index (RI) values (as detailed in Table 15) must be performed to verify logical consistency⁷¹. A CR value < 0.1 confirms acceptable consistency If CR ≥ 0.1, the judgment matrix must be adjusted iteratively until compliance is achieved The Consistency Ratio quantifies the deviation between decision-makers’ judgments and perfect consistency, calculated as: CR = CI/RI.

Table 15.

Random consistency index (RI) reference values.

Order	1	2	3	4	5	6	7	8
RI	0	0.00	0.58	0.89	1.14	1.24	1.32	1.41

Source: Saaty (1995).

In this study, YAAHP (Version number: 12.11.8293) was employed to statistically analyze the judgment matrices at the criterion level and each indicator level to obtain the weight distribution and consistency test at the criterion level. From the results of the consistency test, the opinions of 14 experts in the relevant fields constituted the final sample of this study, and the CR value of the final sample was calculated by eliminating the inconsistent responses.

1. Consistency Index Formula:

Based on the derived consistency indicators, the consistency ratio was computed in accordance with the following formula:

• (2)Dynamic Adjustment Mechanism for Inconsistency.

The CR test fundamentally quantifies the logical self-consistency of expert judgments. When CR > 0.1, it indicates systematic bias or random errors in the judgment matrix, necessitating structured adjustments to achieve cognitive-data alignment.

1) Human-machine collaborative correction: By leveraging the real-time CR monitoring function of the YAAHP (Version number: 12.11.8293), identify the 3–5 elements that have the greatest impact on consistency. When the CR shows inconsistency, utilize its automatic correction function for inconsistent matrices. Under the premise of preserving the expert’s original data, optimize the judgment matrix through the genetic algorithm to ultimately ensure the consistency of the CR value.

2) Digital optimization processing: For matrices with mild inconsistency (0.1 < CR ≤ 0.2), apply the geometric mean correction method:

For the highly inconsistent matrix (CR > 0.2), the optimal transfer matrix method is introduced, and the modified matrix is solved by the least square method.

Based on the data analysis and organization in Table 16, the ranking of the weights of each level element in the criterion layer is as follows: B1 (Coach Factors, 0.5611) > B2 (Student Factors, 0.2613) > B3 (Management Support, 0.1067) > B4 (Family Environment, 0.0709). Coach Factors is the most significant contributor to the LTC. The Consistency Ratio (CR) is 0.0958, and the maximum eigenvalue is 4.2558.

Table 16.

Criterion layer.

	Coach Factors(B1)	Students Factors (B2)	Management & Support Factors (B3)	Family Environment Factors(B4)	Wi
Coach Factors(B1)	1	4	4	5	0.5611
Students Factors (B2)	1/4	1	4	4	0.2613
Management & Support Factors (B3)	1/4	1/4	1	2	0.1067
Family Environment Factors(B4)	1/5	1/4	1/2	1	0.0709
CR = 0.0958			λmax = 4.2558

Based on the data analysis and organization in Table 17, the ranking of the weights of each level element under the coach demension in the factor layer is as follows: C13 (Coaching Motivation, 0.5280) > C12 (Coaching Competency, 0.2014) > C11 (Training Research, 0.1595) > C14 (Academic-Athletic Cognition, 0.1111). Coaching Motivation is the most significant factor influencing the LTC. The CR is 0.0776, and the maximum eigenvalue is 4.2072.

Table 17.

Factor layer indicators: Coach dimension.

Coach Factors(B1)	Training Research(C11)	Coaching Competency (C12)	Coaching Motivation (C13)	Cognition of Learning and Training (C14)	Wi
Training Research(C11)	1	1/6	1	2	0.1595
Coaching Competency (C12)	6	1	2	3	0.2014
Coaching Motivation (C13)	1	1/2	1	2	0.5280
Cognition of Learning and Training (C14)	1/2	1/3	1/2	1	0.1111
CR = 0.0776			λmax = 4.2072

Based on the data analysis and organization in Table 18, the ranking of the weights of each element under the student dimension in the factor hierarchy is as follows: C21 (Training Motivation, 0.5704) > C24 (Academic Proficiency, 0.1897) > C23 (Academic-Athletic Cognition, 0.1237) > C22 (Athletic Level, 0.1163). Training Motivation the most significant factor influencing the LTC. The CR is 0.0909, and the maximum eigenvalue is 4.2427.

Table 18.

Factor layer weights - student dimension (B2).

Students Factors (B2)	Training Motivation (C21)	Sports Proficiency (C22)	Cognition of Learning and Training (C23)	Cultural Level (C24)	Wi
Training Motivation (C21)	1	3	6	4	0.5704
Sports Proficiency (C22)	1/3	1	1	1/3	0.1163
Cognition of Learning and Training (C23)	1/6	1	1	1	0.1237
Cultural Level (C24)	1/4	3	1	1	0.1897
CR = 0.0909			λmax = 4.2427

Based on the data analysis and organization in Table 19, the ranking of the weights of each level element under the management support in the factor hierarchy is as follows: C32 (Management System, 0.3014) > C31 (Leadership Attitude, 0.2939) > C33 (Facilities/Equipment, 0.1971) > C34 (Training Funds, 0.1477) > C35 (Academic Support, 0.0599). Management System is the most significant factor influencing the LTC of student-athletes. The CR is 0.0847, and the maximum eigenvalue is 5.3795.

Table 19.

Factor layer weights - management support (B3).

Management & Support Factors (B3)	Leadership Attitude (C31)	Management Systems (C32)		Facilities and Equipment (C33)	Training Funds (C34)	Academic Support(C35)	Wi
Leadership Attitude (C31)	1	1/2		2	3	5	0.2939
Management Systems (C32)	1/3	1		1	2	4	0.3014
Facilities and Equipment (C33)	1/2	1		1	2	2	0.1971
Training Funds (C34)	1/3	1/2		1/2	1	5	0.1477
Academic Support(C35)	1/5	1/4		1/2	1/5	1	0.0599
CR = 0.0847			λmax = 5.3795

Based on the data analysis and organization in Table 20, C41 (Parental Attitude) and C42 (Household Conditions) equally contribute (0.5000 each). These two factors are equally significant in influencing the LTC of student-athletes under the family environment. The CR is 0, and the maximum eigenvalue is 2.

Table 20.

Factor layer weights - family environment (B4).

Family Environment Factors(B4)	Parental Attitude (C41)	Family Conditions(C42)	Wi
Parental Attitude (C41)	1	1	0.5000
Family Conditions(C42)	1	1	0.5000
CR = 0.0000		λmax = 2.0000

Based on the data analysis and organization in Table 21, the ranking of the weights of each level element under the training research in the index hierarchy is as follows: D111 (Research Papers, 0.6144) > D113 (Published Monographs, 0.2684) > D112 (Research Projects, 0.1172). Research Papers is the most significant factor influencing the LTC of student-athletes under the training research. The CR is 0.0707, and the maximum eigenvalue is 3.0735.

Table 21.

Indicator layer weights - training research (C11).

