Timewise: Improving pupils' understanding of historical time in primary school
Bibliographic record
Abstract
The understanding of historical time is an essential aim for the subject of history. However, evaluations in the Netherlands show that that too few pupils at the end of primary school, reach a sufficient understanding of historical time, despite the implementation of the ten-era curriculum (2006), which aims at supporting pupils in their orientation in time. Therefore, the central question of this dissertation is: How can pupils’ understanding of historical time in primary school be improved? Five successive studies focus on the conceptualization of the understanding of historical time; on pupils’ development; on analyses of curricula on historical time in England and the Netherlands; on measurements of pupils’ performances; and on effective aspects of teaching and teacher training. This dissertation yielded in a model with objectives and stages on pupils' development in the understanding of historical time, an instrument to assess pupils' development and an overview of types of problems that might arise in pupils' reasoning while placing historical phenomena in time. Findings of the intervention study indicate that pupils’ development in the understanding of historical time can be stimulated from an early age, by teaching according to the objectives that are defined in the model, with a consistent use of timelines. The model, assessment instrument, overview of types of problems, Timewise (the teaching approach that was developed with materials and resources on a website) and the professional development program for teachers can be useful for teachers in primary school, for teacher trainers, textbook editors and educational policy makers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".