Bibliographic record
Abstract
Like other disciplines, physical geography has seen substantial recent interest in research on ways to improve undergraduate teaching and learning. Most of this research has taken place in a constructivist framework in which students construct knowledge in ways that are meaningful to them. Constructivist theory forms the basis for a wide range of active learning approaches, such as inquiry-based learning and problem-based learning. These approaches are inductive in that students build theory and generalizations from case studies rather than more traditional approaches in which the students learn the theory and then study some examples. Students are typically more engaged in their active learning than they are in traditional approaches, but the impacts of the newer approaches on student learning are unclear. Experiential and service learning, together with fieldwork, offer considerable organizational challenges, but the learning rewards are clear and unchallenged. Attempts to replace fieldwork with virtual field trips have met with resistance, but there has been little research on the ways that virtual fieldwork could be improved. Introductory physical geography textbooks have failed to keep up with changes in teaching the subject, although there have been some recent innovations that offer promise. Animations in particular seem to engage students, although there is no evidence that they enhance the learning of physical geography. The nature of the relationship between research and teaching continues to fascinate, yet eludes clarification. The scholarship of teaching and learning physical geography offers challenges and opportunities for new and experienced faculty who have not previously published in this field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".