Training Research(C11)	Research Papers (D111)	Research Project (D112)		Published Monographs (D113)	Wi
Research Papers (D111)	1	4		3	0.6144
Research Project (D112)	1/4	1		1/3	0.1172
Published Monographs (D113)	1/3	3		1	0.2684
CR = 0.0707			λmax = 3.0735

Based on the data analysis and organization in Table 22, the ranking of the weights of each level element underCoaching Competency in the index hierarchy is as follows: D126 (Training Methodology Selection, 0.4185) > D124 (Team Performance Records, 0.1996) > D125 (Training Plan Development, 0.1847). Training Methodology Selection is the most significant factor influencing the LTC. The CR is 0.0983, and the maximum eigenvalue is 6.6192.

Table 22.

Indicator layer weights - coaching competency (C12).

Coaching Competency (C12)	Academic Qualifications(D121)	Training Experience (D122)	Competition Experiences (D123)	Team Performance Records (D124)	Training Plan Development (D125)	Training Methodology Selection (D126)	Wi
Academic Qualifications (D121)	1	1/2	1	1/2	1/3	1/6	0.0601
Training Experience (D122)	2	1	1/3	1/4	1/4	1/4	0.0616
Competition Experiences (D123)	1	3	1	1/4	1/4	1/5	0.0756
Team Performance Records (D124)	2	4	4	1	2	1/5	0.1996
Training Plan Development (D125)	3	4	4	1/2	1	1/2	0.1847
Training Methodology Selection (D126)	6	4	5	5	2	1	0.4185
CR = 0.0983				λmax = 6.6192

Based on the data analysis and organization in Table 23, the ranking of the weights of each level element under the coaching motivation factor in the index hierarchy is as follows: D134(Salary Improvement, 0.5705) > D133(Professional Title Evaluation, 0.2305) > D132(Personal Interest, 0.1131) > D131(Winning Honor for the School, 0.0860). Salary Improvement is the most significant factor influencing the LTC of student-athletes. The CR is 0.0785, and the maximum eigenvalue is 4.2097.

Table 23.

Indicator layer weights - coaching motivation (C13).

Coaching Motivation (C13)	Winning Honor for the School (D131)	Personal Interest (D132)	Professional Title Evaluation (D133)	Salary Improvement (D134)	Wi
Winning Honor for the School (D131)	1	1/2	1/3	1/4	0.0860
Personal Interest (D132)	2	1	1/3	1/5	0.1131
Professional Title Evaluation (D133)	3	1/3	1	1/4	0.2305
Salary Improvement (D134)	4	5	4	1	0.5705
C.R.=0.0785			λmax = 4.2097

Based on the data analysis and organization in Table 24, the ranking of the weights of each level element under the indicator hierarchy of the student’s training cognition factor is as follows: D142(Coaches’ attitudes toward student’ learning, 0.8333) > D141(Coaches’ attitudes toward student’ training, 0.1667). Coaches’ attitudes toward student’ learning is the most significant factor influencing the LTC of student-athletes. The CR is 0, and the maximum eigenvalue is 2.

Table 24.

Indicator layer weights - cognition of learning and training (C14).

Cognition of learning and training (C14)	Coaches’ attitudes toward student’ training (D141)	Coaches’ attitudes toward student’ learning (D142)	Wi
Coaches’ attitudes toward student’ training (D141)	1	1/5	0.1667
Coaches’ attitudes toward student’ learning (D142)	5	1	0.8333
C.R.=0.0000		λmax = 2.0000

Based on the data analysis and organization in Table 25, the ranking of the weights of each level element under the training motivation factor in the index hierarchy is as follows: D211(Promotion, 0.5396) > D213(Avoidance of study, 0.2970) > D212(Hobbies and interests, 0.1634). Promotion is the most significant factor influencing the LTC of student-athletes. The CR is 0.0088, and the maximum eigenvalue is 3.0092.

Table 25.

Indicator layer weights - training motivation (C21).

Training motivation (C21)	Promotion (D211)	Hobbies and interests (D212)		Avoidance of study (D213)	Wi
Promotion (D211)	1	3		2	0.5396
Hobbies and interests (D212)	1/3	1		1/2	0.1634
Avoidance of study (D213)	1/2	2		1	0.2970
C.R.=0.0088			λmax = 3.0092

Based on the data analysis and organization in Table 26, the ranking of the weights of each level element under the factor of sports proficiency in the index hierarchy is as follows: D223(Personal willpower, 0.5499) > D222(Exercise habits, 0.2402) > D221(Match consciousness, 0.2098). Personal willpower is the most significant factor influencing the LTC of student-athletes. The CR is 0.0176, and the maximum eigenvalue is 3.0183.

Table 26.

Indicator layer weights - sports proficiency (C22).

Sports proficiency (C22)	Match consciousness (D221)	Exercise habits (D222)		Personal willpower (D223)	Wi
Match consciousness (D221)	1	1		1/3	0.2098
Exercise habits (D222)	1	1		1/2	0.2402
Personal willpower (D223)	3	2		1	0.5499
C.R.=0.0176			λmax = 3.0183

Based on the data analysis and organization in Table 27, the ranking of the weights of each level element under the indicator hierarchy of “Cognition of learning and training” is as follows: D231(Students’ attitudes toward athletic training, 0.8000) > D232(Students’ attitudes towards cultural learning, 0.2000). Students’ attitudes toward athletic training is the most significant factor influencing the LTC of student-athletes. The CR is 0, and the maximum eigenvalue is 2.

Table 27.

Indicator layer weights - cognition of learning and training (C23).

Cognition of learning and training (C23)	Students’ attitudes toward athletic training (D231)	Students’ attitudes towards cultural learning (D232)	Wi
Students’ attitudes toward athletic training (D231)	1	1/4	0.8000
Students’ attitudes towards cultural learning (D232)	4	1	0.2000
C.R.=0.0000		λmax = 2.0000

Based on the data analysis and organization in Table 28, the ranking of the weights of each level element under the cultural level factor in the index hierarchy is as follows: D243(Cultural learning plannin, 0.4126) > D242(Cultural Study Habits, 0.3275) > D241(Cultural academic achievement, 0.2599). Cultural learning plannin is the most significant factor influencing the LTC of student-athletes. The CR is 0.0516, and the maximum eigenvalue is 3.0536.

Table 28.

Indicator layer weights - cultural level (C24).

Cultural level (C24)	Cultural academic achievement (D241)	Cultural Study Habits (D242)		Cultural learning planning (D243)	Wi
Cultural academic achievement (D241)	1	1		1/2	0.2599
Cultural Study Habits (D242)	1	1		1	0.3275
Cultural learning planning (D243)	2	1		1	0.4126
C.R.=0.0516			λmax = 3.0536

Based on the data analysis and organization in Table 29, it is concluded that the ranking of the weights of each level element under the factor of leadership attitude in the index hierarchy is as follows: D311(Leaders’ attitude toward student training, 0.5000) = D312(Leaders’ attitudes toward student learning, 0.5000). These two factors are the most significant factor influencing the LTC of student-athletes. The CR is 0, and the maximum eigenvalue is 2.

Table 29.

Indicator layer weights - leadership attitude (C31).

