Struggles and Growth in Mathematics Education: Reflections by Three Generations of Mathematicians On The Creation of the Computer Game E-Brock Bugs
Bibliographic record
Abstract
In the Fall of 2013 our team of three different generations of mathematicians launched the free, online E-Brock Bugs© mathematics computer game [5] which we developed from an original probabilistic board game, Brock Bugs, and its digital learning object version. We constructed E-Brock Bugs using Devlin’s [9] mathematics computer game design principles for games that prompt players’ development of mathematical thinking. As we created E-Brock Bugs we found it necessary to go through an evolving cyclic process of design, implementation, and analysis. In this paper we reflect upon the main struggles we faced in this process and the unexpected personal growth that ensued in terms of our views and beliefs as mathematics educators.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.041 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".