EXPLORING THE INFLUENCE OF NON-GOVERNMENTAL ORGANIZATION-LED INITIATIVES IN TEACHERS’ CONTINUOUS PROFESSIONAL DEVELOPMENT ON LEARNERS’ ACADEMIC PERFORMANCE IN KINIHIRA AND TUMBA SECTORS, RULINDO DISTRICT, RWANDA
Bibliographic record
Abstract
The study sought to examine the relationship between continuous professional development (CPD) on learners’ academic performance in Rwandan primary schools. This focused on the following specific objectives: What is the relationship between teacher in-service training and learners’ academic performance in Rulindo primary schools, To what extent teacher peer learning platforms relate to learners’ academic performance in Rulindo primary schools, What is the relationship between teacher coaching and mentoring and learners’ academic performance in Rulindo primary schools. The descriptive research design was used. Quantitative and qualitative approaches were used to analyze data. 158 people were used as the target population and 103 as the sample size to represent the whole population. Data were collected using structured questions with 5-point Likert scales and an interview. Quantitative data were analyzed using frequencies, percentages, standard deviation, means, and regression analysis while qualitative data were analyzed using a thematic method that helped in analyzing qualitative data. The finding from the study concluded that there is a significant relationship between continuous professional development and learners’ academic performance in Rwandan primary schools. SPSS and the thematic method were used to calculate the regression analysis of the study: based on the findings from the study, the researchers revealed that some gaps need to be solved by different organs such as non-governmental organizations (NGOs) across the world. The results indicated that there is positive and significant effect of teacher coaching and mentoring on learners’ academic performance (B = 0.894, P value ˃0.00). The study recommended providing frequent continuous professional development in schools across the country. The study recommends REB survey how continuous professional development (CPD) impacts the quality of education. In the same regard, the study recommends that educational-related stakeholders fund the continuous professional development of teaching and administrative staff. Article visualizations:
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".