Co-teaching as a strategy of implementing a Competence Based Curriculum. A case of two Teacher Training Colleges in Rwanda.
Bibliographic record
Abstract
In many parts of the world, co-teaching has been used as an alternative to lone-teaching approaches. Co-teaching entails the collaboration of two or more teachers in the planning, delivery, and evaluation of instruction. Given its flexible nature, co-teaching has been widely used in a variety of educational contexts. While research has extensively dealt with co-teaching in many contexts little is known about co-teaching that brings together expatriate teachers and local ones in settings where both collaborate to enhance the implementation of competence-based curriculum (CBC) goals. This study explored how co-teaching can be used as a tool to facilitate the cooperation of expatriate tutors and their local counterparts in the implementation of teacher training colleges (TTCs) Competency Curriculum. The study employed the qualitative research approach in which tutors from both Rwanda and Zimbabwe co-teaching in TTCs formed the purposive sample. This study employed the multiple case study design using two TTCs. Data was collected using in-depth interviews and observations. Findings from the study showed that despite somewhat positive indicators of mutual collaboration, tutors’ practices lacked a deep understanding of co-teaching and how it could be used to steer a movement from a knowledge-based curriculum to a competency-based one. Other findings showed that concurrent with workshops used to introduce the concept more robust written policies were needed to effectively guide teacher development and implementation of the CBC in TTCs. The study recommends continuous professional development for both the expatriate tutors and the locals to create a common and shared understanding of co-teaching and effective implementation of the CBC in TTCs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".