Pre-service teachers’ understandings of exploratory task design in mathematics: from GeoGebra task design to the 8 <sup>th</sup> grade student
Bibliographic record
Abstract
The aim of the study informing this paper was to investigate pre-service teachers’ (PSTs’) approaches to task design, posing the research question: How do pre-service teachers respond when they are asked to design an exploratory task using GeoGebra for 8th grade students? The research study and this paper focus on the PSTs’ design stories rather than their task formulations. Drawing on multiple case study design, the study’s sample consisted of 54 PSTs from a Norwegian teacher education program in mathematics for lower secondary school. Once an initial analysis and coding of the tasks was complete, a group of 13 PSTs were interviewed about their own task design, with some of these PSTs taking part in a focus group discussion as well. In this paper, six representative cases of PST task design are presented and analyzed together with additional support from the rest of the interviews and focus groups. Findings revealed four key themes in their design stories: the knowledge of 8th graders, guiding or exploring, use of context or not, and the role of GeoGebra. Each of these themes is discussed with an aim of highlighting key implications for PST task design and the education of new mathematics teachers.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".