How graduate students are supported in their teaching
Bibliographic record
Abstract
Purpose – To provide a cohesive framework for understanding how supports co-occur and interact to impact graduate students’ teaching experiences, this paper systematizes the multi-layered context in which institutions, departments, faculty, peers, and individuals provide support. Previous studies on graduate students’ teaching focussed on specific programs, initially to describe them, and more recently to assess their outcomes. However, this piecemeal approach misses the complexity of graduate students’ contexts. Design/methodology/approach – Through a literature review of existing supports for graduate students’ teaching, the need for a contextual framework was clearly identified leading to its development and application to provide a cohesive categorization of supports. Findings – The review of existing literature identified graduate students’ supports and needs for support across all layers of their higher education context. Practical implications – This new framework offers a theoretical grounding for teasing apart the intertwined influences on graduate students’ teaching development. Higher education professionals seeking to demonstrate value for money may be disappointed by evaluations of formal programming revealing lower than expected changes in practice despite promising growth in graduate student’s conceptions of teaching. By considering additional influences and barriers to graduate students implementing newly learned teaching practices, potential conflicts may be revealed and addressed, and enabling influences identified and increased. Originality/value – Missing from existing literature is consideration of the multiple co-occurring influences on graduate students’ development, and an examination of how the various sources of support interact. This framework reveals potential interactions and contradictions that are important to consider when creating and evaluating supports for graduate students’ teaching.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".