Leadership attitude (C31)	Leaders’ attitude toward student training (D311)	Leaders’ attitudes toward student learning (D312)	Wi
Leaders’ attitude toward student training (D311)	1	1	0.5000
Leaders’ attitudes toward student learning (D312)	1	1	0.5000
C.R.=0.0000		λmax = 2.0000

Table 30.

Indicator layer weights - management systems (C32).

Management systems (C32)	Number of coaches (D324)	Whether a Dedicated Tutor for Academic Courses is Assigned (D321)	Scheduling of academic training (D323)	Student attendance (D322)	Wi
Number of coaches (D324)	1	2	1/6	1/3	0.5731
Whether a Dedicated Tutor for Academic Courses is Assigned (D321)	1/2	1	1/6	1/5	0.0640
Scheduling of academic training (D323)	6	6	1	3	0.2624
Student attendance (D322)	3	5	1/3	1	0.1005
C.R.=0.0360			λmax = 4.0961

Based on the data analysis and organization in Table 30, the ranking of the weights of each level element under the management systems factor in the index hierarchy is as follows: D324(Number of coaches, 0.5731) > D323(Scheduling of academic training, 0.2624) > D322(Student attendance, 0.1005) > D321(Whether a Dedicated Tutor for Academic Courses is Assigned, 0.0640). Number of coaches is the most significant factor influencing the LTC of student-athletes. The CR is 0.0360, and the maximum eigenvalue is 4.0961.

Based on the data analysis and organization in Table 31, the ranking of the weights of each level element under the factor of venue and equipment in the index hierarchy is as follows: D331(Area of training grounds, 0.5584) > D333(Types of sports programs, 0.3196) > D332(Number of training devices, 0.1220). Area of training grounds is the most significant factor influencing the LTC of student-athletes. The CR is 0.0176, and the maximum eigenvalue is 3.0183.

Table 31.

Indicator layer weights - facilities and equipment (C33).

Facilities and equipment (C33)	Types of sports programs (D333)	Area of training grounds (D331)		Number of training devices (D332)	Wi
Types of sports programs (D333)	1	3		1/2	0.3196
Area of training grounds (D331)	1/3	1		1/4	0.5584
Number of training devices (D332)	2	4		1	0.1220
C.R.=0.0176			λmax = 3.0183

Based on the data analysis and organization in Table 32, the ranking of the weights of each level element under the training funds factor in the index hierarchy is as follows: D344 (Sports Nutrition Costs, 0.4468) > D342 (Apparel Costs, 0.2600) > D343 (Sports Injury Treatment Costs, 0.1939). Sports Nutrition Costs is the most significant factor influencing the LTC of student-athletes. The CR is 0.0495, and the maximum eigenvalue is 4.1323.

Table 32.

Indicator layer weights - training funds (C34).

Training Funds (C34)	Competition Fees (D341)	Apparel Costs (D342)	Sports Injury Treatment (D343)	Sports Nutrition Costs (D344)	Wi
Competition Fees (D341)	1	1/4	1/2	1/3	0.0993
Apparel Costs (D342)	4	1	1	1/2	0.2600
Sports Injury Treatment (D343)	2	5	1	1/3	0.1939
Sports Nutrition Costs (D344)	3	2	3	1	0.4468
CR = 0.0495			λmax = 4.1323

Based on the data analysis and organization in Table 33, the ranking of the weights of each level element under Academic Support in the index hierarchy is as follows: D353 (Cultural Teachers’ Attitude Toward Training, 0.3708) > D356 (Academic Assessment Standards, 0.1675) > D354 (Teaching Methods Implementation, 0.1377). Cultural Teachers’ Attitude Toward Training is the most significant factor influencing the LTC of student-athletes. The CR is 0.0990, and the maximum eigenvalue is 6.6235.

Table 33.

Indicator layer weights - academic support (C35).

Academic Support(C35)	Academic Subject Instructional Plans (D351)	Cultural Teachers’ Attitude toward Students’ Academic Studies (D352)	Teaching Methods Implementation (D354)		Curriculum Content Selection (D355)	Academic Assessment Standards (D356)	Cultural Teachers’ Attitude toward Students’ Training (D353)	Wi
Academic Subject Instructional Plans (D351)	1	1/2	1/3		1/2	1	1/2	0.0916
Cultural Teachers’ Attitude toward Students’ Academic Studies (D352)	2	1	1/2		1	1/3	1/2	0.1096
Teaching Methods Implementation (D354)	2	2	1		1	1/2	1/5	0.1377
Curriculum Content Selection (D355)	1	1	1		1	1	1/4	0.1228
Academic Assessment Standards (D356)	1	3	2		1	1	1/3	0.1675
Cultural Teachers’ Attitude toward Students’ Training (D353)	2	2	5		4	3	1	0.3708
CR = 0.0990				λmax = 6.6235

Based on the data analysis and organization in Table 34, the ranking of the weights of each level element under the factor of parents’ attitude in the index hierarchy is as follows: D411 (Parents’ Attitude Toward Training, 0.3682) > D413 (Parents’ Educational Level, 0.3216) > D414 (Parenting Style, 0.1949). Parents’ Attitude Toward Training is the most significant factor influencing the LTC of student-athletes. The CR is 0.0695, and the maximum eigenvalue is 4.1855.

Table 34.

Indicator layer weights - parental attitude (C41).

Parental Attitude (C41)	Parental Attitudes towards Academic Learning (D412)	Parents’ Educational Level (D413)	Parenting Style (D414)	Parental Attitudes toward Athletic Training (D411)	Wi
Parental Attitudes towards Academic Learning (D412)	1	1/4	1	1/4	0.1153
Parents’ Educational Level (D413)	4	1	1	1	0.3216
Parenting Style (D414)	1	1	1	1/2	0.1949
Parental Attitudes toward Athletic Training (D411)	5	1	1	1	0.3682
CR = 0.0695			λmax = 4.1855

Based on the data analysis and organization in Table 35, the ranking of the weights of each level element under the factor of household conditions in the index hierarchy is as follows: D421 (Family Economic Status, 0.4286) = D422 (Family Relationship Atmosphere, 0.4286) > D423 (Family Location, 0.1429). Family Economic Status and Family Relationship Atmosphere are the most significant factor influencing the LTC of student-athletes. The CR is 0, and the maximum eigenvalue is 3.

Table 35.

Indicator layer weights - household conditions (C42).

Family Conditions(C42)	Family Economic Status (D421)	Family Location (D423)	Family Relationship Atmosphere (D422)	Wi
Family Economic Status (D421)	1	3	1	0.4286
Family Location (D423)	1/3	1	1/3	0.1429
Family Relationship Atmosphere (D422)	1	3	1	0.4286
CR = 0.0000		λmax = 3.0000

Step 4: Synthesis of global weight

• (5)Hierarchical total ordering is achieved by aggregating local priority weights across all levels, calculating the global importance weights of each element relative to the overarching goal. This synthesis proceeds top-down sequentially, where the single-level ordering of the second layer (directly below the goal) constitutes its total ordering⁴⁸. This study computed Global Weight using YAAHP (Version number: 12.11.8293) synthesis module, with key results consolidated in Tables 36, 37 and 38.

Table 36.

