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.
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.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".