Flipped Classrooms: An Introduction for Coaching Candidates in Higher Education
Bibliographic record
Abstract
Former UCLA Bruins head basketball coach and 10-time national champion John Wooden is arguably the most revered coach in any sport and in any time. Yet, in his own words, he suggested “I’m no wizard, I am a teacher” (Gallimore, 2006, np), and that he learned to coach by applying what he learned as a high school English teacher (Gallimore, 2006). Similarly, Côté and Gilbert’s (2009) conceptual model of coaching identifies categories of knowledge coaches need, including professional knowledge as “declarative knowledge in the sport sciences, sport-specific knowledge, and pedagogical knowledge with accompanying procedural knowledge” (p. 310). Thus, inspired by Coach Wooden, and following Côté and Gilbert (2009), the purpose of this workshop is to enhance coaches’ pedagogical knowledge by introducing coaching candidates at post-secondary institutions to the flipped classroom (FC) approach. In higher education, FCs have been shown to improve student engagement, motivation, satisfaction, and creativity (Al-Zahrani, 2015; Chen, Lui, & Martinelli, 2017; Herreid & Schiller, 2013; Rotellar & Cain, 2016) – outcomes that may be especially important to coaches. Participants in this workshop will learn about FCs in an interactive 90-minute session, collaborating with peers to address issues that are relevant to their teams, and incorporating FC principles to improve their teaching and enhance student-athlete satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.020 |
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".