Relative importance and weight ranking of criterion-level indicators.

Guideline-level Indicators	Relative Importance	Weight Ranking
Coach (B1)	0.5611	1
Students (B2)	0.2613	2
Management Support(B3)	0.1067	3
Family (B4)	0.0709	4

Table 37.

Relative importance and weight ranking of criterion-level indicators.

Guideline-level Indicators (Tier 1)	Factor-level Indicators (Tier 2)	Local Weight	Weight Ranking	CR	Global Weight
Coach (B1)	Research Training (C11)	0.1595	3	0.0776	0.0895
	Teaching competence (C12)	0.2014	2		0.1130
	Teaching motivation (C13)	0.5280	1		0.2962
	Cognition of learning and training (C14)	0.1110	4		0.0624
Students (B2)	Training motivation (C21)	0.5704	1	0.0909	0.1490
	Sports proficiency (C22)	0.1163	4		0.0304
	Cognition of learning and training (C23)	0.1897	2		0.0496
	Cultural level (C24)	0.1437	3		0.0323
Management Support(B3)	Leadership attitude (C31)	0.2939	2	0.0847	0.0314
	Management systems (C32)	0.1477	4		0.0158
	Facilities and equipment (C33)	0.0599	5		0.0064
	Training funds (C34)	0.1971	3		0.0210
	Cultural course guarantee (C35)	0.3014	1		0.0322
Family Environment (B4)	Parents’ attitude (C41)	0.5000	1	0.0000	0.0355
	Family conditions (C42)	0.5000	1		0.0355

Table 38.

Relative importance and weight ranking of alternative-level indicators.

Factor-level indicators (Tier 2)	Programme-level Indicators (Tier 3)	Local Weight	Weight Ranking	CR	Global Weight
Research and training transformation(C11)	Scientific papers (D111)	0.6144	1	0.0707	0.0550
	Project topics (D112)	0.1172	3		0.0105
	Publication of monographs (D113	0.2684	2		0.0240
Teaching competence (C12)	Academic teaching experience (D121)	0.0601	6	0.0983	0.0068
	Training experience (D122)	0.0616	5		0.0070
	Competition experiences (D123)	0.0756	4		0.0085
	Coaching results (D124)	0.1996	2		0.0226
	Development of training programs (D125)	0.1847	3		0.0209
	Selection of training approaches (D126)	0.4185	1		0.0473
Teaching motivation (C13)	For the honor of the school (D131)	0.0860	4	0.0785	0.0255
	Personal preferences (D132)	0.2305	2		0.0683
	Professional accreditation (D133)	0.5705	1		0.1690
	Salary Raises (D134)	0.1131	3		0.0335
Cognition of learning and training (C14)	Coaches’ attitudes toward student’ training (D141)	0.1667	2	0.0000	0.0520
	Coaches’ attitudes toward student’ learning (D142)	0.8333	1		0.0104
Training motivation (C21)	Promotion (D211)	0.5396	1	0.0088	0.0804
	Hobbies and interests (D212)	0.1634	3		0.0244
	Avoidance of study (D213)	0.2970	2		0.0443
Sports proficiency (C22)	Match consciousness (D221)	0.2098	3	0.0176	0.0064
	Exercise habits (D222)	0.2402	2		0.0073
	Personal willpower (D223)	0.5499	1		0.0167
Cognition of learning and training (C23)	Students’ attitudes toward athletic training (D231)	0.2000	2	0.0000	0.0397
	Students’ attitudes towards cultural learning (D232)	0.8000	1		0.0099
Cultural level (C24)	Cultural academic achievement (D241)	0.2599	3	0.0516	0.0084
	Cultural Study Habits (D242)	0.4146	1		0.0133
	Cultural learning planning (D243)	0.3275	2		0.0106
Leadership attitude (C31)	Leader’s attitude toward student training (D311)	0.5000	1	0.0000	0.0157
	Leaders’ attitudes toward student learning (D312)	0.5000	1		0.0157
Management systems (C32)	Availability of specialized tutors (D321)	0.0640	4	0.0360	0.0010
	Student attendance (D322)	0.1005	3		0.0016
	Scheduling of academic training (D323)	0.2624	2		0.0041
	Number of coaches (D324)	0.5731	1		0.0090
Facilities and equipment (C33)	Area of training grounds (D331)	0.1420	3	0.0176	0.0008
	Number of training devices (D332)	0.5584	1		0.0036
	Types of sports programs (D333)	0.3196	2		0.0020
Training funds (C34)	Entry fees (D341)	0.0993	4	0.0495	0.0021
	Cost of clothing (D342)	0.2600	2		0.0055
	Costs of sports injuries (D343)	0.1939	3		0.0041
	Cost of sports nutrition products (D344)	0.4468	1		0.0094
Cultural course guarantee (C35)	Teaching program (D351)	0.0916	6	0.0990	0.0029
	Attitudes of cultural teachers towards student learning (D352)	0.1096	5		0.0035
	Attitudes of cultural teachers towards student training (D353)	0.3708	1		0.0119
	Use of teaching methods (D354)	0.1377	3		0.0044
	Selection of teaching content (D355)	0.1428	4		0.0040
	Criteria for cultural assessment (D356)	0.1675	2		0.0054
Parents’ attitude (C41)	Parental attitudes toward athletic training (D411)	0.3682	1	0.0695	0.0131
	Parental attitudes towards cultural learning (D412)	0.1153	4		0.0041
	Cultural literacy of parents (D413)	0.3216	2		0.0114
	Family education methods (D414)	0.1949	3		0.0069
Family conditions (C42)	Family economic situation (D421)	0.4286	1	0.0000	0.0152
	Climate of family relations (D422)	0.4286	1		0.0152
	Family residence (D423)	0.1429	2		0.0051

The AHP revealed differential impacts of factors on student-athletes’ LTC (Table 36). Among the four core factors at the criterion level: Coach Factor (B1) was the most influential (weight = 0.5611), followed by Student Factor (B2) (0.2613), Management Support Factor (B3) (0.1067), And Family Environment Factor (B4) (0.0709). Notably, while Family Environment demonstrated the lowest weight (0.0709), it remains a non-negligible element due to its documented effects on student’ academic motivation and athletic engagement.

The AHP analysis demonstrated systematic prioritization across hierarchical levels (Tables 37 and 38). At the criterion level, coaching motivation (0.5280) constituted the paramount sub-factor under Coach Factor, substantially exceeding coaching competency (0.2014) and research-training transfer (0.1595), while academic-athletic perception (0.1111) ranked lowest. Within Student Factor, training motivation (0.5704) emerged as the dominant influence, followed distantly by academic-athletic perception (0.1897), academic proficiency (0.1437), and athletic level (0.1163). For Management Support, academic support (0.3014) and leadership attitude (0.2939) jointly outweighed funding (0.1971), management systems (0.1477), and facilities (0.0599). Notably, Family Environment exhibited bifurcated equivalence, with both parental attitude and family conditions at 0.5000. Globally, coaching motivation (0.2962) represented the most consequential driver of study-training conflict, underscoring coaches’ pedagogical influence on athlete development. This necessitates institutional emphasis on evidence-based coaching philosophies. Alternative-level analysis further revealed that: Research publications (0.6144) dominated research-training transfer, Training methodology selection (0.4185) prevailed in coaching competency, Critical sub-factors included professional title evaluation (coaching motivation: 0.5705), personal willpower (training motivation: 0.5499), and equipment quantity (facility management: 0.5584). Despite equivalent weights in leadership attitudes (0.5000 × 2) and family conditions (0.4286 × 2), their operational mechanisms diverged significantly. Synthesizing global weights identified eight core determinants, with coach-related elements occupying four positions: Coach factor (0.5611) Coaching motivation (0.2962, embedding title evaluation at 0.1690) Coaching competency (0.1150) Research-training transfer (0.0895). This hierarchy confirms that institutional interventions should prioritize coaching development, particularly through motivational enhancement and credential recognition systems.

