Exploring the impact of in-service training on elementary science teachers’ professional competence
Bibliographic record
Abstract
Public school teachers in Pakistan often undergo in-service trainings that are usually managed by foreign donor-funded agencies. Because of the poor performance of primary schools, the training projects usually focus on professional development of primary and elementary teachers. It is important to know how much the programmes are beneficial for building teachers’ professional knowledge basis by enhancing their teaching skills. To improve schools and enhance professional competence of elementary teachers, the Aga Khan University-Institute for Educational development launched a project known as Strengthening Teacher Education in Pakistan (STEP) through funding of Foreign Affairs and Trade Developments of Canada and the Aga Khan foundation (AKF-C) Canada. Among a variety of programmatic activities, Whole School Improvement (WSIP) is one of the key programmes of STEP project implemented in multifaceted interventions in three separate districts of Balochistan. Teachers continued professional development through in-house workshops and follow up support has been one of the key factors of WSIP. This study explored the impact of training programme conducted under Whole School Improvement Programme (WSIP) on primary teachers’ competence in the context of Balochistan. A qualitative case study methodology was adopted to conduct the study. The participants comprised four primary teachers and a Lead Teacher who was the master trainer. The data was collected through multiple sources: semi-structured interviews, classroom observation, field notes and document analysis. The main findings of the study suggested that the teachers built a collaborative learning environment in the schools. Their interactions and discussions enhanced their professional knowledge and skills. The teachers also developed friendly attitude with students. Furthermore, the teachers believed that on the one hand, the new learnt pedagogies help them involve students in interactive learning and on the other hand, they perceived that the pedagogical skills were helpful to enhance students’ conceptual understanding of science topics. However, the teachers faced challenges like, shortage of necessary teaching materials, lack of parental involvement, pressure of syllabus completion and lack of follow up support. These challenges impede the process of effective classroom teaching and learning. The study recommends further support in terms of resources and professional development for teachers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".