Change in mathematics education during a time of crisis: Reflections through the lens of complexity constructs
Bibliographic record
Abstract
The year 2020 will be remembered as the time when the COVID-19 pandemic swept the world. Almost overnight, all educational activities pivoted to online platforms and teaching and learning was navigated in uncharted terrains. In mathematics education, concerns about sustaining online teaching and learning of mathematics have generated efforts in using digital technologies. In this paper, we use the lens of complexity theory and in particular the constructs of agents, interaction, dispersed control, and emergence to describe top-down and bottom-up mechanisms for change within the sudden shift to emergency remote teaching and learning. The authors’ collaborative work was carried out through online meetings discussing observations on and insights about their experience as mathematics teacher educators during the COVID-19 pandemic and traction data in three locally available online platforms. The main findings indicate two government-led, top-down initiatives, and three community-led bottom-up initiatives. The results suggest that mathematics teachers, mathematics teacher educators, and mathematics teacher consultants served as actors within the larger system. We discuss the possibilities and constraints of mathematics education in a time of crisis through the lens of complexity theory and offer trajectories for further research.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".