Student Cohorts in Teacher Education: Support Groups or Intellectual Communities?
Bibliographic record
Abstract
Recent initiatives in preservice teacher education have experimented with cohorts as a way to create supportive ties among peers, mutual intellectual support, and a sense of professionalism. The initiatives reflect a belief in collaboration, one expressed in educational literature supporting related forms of collaboration in education, such as descriptions of learning communities and of cooperative learning in classrooms. A review of this literature suggests a number of cautions, however, about the value and success of collaboration among colleagues, and in any case does not address the needs of cohorts in teacher education directly. To begin closing this information gap, the authors studied one particular teacher education program organized around cohorts. Sixteen students in a preservice bachelor of education program were interviewed at length about their experiences with cohort peers. Their comments were analyzed for recurring themes. Overall, they expressed a liking for cohort organization, but to varying degrees. Appreciation of the cohorts was focused almost entirely on the immediate social support provided by the cohort as a whole. It had little to do with its potential for academic stimulation, long-term professional ties, or individual personal friendships. Students reported a number of factors that affected their liking for cohorts, such as their age, prior family or job responsibilities, religion, and geographic distance of their home from the university.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".