Changing the curriculum script: teaching line symmetry in a digital environment
Bibliographic record
Abstract
In this study, a set of activities on line symmetry in the Geometer’s Sketchpad environment, published on the Canadian website http://www.sfu.ca/geometry4yl.html, were adapted by two Italian researchers for 1st and 2nd grade classes with an Interactive White Board (IWB) in the classroom. The activities were proposed to a 2nd grade during two lessons conducted by one of the researchers, that were video-recorded. The videos were passed to 3 teachers who then proposed the same activities in their 1st and 2nd grade classes. The study was carried out over a 6-month period, with the aim of studying 1) Italian children’s responses to the activities and the emergence of attention towards mathematical properties of line symmetry; 2) the process of change undertaken by the set of activities, conceived as a boundary object (Star and Griesemer 1989); 3) teachers’ implementation of the activities and reflection upon the close interaction they had with the researchers involved on issues including the design of the digital objects in the activities. This poster discusses aspects of aim 3; in particular, the changes in structuring features (Ruthven, 2011) of teachers’ lessons on line symmetry before and during this experience.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".