Understanding Student-Teachers’ Performances within an Inquiry-Based Practicum
Bibliographic record
Abstract
The role of an inquiry-based practicum in the education of future teachers has been identified as a key component to foster student-teachers’ abilities to face problems, try to solve them, work on doubts and produce situated and valuable learning from their own practices (Cochran-Smith & Little, 2001; Beck, 2001). The interaction between mentors and student-teachers involved in this process requires feeding the mutual understanding to collaboratelly make decisions to work on difficulties. In this reflective study, the practicum was the perfect scenario to evaluate how student-teachers and mentors engaged in inquiry-based work to face problems on a daily basis in schools in the context of teachers education. This article shows how a group of 12 students-teachers and their mentors experienced difficulties within an inquiry-based practicum in an undergraduate program while conducting an instructional intervention, designing, implementing and applying a project relevant to their teaching context. Given the qualitative nature of this study, some stages of the process were analysed over a period of a year bringing as a result relevant insights to enhance their teaching practicum-process in Colombian public schools.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".