The Science Behind Powerful Questioning: A Systemic Questioning Framework for Coach Educators and Practitioners
Bibliographic record
Abstract
At the heart of the coaching process is the core competency of questioning, often referred to as powerful questioning.Coach educators and trainers diligently teach students the importance of asking questions (versus giving advice) during coaching sessions and teach them to structure questions appropriately (such as using open versus closed-ended questions).Still, coaching students struggle with knowing what questions to ask and when during their work with clients.Although many students search for a list of so-called magic coaching questions, I contend that coaches instead need a framework of questioning to use when coaching a client.A questioning framework could help educators teach the science of questioning as a means for developing coaches' professional judgment, thereby helping coaches make better-informed choices about what types of questions to ask clients during coaching sessions.This paper presents an evidence-based conceptual framework called the Systemic Questioning Framework.Application of the framework during a coaching conversation may increase the coach's confidence and competence when making decisions regarding how to shape questions in the moment in response to the client, enabling better coaching outcomes.
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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 | 0.097 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".