Complementary teaching approaches facilitating interdisciplinary soil science education
Bibliographic record
Abstract
The complementary nature of different teaching approaches in facilitating student learning is rarely discussed in the literature. This study compared diverse teaching approaches in soil science education to explore how a combination of instructional approaches can support student learning. Student perspectives on lectures, problem-based learning, and experiential learning in three upper-level university soil science courses were assessed through student enrolment data and survey responses. Results emphasize the benefits of integrating theory and practice and support the integration of concepts from soil physics, chemistry, and biology within individual courses. All respondents who took two or more courses indicated that the distinct teaching approaches and the integration of soil physics, chemistry, and biology within individual courses were beneficial to their learning. Lectures and problem-based learning were seen as pedagogically reciprocal, with theory supporting the application of knowledge for 75% students, while others noted that having the management course first provided context for learning additional theory. A subset of students (n = 9) indicated the relevance of the interdisciplinary nature of the courses for their current employment. Our findings suggest that combining knowledge-based and competency-based approaches may support both student learning and workforce demands and that diverse teaching approaches can work together to support student learning. The research outcomes call for fellow instructors to diverge from the dichotomy of passive and active learning and to consider the complementary nature of distinct teaching strategies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".