Secondary Science Teacher Preparation: A Scoping Review of Challenges, Structures, and Interventions
Bibliographic record
Abstract
This scoping review synthesizes literature on secondary science teacher preparation programs across five continents, drawing on 22 peer-reviewed studies. It examines program structures, prevailing challenges, and strategies aimed at addressing science teacher shortages, with a particular focus on rural contexts. The countries represented in the review include the United States, Canada, Germany, China, and others from diverse geographic regions. The findings highlight dominant challenges, including shortages of qualified science teachers, technological, pedagogical, and content knowledge (TPACK) gaps, and limited opportunities for interdisciplinary or transdisciplinary training. Shortages are mitigated through lateral/alternative entry pathways and structured programs offering 3--4-year training, combined with clinical experience to enhance TPACK and meet licensure requirements. Some countries train teachers to teach multiple science subjects and provide incentives through scholarships and higher salaries for those who commit to working in rural schools. These findings offer valuable insights for stakeholders, suggesting adaptable strategies to improve the quality and supply of science teachers and strengthen science education.
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 | 0.039 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".