Opportunities and challenges of using geospatial technologies in teaching school geography in Kazakhstan
Bibliographic record
Abstract
The rapid advancement of technology and widespread access to digital resources have transformed teaching practices and enhanced educators’ competencies. Geographic Information Systems (GIS) offer new avenues for understanding global events and conducting multidimensional analyses. This study aimed to evaluate teachers’ potential use of geospatial technologies by analyzing the objectives of geography curricula for grades 7–11 in Kazakhstan. Additionally, this study investigated barriers to GIS adoption among geography teachers and strategies for cultivating geospatial thinking skills. The research employed curriculum analysis and a teacher survey involving 208 respondents from across Kazakhstan. The results indicated moderate adoption of geospatial technologies, with mobile GIS applications being most prevalent. However, obstacles such as equipment costs, lack of experience, and time constraints hinder their utilization. This study underscores the importance of integrating geospatial technologies in geography education to foster students’ spatial thinking, creativity, and information literacy. Moreover, this study highlights the role of a GIS in enhancing educators’ and students’ proficiency in modern geoinformation systems and digital technologies. The findings offer practical insights for educators and serve as a valuable resource for enhancing geography instruction and promoting geospatial technology integration in the classroom.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".