The Impact of Map Type on the Level of Student Map Skills
Bibliographic record
Abstract
Thematic maps are becoming increasingly important in the current information age, and therefore they have become part of the everyday life of the wider public. Given the number of mapping methods used in thematic cartography, the question arises of what extent the utilized method influences a user’s map use level. For this reason, research has been undertaken with 392 students in the 17–20 age group with the aim of identifying and clarifying the influence of the mapping methods used (specifically choropleth mapping, diagram mapping, qualitative and quantitative line symbols, and area shading). The results have shown that the students were less successful with maps that used quantitative mapping methods than with maps using qualitative or both qualitative and quantitative mapping methods. The differences were most significant in the case of the cognitively demanding map interpretation skill, especially due to the students’ problematic understanding of the very essence of the quantitative methods. The different natures of the tested mapping methods also probably accounted for the identification of various factors explaining the differences in the levels of the work with the given thematic maps among the individual tested students.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".