Flipped Classrooms: An Introduction for Coaching Candidates in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it