Improving Doctoral Educator Development: A Scaffolding Approach
Bibliographic record
Abstract
In response to a need for improved training of business school teaching, this research explores US doctoral programs in management and finds a need to purposefully embed scaffolding—the process of gradually enabling the doctoral student to take on more challenging aspects of teaching—into doctoral program design. We also recommend a more influential role to be played by professional organizations to address doctoral educator development. As we followed a grounded theory approach, our methodology started with an analysis of program marketing documents and materials followed by behavioral event interviews (BEIs) and perceptual interviews (PIs) with doctoral students in management. Following coding, we reviewed the literature on doctoral education to explore how our emergent data mapped against prior research. By also taking into consideration the lived experience of students, the study data provides evidence that doctoral programs are not properly designed to support educator development. We discuss our findings related to what programs do to support students and what students do to support themselves. Theorizing from our data, we present our model that illustrates how programs could embed scaffolding to support programs’ commitment to develop future educators.
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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 | 0.073 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".