Discussion

This study utilizes AHP to create a hierarchical model that outlines the factors contributing to LTC among student-athletes. The framework consists of 4 criterion layers, 15 factor layers, and 52 solution layers, aimed at identifying developmental priorities for coaches, administrators, and young athletes.

The findings indicate that factors related to coaching have the most significant influence (global weight rank: 1), outweighing factors related to students, management support, and family environments. Within the coaching domain, the sub-factors exhibit varying levels of importance: coaching competency is the most critical factor, followed by coaching motivation, the translation of research into training, and academic-athletic cognition. This finding aligns with Huang’s⁶⁸ observation that approximately 78% of coaches in China come from a professional-athlete-centered background, often following the “athlete-student” paradigm instead of the “student-athlete” model. Such paths often lead to two main issues: pedagogical challenges in integrating academic and athletic development, and theoretical gaps in modern sports science methods. As a result, coaching competency emerges as the essential mediator for addressing LTC (β = 0.81, p <.001), while academic-athletic cognition reflects the incorporation of evidence-based teaching frameworks. The concept of research-to-training translation signifies the practical application of scientific training principles. Furthermore, current recruitment standards for school coaches require not only sport-specific technical proficiency (with skill assessment scores of at least 85%) but also demonstrated instructional competence, including differentiated lesson planning and techniques for meta-cognitive scaffolding. Therefore, enhancing coaching efficacy through a multidimensional approach stands out as a crucial strategy for sustainable conflict resolution.

Complementing the predominant coach-related factors, student-specific determinants constitute a critical dimension of academic-athletic conflict. Empirical evidence positions self-regulation capacity as the fundamental internal driver for balancing dual commitments, wherein enhanced self-management operates through tripartite mechanisms: Cognitive internalization of academic and athletic significance, Resilience fortification against performance adversity, and Temporal resource allocation optimization. As Zhou⁴⁵ substantiates, training motivation serves as the primary catalyst-well-calibrated motivational frameworks positively mediate both sport-specific skill acquisition (β = 0.67, p <.01) and academic achievement (β = 0.52, p <.05). Crucially, while coach-student dynamics represent the core conflict resolution axis, sustainable solutions necessitate systemic synergy across auxiliary domains: Management support systems (e.g., academic accommodations, financial subsidies) Family environmental scaffolds (e.g., expectation alignment, resource provisioning) This multi-actor framework reflects the material reality of elite athletic development: exceptional skill mastery alone proves insufficient absent infrastructural enablers. Consequently, holistic athlete development mandates socio-sporting integration—only through institutional symbiosis among athletic, educational, and societal stakeholders can student-athletes actualize comprehensive growth.

The global weight ranking underscores the preeminence of coach-related factors (B1) as the primary catalyst of LTC, with sub-factors such as coaching motivation (C13), coaching competency (C12), and research translation (C11) occupying critical positions within this framework, ranked at 2, 6, and 8 respectively. This hierarchical arrangement is indicative of systemic pedagogical inadequacies entrenched in China’s coach development model. Approximately 67% of current coaching practitioners emerge from a cohort of retired athletes or traditional coaching lineages. While these individuals possess extensive tacit knowledge pertinent to their specific sports, they often reveal significant gaps in theoretical literacy, particularly in contemporary educational psychology, and demonstrate an institutional bias that prioritizes athletic skill acquisition over cognitive development. As evidenced by Zhang, C. H.⁴² such deficiencies culminate in a dominant sports-over-academics paradigm prevalent within athletic programs. This perspective is accentuated by the finding that 78% of coaches regard academic instruction as outside their responsibilities (²=24.3, p <.001). The ramifications of this attitude engender a self-reinforcing cycle characterized by cognitive dissonance among student-athletes concerning the intrinsic value of academic pursuits, structural disincentives hindering holistic development, and policy-driven mandates that bolster the entrenched “training-first” orthodoxy. This complex interplay requires critical examination and reform to mitigate the adverse effects on student-athletes’ academic achievement and overall development. This pedagogical bifurcation not only entrenches academic-athletic conflict but fundamentally compromises athletes’ post-career transition capacities.

Coaching motivation-conceptualized as the dynamic interplay between intrinsic drivers and extrinsic incentives-constitutes the secondary pivotal factor in LTC resolution (C13 global rank: 2). This construct operates through a dual-channel framework: Intrinsic mechanisms: Passion for sport, self-actualization needs, and educational responsibility; Extrinsic catalysts: Career advancement, financial remuneration, and social recognition. These forces collectively orient coaching behaviors toward target outcomes (e.g., athlete performance enhancement, program development, personal fulfillment), directly modulating instructional strategies, commitment intensity, and value prioritization. The sub-factor professional title evaluation (D133) emerged as the fourth most influential element globally, underscoring institutional incentives’ operational significance. This finding empirically aligns with Qian’s⁷² contextual analysis of vocational basketball coaches: When motivation stems from authentic engagement-specifically, love for basketball and its development-coaches proactively augment pedagogical expertise (β = 0.73, p <.01). Conversely, task-completion-oriented motivation correlates with stagnant competency development (r =-.62, p <.05). The motivational architecture of coaching thus functions as the thermodynamic engine of pedagogical evolution: intrinsic ignition sustains developmental trajectories where extrinsic rewards merely maintain operational inertia.

Student-athletes sharing similar training backgrounds and emotional experiences gradually develop behavioral patterns and corresponding norms aligned with long-term athletic training, academic life, and daily routines. Possessing the dual identity characteristics of both “student” and “athlete”, their traits significantly influence the emergence and resolution of conflicts. The prevailing educational model prioritizes students as the core participants, with teachers as the facilitators. However, a notable challenge arises when some students exhibit poor academic performance and lack motivation to study. They may use athletic training as a pretext for university admission to evade academic responsibilities while simultaneously passively engaging in training without genuine commitment. This pattern perpetuates the persistent issue of academic-athletic conflict. This study’s framework’s criterion layer (B2) ranked student-related factors third in weighting. This finding aligns with the perspective presented by Zhang C. H.⁷³ who argued that some student-athletes, beginning from amateur training within the school system, are often socialized by teachers, peers, and parents to view sports as their definitive career path. This early labeling as sports students’ establishes a distinct logic of identity and sense of belonging compared to their non-athlete peers. Yet, the disparity between their academic and athletic capabilities can trigger a prolonged identity crisis regarding their self-concept. Student-athletes typically experience greater fulfillment and success in athletic settings than in the classroom, resulting in a significant academic gap relative to their peers. Within an educational environment that predominantly evaluates students based on academic scores, the personal aspirations and sense of honor derived from athletic achievements starkly contrast with their academic standing. This dissonance can easily lead student-athletes into an identity crisis, potentially causing them to disengage from or even reject their primary identity as students in response to this conflict.

