Analysis of Learning Obstacles for High-Performance Student Athletes in Chinese Universities During the COVID-19 Pandemic
Bibliographic record
Abstract
Efforts to control the spread of the coronavirus pandemic have led to the suspension of mass gatherings, including in universities, which have strong population flows and high aggregation. Consequently, universities have faced significant challenges in operational mechanisms. Against this background, students’ learning situation has attracted widespread attention. However, relatively little discussion has occurred on high-performance student athletes (HPSAs) who excel in sports but underperform in academic courses. A country’s sports strength is equal to its citizens’ overall physical fitness level; the higher the national physical fitness level, the stronger the sports strength, and the more developed the economy. Based on attribution theory and using a snowball sampling method, this study selected 15 HPSAs from Chinese universities for semi-structured interviews . The initial survey subjects were artificially selected, and then qualified survey subjects were provided by sports talents. Then, these people provided the third batch of survey objects, and so on, and the sample gradually increased, just like a snowball, and the respondents were all representative sports students. Material from the initial 12-person interviews was first individually coded, analysed, conceptualized and reviewed, followed by analysis of 3 material from the second round of interviews. No new concepts were developed in the analysis of the second round of interviews, thus achieving theoretical saturation and allowing the construction of a coherent explanatory profile (Corbin & Strauss, 2008).From the interviews, we extracted themes related to the learning obstacles student-athletes have faced during the pandemic and analyzed the factors at play during the pandemic. Four types of factors were revealed: pandemic, personal, school, and career development factors. Among them, epidemic factors and personal factors are relatively high.To help student-athletes overcome learning obstacles and balance their learning and training, it is necessary to increase emphasis on academic courses, consolidate knowledge and skills training, reasonably arrange study and training time, enhance interpersonal communication skills, and effectively plan for career development, To discover the various troublesome factors faced by HPSAs in learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".