Viewpoint: Order and Reinforcement in Human Geography: Do They Matter?
Bibliographic record
Abstract
Over a ten-year period students in a two quarter introductory sequence in Human Geography showed contrasting performance on examinations. In the first quarter students were tested after five weeks of work and then again at the end of the quarter on the materials taught during the sixth through tenth weeks of the course. Compared with the first exam, students' grades on the second exam improved an average of 8.43 points. Although the same exam time format was used in the second quarter course, the grades declined from the first to second exam by an average of 9.21 points. Since these patterns were consistent over a wide range of instructional circumstances, the differences in performance were sought in the order in which subjects were taught and the manner in which concepts were reinforced. It appears that reinforcement is more important than the order subjects are taught in helping students score well on examinations. Geographers are urged, therefore, to explore the possibility of using the insights of the writing across the curriculum movement when designing their introductory human geography courses. In addition, this and other pedagogical issues are recommended for formal inclusion in graduate programs training scholar teachers so that future generations of University level geography teachers will be better prepared for actual classroom conditions.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".