Teachers’ Perspectives on Map-Making in Geography Lessons
Bibliographic record
Abstract
In today’s information and visual world, where misinformation, including in the form of maps, is often encountered, map-making is a beneficial activity and skill. Creating maps contributes to one’s understanding of cartographic concepts and helps to develop various geographical and cross-disciplinary competencies. Therefore, the authors aim to characterize teachers’ views on map-making in geography lessons in lower-secondary schools. To fulfill this aim, a questionnaire was developed, which 253 geography teachers then filled out. The results show that many of these teachers include map-making in a few of their lessons and that, in these lessons, pupils largely create less complex map types. However, the exalted status of drawing into outline maps, an activity that more than 90 percent of respondents consider map-making and include in their teaching, is worrying. This affected what the respondents frequently reported as reasons why their pupils make maps, such as improving pupils’ spatial orientation and imagination. The respondents also perceived many obstacles to map-making, including a lack of time for map creation in geography lessons and insufficient software equipment in schools, due to which they do not make maps more often and mainly make them by hand.
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 | 0.003 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".