databases, and practice planning
Bibliographic record
Abstract
Every coach faces the daunting task of creating challenging lesson plans for their team every time they enter the venue. Computers have proven effective in the role of computer-assisted instruction, but there is little evidence to substantiate their effectiveness when used as a tool for coaches. Researchers and coaches at the University of Calgary, Sport Technology Research Centre have developed a model for interactive coaching which includes an education component on planning a practice, a drills database using actual videos of the drills, and a practice planner linked to the drills. An interactive CD-ROM on volleyball was developed using this model. The program includes over 400 full video drills, 250 educational practice notes, the ability to modify the drills, a glossary with 130 volleyball related terms, and a customizable practice planning tool. In order to assess the effectiveness of the program, 24 volleyball coaches at various competition levels were selected to attend a two-hour workshop to learn how to use the program. Following the workshop, the coaches were asked to use the program in planning their daily practices. Pre- and post-workshop testing, consisted of questionnaires which evaluated coaches attitudes towards using computers in their planning, and the suitability of using technology-based tools in their coaching. This paper discusses the potential of technology tools in coaching, the general coaching/technology model, and the Interactive Volleyball CD-ROM as a practical example of the theoretical model. A preliminary analysis of the coaches attitudes toward the technology is included. 4
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".