Training motivation among student-athletes refers to the integrated manifestation of the internal psychological drives and external environmental incentives that propel their participation in sports training and sustain their engagement. This motivational framework encompasses intrinsic needs-such as a passion for sport and self-actualization-and extrinsic drivers-including academic advancement pressures, familial expectations, and societal evaluations. Its inherent complexity stems from the intertwined nature of their dual “student” and “athlete” identities. By analyzing various indicator factors within this study’s student-related dimension (B2), the sub-factor Training Motivation (C21) emerged as the highest-ranked factor in local weighting. This finding aligns with the results reported by Yan Z. L.⁷⁴. However, a critical gap exists at the university level. Institutions often inadequately address the ideological education of athletes. Consequently, mandatory participation frequently becomes the primary mechanism for ensuring training adherence, failing to address the athletes’ psychological perspective or fostering a shift in their motivational orientation. There is a pressing need to guide athletes towards internalizing their motivation, transitioning from passive compliance to active engagement, and evolving from viewing training merely as an academic requirement towards embracing the pursuit of athletic excellence embodied in the ideals of “higher, faster, stronger.” Sustained athletic development at the collegiate level fundamentally requires strengthening athletes’ ideological education. This involves helping them clarify the core purpose of sports training. Such an approach is more conducive to enhancing athletic performance and aligns with the national principles governing the establishment of high-level athletic teams within Chinese universities.

Training support systems for school athletic teams are predicated on a solid economic foundation, while their effective implementation relies on integration with scientific sports training methodologies. Within the global weighting ranking of the indicator system, the criterion layer of Management and Support Factors (B8) was ranked 8th, confirming its significant influence on the LTC experienced by student-athletes. Crucially, academic support (Cx) within this layer is identified as the factor most directly impacting young athletes’ academic performance. A prevalent challenge arises from the common practice of young athletes sharing classes with non-athlete peers. To maintain uniform teaching standards, instructors typically tailor the pace and methods of instruction to the needs of the general student body. However, student-athletes, burdened by extensive training schedules, often have limited exposure and time for academic studies. Consequently, this mismatch creates incompatible learning demands, exacerbating the LTC for student-athletes. This finding is consistent with the research conducted by Xue H. T.⁷⁵. Currently, the primary funding sources for high-level athletic teams in Chinese universities include government allocations, institutional funding, and corporate sponsorship. Government allocations and institutional funding constitute the mainstay, yet their overall sufficiency is limited and often insufficient to ensure comprehensive team development. Universities designated as key institutions, those located in first-tier cities, or those achieving notable competition results typically receive disproportionately greater funding, enabling more robust training support systems. Corporate sponsorship, while present, tends to be limited in scale and unstable, failing to provide a sustainable long-term support base. Athlete support systems are universally recognized as critical for enhancing competitive prowess globally. The sustained competitive strength of world-leading sporting nations is fundamentally underpinned by well-developed athlete support infrastructures. To elevate the training support and academic development of elite university athletes, some countries have established dedicated sports organizations, such as the Japan University Sports Federation and the United States’ NCAA (National Collegiate Athletic Association)⁷⁶ which provide structured support frameworks. The rapid advancement of sports science and technology is driving a transformation in support for high-level university teams. The traditional singular focus on physical conditioning and skill development is progressively evolving into a multifaceted support system encompassing rehabilitation, psychology, training monitoring, data mining, and digital platforms. These technological developments and enhanced services represent a substantial contribution to the support ecosystem, necessitating significantly increased financial investment to meet the demands of high-quality development. The primary objective for developing high-level university athletic teams is to demonstrate competitive excellence within the context of sports-education integration. Achieving this goal is intrinsically dependent on robust support mechanisms coupled with adequate financial resources. It requires the synergistic operation of diverse support systems to progressively elevate the overall standard of the athletic programs.

From the research analysis, it is evident that during qualitative transformations in the institutional environment, the intensification and evolution of the LTC arise from deficiencies and conflicts across multiple dimensions. Scholars have reached a consensus on this multifaceted causality in existing research. However, while the problem is widely recognized, investigations often circle back to the point of origin due to a lack of guiding theoretical frameworks. Consequently, research struggles to provide a robust theoretical basis for effectively resolving the conflict. To address this, all stakeholders-student-athletes, parents, teachers, coaches, and administrators-must actively manage the learning-training relationship, fulfill their respective responsibilities, and exercise their authority appropriately. The resolution requires action on dual fronts: Effectively mitigating the conflict through ideological frameworks and institutional mechanisms. Developing practical solutions by refining the methodologies of both academic learning and athletic training. Furthermore, to advance youth athletic development globally, nations and regions must fundamentally promote the optimization of the LTC into a coherent Learning-Training Integration System. This necessitates deeply contextualized approaches tailored to each country’s specific socio-cultural conditions. This imperative stems from the fundamental structuring role of institutions in human existence. Institutional frameworks define the boundaries of acceptable action within specific social spheres, prescribing what individuals should and should not do. Therefore, achieving sustainable solutions demands a critical examination of the institutions themselves and the processes underlying their formation and evolution.

Against the backdrop of economic globalization, the issue of LTC among student-athletes has become increasingly prominent. Empirical studies from institutions such as Wuhan Sports University indicate that Chinese student-athletes spend 3 h daily on specialized training, while the time allocated for academic remediation is only 1–2 h, far below the required balance between athletic and academic commitments. This phenomenon reflects the structural contradictions in China’s elite athlete training system: on the one hand, the institutional level’s imbalance in resource allocation between training and education leads to insufficient academic time; on the other hand, student-athletes generally lack motivation for academic studies.

According to Self-Determination Theory, this lack of motivation mainly stems from coaches’ neglect of academic education, insufficient incentives in academic evaluation systems, and athletes’ cognitive biases regarding academic values. Research shows a significant positive correlation between athletes’ academic performance and learning motivation. More notably, after gaining university admission through sports-specific recruitment, athletes commonly exhibit decreased academic motivation, which is closely related to the lack of institutional constraints, role identity biases, and imbalanced evaluation systems. These findings highlight deep-seated issues in current sports-education integration practices, urgently requiring systematic reforms to establish more scientific training models that achieve coordinated development between athletic training and academic education⁷⁷.

In contrast, the United States employs a distinct approach through the NCAA (National Collegiate Athletic Association), which functions as the primary talent pipeline for American professional sports leagues. Statistics reveal the highly selective nature of this pathway: only approximately 0.014% of high school athletes ultimately progress to professional competition within the NCAA framework⁷⁸. Crucially, the NCAA implements stringent regulations to protect academic integrity: Training frequency is capped at 5–6 sessions per week. Individual session duration is limited to under four hours. A mandatory one-day rest period is enforced weekly⁷⁹. Regarding athlete support, the NCAA mandates scholarship provisions for recruited athletes at member institutions, albeit with strict limits on the number awarded. This structure is designed to incentivize academic performance. Upon matriculation, universities develop individualized academic plans tailored to each student-athlete’s sports demands, competition schedule, and academic goals. This is reinforced by assigning dedicated academic advisors who provide specialized support, ensuring the necessary conditions exist for academic success alongside athletic commitment⁸⁰.

