The Development of Spatial Knowledge at School and in Teacher Training
Bibliographic record
Abstract
In this chapter, the authors present their reflections on teaching and learning about space. They focus on a possible activity that can be used to develop spatial knowledge. The authors conduct an analysis of this activity and outline four teaching sequences derived from this activity. The first three were designed for primary school pupils in Quebec, more specifically for the 1st (6-8 years old), 2nd (8-10 years old) and 3rd cycles (10-12 years old) of primary school, and the fourth was designed for pupils in the bachelor's degree program in special education at their university institution. The authors present conceptual framework and analyzes the activity of Yackel and Wheatley in relation to it, as well as the didactic variables that can be used. They develop teaching sequence experimented with in teacher training at the Universite de Sherbrooke, who offer a course on the didactics of geometry and measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".