Synchronous online collaborative professional development for elementary mathematics teachers
Bibliographic record
Abstract
Math is often taught poorly emphasizing rote, procedural methods rather than creativity and problem solving. Alberta Education developed a new mathematics curriculum to transform mathematics teaching to inquiry driven methods. This revised curriculum provides a new vision for mathematics and creates opportunities and requirements for professional learning by teachers. Conventional offsite, after school, or weekend professional development is typically “sit and listen, maybe try on Monday”. Professional development that is embedded, responsive, and personalized is known to be more effective at changing teaching practice. Alberta teachers are geographically dispersed making online professional learning a desirable alternative to on-site workshops. As access to and use of the Internet gains momentum in schools across the country, opportunities for collaborative, online professional development become more viable. The online professional development in this hermeneutic study maps on to the new vision promoted in Alberta’s math curriculum, and addresses the challenge of a distributed teacher population. Thirteen geographically dispersed participants, including 10 teachers, a PhD mathematician, and two mathematics education specialists, collaborated in an online professional learning community to build knowledge for teaching mathematics. This paper describes and interprets the shared experiences of learners within an online, synchronous learning community that focused on discipline rich, focused inquiry with mathematics. Findings show that the nature and quality of the mathematics task impacted the quality and nature of the online interaction. Mathematics problems that incorporated easily drawn symbols and minimal text worked best in the online collaborative space. Members of this learning community discovered how to assert their identity in the online environment.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".