What Intercollegiate Athletics Coaches Wish Faculty Knew: Implications for Curriculum and Instruction
Bibliographic record
Abstract
Collegiate athletics coaches play a vital role in the lives of student-athletes and regularly interact with the membersof their teams more than faculty given the nature of athletics practice schedules compared to academic classschedules. Although the primary purpose of university attendance at all universities is pursuit of academic degrees,student-athletes receive broad non-academic, life-skills oriented education from athletics coaches. Typically, teachingfaculty at American colleges and universities hold terminal degrees in their fields, but unlike internationaluniversities, faculty in the U.S. are not required to have any particular training in pedagogy. Due to the enormousamount of time athletics coaches spend with student-athletes, coaches, by nature must be effective communicators,effective motivators, effective teachers, and effective ethical models for their student-athletes to a degree notnecessary for faculty members. The purpose of this paper was to gather recommendations from coaches for facultymembers regarding needs of student-athletes, and a comparison of the perception of student-athlete needs betweencoaches and faculty members. We employed a mixed methods convergent parallel design. We administered aquestionnaire that included both an open-ended response section to what the respondent wished faculty knew withrespect to student-athlete success, and three Likert scale questions related to confidence in what faculty knew or didwith respect to student-athlete academic needs. Based on the thematic coding of the responses by coaches, andquantitative analysis of the Likert scale questions, recommendations for faculty regarding curriculum and instructionare given in the discussion section.
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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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".