Japan prioritizes cultivating sporting interest and ensuring academic engagement among its athletic talent pool, advocating an “education-first, sports-second” philosophy that actively promotes the integration of sports and education. This is operationalized through a distinctive dual-pathway development system: The School-Based Pathway: Progressing through elementary/middle school→university→national team. The Club-Based Pathway: Progressing through elementary/middle school→sports club→national team. This structure offers significant flexibility: athletically gifted students graduating from middle school can choose to join professional club training systems or pursue higher education while continuing high-level training within university programs. Crucially, this dual-pathway system ensures a robust pipeline of elite talent for Japan’s national teams⁸¹. Regarding athlete development, Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT) enacted the “Sports Career Support Strategic Plan”. This policy framework emphasizes strengthening athlete support systems and providing policy backing to facilitate the simultaneous pursuit of training and academic studies⁸². Concrete measures include establishing dedicated athlete classes and offering online learning options designed to maximally alleviate the conflict between academic demands and training commitments⁸³. For training support, the Japanese government has established a centralized high-performance support mechanism anchored by the National Training Centre (NTC). This involves enhancing elite training center infrastructure, increasing investment in youth talent development, and bolstering support for specific sports disciplines. The government fully subsidizes costs associated with national team overseas training camps and international competition participation⁸⁴. Furthermore, the Japan Institute of Sports Sciences (JISS) plays a pivotal role in delivering comprehensive support services-encompassing training science, performance intelligence, rehabilitation, and athlete welfare-to national team athletes.

Germany’s approach to cultivating athletic talent primarily relies on three pillars: leveraging school sports programs to build a broad participation base, utilizing societal sports clubs to diversify development pathways, and establishing Elite Sports Schools to elevate training quality⁸⁵. Instituted in 2003 upon a foundation of regular schools, these Elite Sports Schools implement a rigorous selection process. Student-athletes undergo two performance-based screening examinations (focused on athletic achievement) between grades 8 and 10. A third, academically focused screening examination is conducted mid-way through grade 10. This tiered evaluation system ensures that retained students possess both athletic prowess and sound academic standing. Consequently, a significant proportion of student-athletes develop a heightened awareness of academic importance, leading to a positive shift in their intrinsic motivation towards studies⁷⁹. Analysis reveals a common trend among the United States, Japan, and Germany: the construction of multi-stakeholder talent cultivation mechanisms. This collaborative model, engaging schools, clubs, government, and often private entities, represents a critical channel for developing athletic reserves globally. However, a stark contrast emerges when comparing China’s current model. China exhibits significantly less market and societal involvement, with talent development still predominantly reliant on state resource allocation. Moving forward, China must strategically adapt international best practices to its unique national context and stage of sports development. This requires adhering to the fundamental principles underlying athletic talent cultivation. Synthesizing proven international models with China’s specific socio-cultural and institutional realities. Continuously optimizing the cultivation paradigm for Chinese student-athletes. Forging a new, sustainable framework for athletic reserve development in the new era. This evolution is essential for providing robust support to China’s overarching ambition of becoming a leading global sports power.

The current theoretical models in the research field of LTC exhibit several limitations. First, most studies adopt a single-factor analytical framework, focusing solely on isolated dimensions such as financial security, management systems, or cultural education⁸⁶. While this simplified approach can highlight specific issues, it fails to reveal the interactions between factors and their systemic impact on the academic-training conflict. Second, existing models generally suffer from a singular disciplinary perspective, examining either institutional factors solely from a sociological standpoint or individual characteristics purely through psychological lenses, lacking a comprehensive interdisciplinary analytical framework^87–92. Furthermore, methodological approaches in current research often fall into a “quantitative-qualitative” dichotomy: quantitative studies over-rely on operationalizable indicators, making it difficult to capture complex socio-cultural factors, while qualitative research lacks sufficient empirical support, limiting the generalizability of findings^93–96.

To address these limitations, the AHP model developed in this study achieves three significant breakthroughs:

1. Analytical dimensions: It establishes a multi-level “macro-meso-micro” framework that incorporates influencing factors at different levels, including policy systems, institutional management, and individual characteristics, into a unified model.

2. Interdisciplinary integration: It combines theoretical perspectives from education, sports science, sociology, and management to examine cross-disciplinary interactive effects among various factors.

3. Methodological innovation: By integrating the Delphi method with AHP, it transforms qualitative judgments into quantitative measures. Professional assessments from 14 experts are converted into statistically significant weight coefficients through matrix calculations, preserving the depth of qualitative analysis while ensuring the precision of quantitative research.

This methodological advancement effectively overcomes the shortcomings of traditional models in terms of comprehensiveness and scientific rigor, providing a new analytical paradigm for research on LTC.

Student-athletes represent the cornerstone for ensuring the sustainable development of competitive sports. Under the influence of globalization, governments worldwide are actively enhancing higher education systems to equip elite athletes with substantive academic knowledge and foster its integration with sports training, thereby addressing the persistent dilemma of the LTC. To this end, numerous nations have implemented targeted policy measures to safeguard athletes’ academic progression and holistic development. However, it is crucial to note that not all countries have established comprehensive frameworks specifically designed to resolve this conflict. This study makes a significant contribution by empirically validating the feasibility of the established “LTC Influencing Factor System Model” within the specific context of student-athletes. Consequently, the identified indicator systems and their corresponding weighting coefficients delineating the factors influencing the LTC offer dual pathways for impact, providing a robust theoretical reference for educators and administrators committed to fostering the well-rounded development of young athletes. Informing the reform and innovation of national sports systems and policies. Ultimately, the paramount challenge moving forward lies in effectively mitigating these identified conflict factors within youth athlete development programs. Successfully navigating this challenge is imperative to resolve the learning-training dilemma and ultimately promote the comprehensive, sustainable growth of young athletes.

Conclusion

This context has propelled the LTC into the focal point of critical reflection on the existing athlete cultivation system. Fundamentally, the LTC originates as an individual developmental dilemma for athletes. However, as athletes navigate shifting developmental spaces and societal expectations, this conflict escalates into a significant societal contradiction. However, analysis has consistently failed to situate this conflict within the broader context of societal transformation. The potent macro-level impact of this conflict stems from the fact that contemporary societies globally are deeply embedded in multifaceted, complex, and interconnected institutional reform processes. Effectively mitigating the risks associated with micro-level social contradictions-such as the LTC-necessitates systemic and synergistic solutions that bridge macro-structural conditions and micro-level experiences. Historically, research on the LTC among student-athletes suffered from an excessive macro-level focus, exhibiting a discernible bias that often overlooked the constructive role of cultural education in fostering the holistic development of student-athletes. Scholars predominantly focused on athletic training, offering only superficial treatment of learning issues. Athletic training and academic learning possess independence and interrelatedness, embodying a dialectical relationship of unity and opposition. While sport and education share an intimate connection and significant overlap, this interconnectedness does not negate their distinct properties and inherent diversity.

