Engaging the students of today and preparing the catchment hydrologists of tomorrow: student-centered approaches in hydrology education
Bibliographic record
Abstract
Abstract. As hydrologists confront the future of water resources on a globalized, resource-scarce and human-impacted planet, the educational preparation of future generations of water scientists becomes increasingly important. Although hydrology inherits a tradition of teacher-centered direct instruction – based on lecture, reading and assignment formats – a growing body of knowledge derived from engineering education research suggests that modifications to these methods could firstly improve the quality of instruction from a student perspective, and secondly contribute to better professional preparation of hydrologists, in terms of their abilities to transfer knowledge to new contexts, to frame and solve novel problems, and to work collaboratively in uncertain environments. Here we review the theoretical background and empirical literature relating to adopting student-centered and inductive models of teaching and learning. Models of student-centered learning and their applications in engineering education are introduced by outlining the approaches used by several of the authors to introduce student-centered and inductive educational strategies into their university classrooms. Finally, the relative novelty of research on engineering instruction in general and hydrology in particular creates opportunities for new partnerships between education researchers and hydrologists to explore the discipline-specific needs of hydrology students and develop new approaches for instruction and professional preparation of hydrologists.
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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 | 0.007 | 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 | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".