Reevaluating Froebel Gifts: A Tool That Embodies Three Dimensions in Space to Enhance Pre-school Children’s Geometry Skills
Bibliographic record
Abstract
Froebel Gifts are the first educational materials designed for kindergarten children in the education history. After Froebel introduced the first construction and design materials for early childhood mathematics education in 1850’s, several companies such as Lego Bricks, Lincoln Logs, and K’nex were influenced by his design. Considering the influence of Froebel Gifts on mathematics and geometry education tools for young children, it is important to analyze their impact on pre-school children’s geometry skills. This study investigated the effects of a geometry education program mediated with Froebel Gifts on 5-6 years-old children’s geometric skill development. The participants consisted of 40 pre-school children in Istanbul, Turkey. Twenty children in the experimental group received an 8-week intervention with Froebel Gifts while the control group continued their regular program. Early Geometry Skill Test (EGST) was used before and after the intervention and the collected data were analyzed with independent sample T-test. The results indicate a significant change in favor of the experimental group in the skills of building with blocks, recognizing the three-dimensional objects, predicting the surface shape of the three-dimensional objects, and recognizing the side and corner properties of two-dimensional shapes. The teacher’s instructional strategies and the physical and representational properties of Froebel Gifts are discussed and evaluated in the light of these results to provide insight about the features of an effective geometry education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".