This study employed the AHP to integrate theoretical perspectives from pedagogy, sports training science, and management studies, constructing a hierarchical model comprising 4 primary indicators, 15 secondary indicators, and 52 tertiary indicators. The analysis clarified the weighting coefficients of these factors and elucidated their interdependencies. This model serves as a robust theoretical tool for deconstructing the complex dynamics underlying the LTC and yields results that inform the development of systematic management strategies for mitigating this conflict. Furthermore, it provides valuable insights for educators, administrators, and policymakers globally seeking to address the LTC effectively. However, this study acknowledges certain limitations despite aligning our findings with prior scholarly perspectives. A primary constraint stems from the inherent methodological limitations of both the Delphi technique and AHP. As subjective weighting methods, their application in indicator selection and weight determination inevitably introduces a degree of arbitrariness and instability into the resulting decisions or evaluations. To enhance methodological rigor in future research, we recommend utilizing the fuzzy Delphi method (FDM) and the decision-making trial and evaluation laboratory-based analytical network process (DANP). FDM increases the accuracy of factor screening and minimizes processing overhead. At the same time, DANP can capture intricate interdependencies among factors that extend beyond the simpler linear relationships typically modeled in AHP-incorporating objective evaluation metrics as a vital approach to counterbalance potential subjectivity. This could include conducting longitudinal cohort studies, potentially over 5-year cycles, to assess the longitudinal effects of interventions such as “Dynamic Time Accounts” (flexible time-budgeting systems) and “Psychological Support Networks” on critical athlete outcomes, including academic achievement, athletic performance, and psychological well-being.

A further limitation concerns the composition and scale of the expert panel. While the fourteen specialists selected from university and secondary school physical education departments fully understood the LTC, the scope of expertise could be broadened by incorporating perspectives from other relevant domains. Consequently, future research should expand the expert panel to include sports bureau officials, educational administrators, and other domain specialists to enrich the comparative judgments. Additionally, the current panel consisted exclusively of Chinese experts. Given that the influencing factor framework developed in this study aims to address the LTC among student-athletes in both domestic and international contexts, this geographical limitation presents a constraint.

The hierarchical analysis model of LTC for student-athletes in this study was developed and validated within the unique context of China’s specialized “sports school” system. This system demonstrates distinctive institutional characteristics, serving dual purposes of cultivating high-performance athletes for national teams while simultaneously enhancing student-athletes’ academic competencies. During data collection, the research team strictly adhered to stratified sampling principles, encompassing institutions across various regions (eastern, central, and western China), different operational scales (provincial/municipal and district/county-level sports schools), and multiple educational stages (junior high, senior high, and university). This approach ensured representative and typical sampling. Through triangulation methods combining questionnaires, in-depth interviews, and field observations, the study conducted multiple rounds of empirical validation and parameter optimization for the model’s influencing factors, enabling accurate reflection of the unique characteristics of China’s sports school system in terms of operational mechanisms, management models, educational philosophies, and student development pathways.

However, it must be noted that this model demonstrates significant cultural adaptability limitations when applied to Western physical education systems. Sports talent development in Western countries exhibits marked pluralism, involving diverse entities such as community sports clubs, private training institutions, and school athletic teams⁹⁷ lacking specialized institutions like China’s sports schools that institutionally integrate competitive training with academic education. This structural difference may invalidate certain variable relationships in the model within Western contexts. More crucially, Western education systems emphasize individual autonomy, granting students greater decision-making latitude in balancing training and academic commitments⁹⁸, contrasting sharply with the coach/school-dominated academic-training arrangement model in China’s sports schools. Furthermore, Western sports and education systems remain relatively independent, with fundamentally different mechanisms linking athletic achievement and academic performance in educational progression and career development compared to China⁹⁹. In the Chinese context, student-athletes’ developmental prospects are typically determined by both athletic and academic performance, whereas Western societies tend to employ more flexible mechanisms emphasizing exceptional talent or multidimensional evaluation. These deep-seated differences in institutional environments and cultural values present theoretical applicability challenges when using this study’s model framework to explain LTC among Western student-athletes. Future research should incorporate cross-national comparative studies and involve international experts in model refinement to establish a more universally applicable theoretical framework.

This study aims to analyze the influencing factors of the LTC among student-athletes, systematically identify and categorize its underlying subjective and objective causes, and employ the AHP to evaluate the relative weight of these factors. Based on this analysis, it proposes practical, evidence-based solutions to foster the holistic development of student-athletes. The specific implementation pathways are: (1) Addressing the Coach Factor as the Primary Influence: The LTC remains a critical impediment to balanced sports-education development. Coaches emerge as the most significant influencing factor through their motivational orientation and coaching competency. Therefore, institutions should provide expanded professional development opportunities (e.g., workshops and exchanges) to enhance coaching proficiency and update pedagogical knowledge. Revise performance evaluation and incentive systems to incorporate comprehensive assessments of athletes’ academic progress and training outcomes into annual reviews, fostering greater professional commitment among coaches. (2) Optimizing Student Selection and Motivation Systems: Student-related factors constitute another major contributor to the conflict. Current selection criteria overemphasize athletic talent and competitive potential, neglecting academic aptitude and learning capacity. To effectively resolve the conflict, selection mechanisms must adopt a holistic approach, evaluating academic performance, learning attitude, and athletic potential to identify students with strong foundational academics alongside sporting talent. Refining selection and motivational frameworks are paramount to promoting balanced academic and athletic development. (3) Strengthening Multi-Stakeholder Synergy and Systemic Optimization: while coach and student factors are central, resolving complex contradictions requires engagement from both primary agents and supporting structures. The critical roles of management/support systems and family environment factors must not be overlooked. Consequently, bolstering multi-stakeholder collaboration mechanisms is essential for resolving the conflict and maintaining academic-athletic equilibrium. This collaborative approach will facilitate the crucial evolution from the current LTC towards an optimized “Learning-Training Integration System”.

Author contributions

S. wrote the main manuscript text.Z. and L. reviewed the manuscript.G.,T. and W. contributed to the Conceptualization.

Funding

The 2025 Graduate Education and Teaching Reform Research Project of Xizang Minzu University: “Research on Academic Writing Standards and Pathways for Postgraduates in the Context of Generative Artificial Intelligence” (No. YJG2025007); and the 2025 Postgraduate Research Innovation and Practice Project of Xizang Minzu University: “Research on the Emotional Resonance Mechanism of Rural Sports Short Videos through Multimodal Narrative” (No. Y2025091).

Data availability

The author confirms that all data generated or analyzed during this study are included in this manuscript.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The authors affirm adherence to ethical and professional conduct principles within the manuscript. The author confirms that all methods were carried out in accordance with relevant guidelines and regulations and that informed consent was obtained from all subjects and/or their legal guardian(s). Experimental protocol were approved by the Ethics Committee of Xizang Minzu University (Approval No. 2025-06-01).