Learning Geographic Information from a Map and Text: Learning Environment and Individual Differences
Bibliographic record
Abstract
A map is frequently combined with a text to provide spatial and non-spatial information for learners. How a map and a text are combined and the characteristics of learners are keys for understanding successful learning. This study used a cognitive experiment to investigate spatial learning by explaining performance on a test of acquired knowledge with variables related to the learning environment and to individual differences of learners. Results indicate that having participants read a text beside a map produced the best performance. Participants were more successful at learning the information in the text and less successful at learning the information on the map. Performance was measured by accuracy, reaction time, and confidence measures; a standardized index for overall efficiency combined these measures. Performance was significantly related to individual difference variables measuring experience, verbal and spatial working memory capacity, 2D/4D digit ratio, and cognitive style. Sex and gender variables were not significantly related to variations in performance. In complex learning situations, as in processing a combined map and text, the expected verbal and spatial processing advantages of female and male learners may both produce positive results. In more complex cases, variables related to brain asymmetry, memory capacity, and cognitive style may provide more useful explanations of performance.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".