A Waterfall Model for Providing Professional Development for Elementary School Teachers: A Pilot Project to Implement a Competency-Based Approach
Bibliographic record
Abstract
Supporting the professional development of teachers to enhance mathematics learning is an important consideration of global education initiatives. However, designing and implementing professional development depends on the structures in place in different contexts. For instance, some structures involve different roles played by the different actors in the schooling system. Thus, school board consultants, principals, inspectors and teachers might be in charge of providing information, coaching, training or educating teachers. Those policies and practices are key components when designing and implementing professional development for teachers at a large scale. This article presents an initiative supported by UNICEF in Democratic Republic of the Congo (DRC). In 2015, the DRC undertook a transitional approach to school reform by adopting a situation-based approach “Approche par les Situations (APS)” in the elementary school curriculum. An experimental pilot project to improve teaching and learning Mathematics and Language Arts in elementary school was set up. To this end, learning situations were created and 80 teachers were trained in the use of these situations in class using a waterfall model of professional development. The results indicated positive contributions resulting from the teacher-enacted situation-based approach, but also exposed functional problems of implementing a waterfall model to support teachers at a large scale. Our results highlight the challenge of supporting all teachers in a global context.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".