Exploring Teachers’ Professional Development Based on Pedagogical Content Knowledge: A Case of in-Service Biology Teachers in Tanzania
Bibliographic record
Abstract
Purpose: This study aimed to reveal the impact of the professional development program on the enhancement of pedagogical content knowledge (PCK) among in-service biology teachers. Instructional strategies, students' misconceptions and learning difficulties, representation of the content, context for learning, and curriculum knowledge were some of the main PCK themes. Methodology: Eighteen secondary school teachers from six regions in Tanzania were involved in the intervention program. The study adopted a mixed-methods and case-study research design to collect both quantitative and qualitative data. Findings: The study revealed a positive transformation in the teachers’ conception of teaching and learning processes by shifting from a traditional teacher-centred approach to a more constructivist approach. Teachers are competent with the new trends in teaching and learning biology, including the use of technology and improved instructional materials, utilization of the local environment, and integrating academic content knowledge with everyday life. Unique contribution to theory, practice and policy: Based on the findings, it is recommended that in-service teachers' professional development programs be an ongoing process with the ultimate goal of offering opportunities to learn contemporary teaching methods, improve teaching skills, and acquire a broad knowledge of how students learn biology.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".