Preservice teachers’ perception of longitudinal child development field coursework at a university-affiliated teaching school
Bibliographic record
Abstract
Background: Most field coursework in teacher education (TE) programmes do not incorporate extended in situ interaction with individual children in a classroom. Furthermore, child development theory (from coursework) is not taught in tandem with students’ extended periods of practicum placement in schools.Aim: This study sought to determine preservice teachers’ perception of their longitudinal study of children’s development and learning in a clinical setting at a university-affiliated teaching school.Setting: This study focusses on two undergraduate primary school TE programmes at an urban university in Johannesburg, South Africa. These programmes incorporate six semester courses on child development with extended clinical field experience at a teaching school on campus. Each student teacher follows a particular child’s development and learning over four years of their undergraduate coursework.Methods: This was a qualitative descriptive study with some cross-sectional data. Data were collected from 120 undergraduate students, by using anonymous questionnaires and four focus group interviews.Results: Students reported that they had gained in-depth learning of child development during their longitudinal pairing with an individual child and that assigned observation activities had taught them to recognise, and support, nuanced differences in a child’s learning.Conclusion: Students regard their longitudinal interaction and learning in the clinical setting positively. Future research should focus on the long-term value of the clinical model with insights from students who have graduated from the programme and are in the teaching profession.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".