TEACHER TRAINING TECHNOLOGY AND THE USE OF THE IWB IN THE SECONDARY MATHEMATICS CLASSROOM
Bibliographic record
Abstract
At the beginning of the current century many educational researchers believed that the problems of teaching and learning mathematics within technological environments had not been deeply addressed. After 17 years of research in this area, some of these problems have been solved while others still persist. Taking the perspective on this problematic as a complex system, in this document, we analyze different variables that come into play in the learning process in the training of mathematics teachers in the province of Quebec (Canada), with particular attention to the use of the interactive whiteboard (IWB). The use of IWB was introduced in primary school (6-11 years old) and secondary school (12-17 years old) in 2007 in Quebec, taking mathematics teachers and textbook authors by surprise. Adding to this disequilibrium, shortly before that time the Ministry of Education of Quebec had conducted an education reform centered on mathematical competencies. In this document, we introduce the notions of global theoretical model and local theoretical model, as tools that enable us to conduct an analysis of the complex system that is the problem of mathematics teacher training around the use of technology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".