The good teacher form children’s perspective
Bibliographic record
Abstract
The research explores the characteristics that define a good teacher from the children's perspectives. It employs the Mosaic Approach (MA) and involves all five-year-old children in a preschool setting, using drawings, photographs and semi-structured conversations prompted by guiding questions. In recent years, the MA has been used to explore children’s views on early childhood education (Koçyiğit, 2014). Notably, some studies have investigated children’s quality standpoints about their teachers (Harcourt and Mazzoni, 2012) and how preschool educators’ gender shapes their roles (Rentzou, 2023). The research is informed by the Reggio Emilia Approach (Edwards et al., 1993) and the MA (Clark and Moss, 2014). Central to this framework is the value placed on documentation and children’s active, participatory role in the learning process. The research adopts an interpretative paradigm and follows a qualitative methodology (Cohen et al., 2011). Photographs, drawings, and excerpts from interviews/discussions were coded, with a codebook guiding the overall data analysis. Inspired by Rentzou (2023), a participatory consent form was developed to address ethical concerns regarding children's right to freely express their views (UNCRC, 1989, Article 12). The form presents the four elements of the MA and allows children to choose how they wish to participate. As previously identified by Harcourt and Mazzoni (2012) teacher quality is linked to respectful relationships. Similarly, this research shows that children value kind teachers who do not scold and who are caring. These insights can inform everyday teaching practices. Discussions with educators and comparisons with university programs will be undertaken to promote meaningful changes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".