Rare Events: Technology Throughout the History of ICMI
Bibliographic record
Abstract
Reflecting on the history of technology in mathematics education reminds me of a comment made by Richard Noss while answering a question after his opening keynote address to the 2005 Fields Symposium on Online Mathematical Investigation as a Narrative Experience, held at the University of Western Ontario, Canada. Noss described the wonderful mathematics experiences he has witnessed in classrooms using technology as rare events. I will return to this idea at the end of this paper. Five pages cannot do justice to the topic of technology in the history of ICMI. To make the task manageable I will do the following: (1) I will address the historical component only partially by focusing on one point in time, on ICME-7 (1992). I have chosen to focus on ICME-7 for three reasons. First, ICME-7 was held in Quebec City, Canada, and it was a Congress that I attended. My paper in part represents a Canadian perspective, so it seems appropriate that I focus on an ICME that was held in Canada. Second, ICME-7 was the first ICME to focus intensely on technology in mathematics education. Third, as I will elaborate below, ICME-7 took place near a turning point in our view of technology in mathematics education. (2) Then, I will discuss changes from 1992 to the present in the context of the shifts in my own focus in mathematics education technology. Though my experience does not provide a comprehensive history, it does identify a number of important trends in mathematics education technology. (3) Lastly, I will return to the idea of rare events and offer some ideas about what they are and how current trends in technology may help us to make rare events public, to be shared as models for others and to serve as objects for reflection and critique.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".