Supporting teacher candidates to become collaborative teaching professionals: developing professional capital through a collaborative inquiry-based community of practice
Bibliographic record
Abstract
Purpose This paper draws on data from a research project that examined the impact of a community of practice (CoP) model of teaching practicum that engaged teacher candidates in collaborative inquiry projects based on self-identified problems of practice that emerged during their practicum experiences. Design/methodology/approach A qualitative approach was adopted to better understand the ways in which the CoP served as a support mechanism for teacher candidates to develop social capital during internship. Data collection included anecdotal observation notes, student postings in online discussion forums, and a one-hour post-project focus group. Data analysis was rooted in phenomenology (Lin, 2013) and was guided by the four pronged coding process outlined by Bicudo (2000). Findings As the paper illustrates, the CoP created rich opportunities for teacher candidates to cultivate social capital, which positively impacted their human and decisional capital. Relatedly, teacher candidates demonstrated an enhanced sense of collective efficacy and an understanding of the significance of collaborative professional cultures on their continued growth as members of the teaching profession. Originality/value While a number of studies have considered various factors impacting the professional capital of practicing teachers, the development of professional capital amongst interning teachers remains as an under-explored area in the research literature.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".