Bibliographic record
Abstract
According to the constructivist approach in mathematics education, knowledge is actively created or invented by the students, not passively received from the teacher or the environment. In this regard, learning environments should be designed so that students have to use effectively their “own” knowledge and thus students achieve desired information. Through the micro-worlds, the experimental environments where students make observations, researches and predictions shall be created. In the micro-worlds formed by means of Cabri Geometry, a dynamic geometry sketchbook, users can draw and explore many geometric shapes. Besides, the manipulation and displacement of objects can be easily done via this software. In this study, we emphasize on that property of Cabri Geometry. Four pre-service elementary mathematics teachers among thirty ones which have taken a course on the mathematics teaching in the dynamic mathematics software environment have voluntarily participated to the study. The participants have randomly divided into two groups. Each group has worked on computer together and is asked to draw two dimensions shapes like triangles, parallelogram, rectangle etc. without using related “tool of Cabri”. The collection of the data of this study involves interviews during their work, screen record of the computer and tape record of conversation among pair of participants. The results of study reveal that more professionally the participants use the Cabri Geometry more different strategies they use to construct asked shapes and participants have always questioned themselves about their pre-knowledge of the dynamically constructed shape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".