Mapping the Kazakhstani STEM Education Landscape: A Review of National Research
Bibliographic record
Abstract
The aim of this study was to map the current status of STEM education in Kazakhstan. The study encompasses 24 studies selected through a literature search in Google Scholar, ERIC, Web of Science, and Scopus. The descriptive characteristics of the reviewed studies reveal a significant increase in STEM education publications in Kazakhstan since 2019, indicating a growing emphasis on this field. The reviewed studies, spanning the years 2019 to 2023, included diverse formats such as journal articles, conference proceedings, book chapters, theses, and review articles. Notably, the reviewed studies involved participants from both K-12 and university levels, with a particular focus on female students in some studies. The thematically organized findings of the reviewed studies highlighted challenges faced by STEM education in Kazakhstan, including students’ perceptions about STEM subjects and careers, school environment and educational culture, and societal and gendered expectations. Creating interactive learning environments, addressing biases, dismantling gender stereotypes, and challenging traditional norms were identified as crucial steps to encourage the participation of young women in STEM disciplines. This study contributes to understanding STEM education in Kazakhstan and provides a foundation for future cross-country comparisons, emphasizing the necessity for adaptable approaches in designing and evaluating STEM initiatives in evolving educational contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".