Re-Structuring Preservice Teacher Education: Introducing the School-Community Integrated Learning (SCIL) Pathway
Bibliographic record
Abstract
Reviews into teacher education call for new models that develop preservice teachers’ practical knowledge andskills. The study involved 9 mentor teachers and 14 mentees (final-year preservice teachers) working in a newteacher education model, the School-Community Integrated Learning (SCIL) pathway, and analysed data from aLikert survey with extended written responses. Despite minor discrepancies between mentors and mentees’agreement on the experiences mentees received during the SCIL pathway, findings indicated 100% agreement on 8of the 27 survey items for addressing the practicalities of learning how to teach. Indeed, 70% or more menteesagreed that they had a range of experiences across the five categories (i.e., personal-professional skill development,understandings of system requirements, teaching practices, student behaviour and reflective practices). Writtenresponses outlined that preservice teachers began to recognise the breadth of teachers’ roles and responsibilities.The SCIL pathway was noted as a cost-effective model that assisted to fill some gaps indicated within generalpracticum and internship models.
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".