Combining problem‐based learning and team‐based learning in a sustainable soil management course
Bibliographic record
Abstract
Abstract Professional natural resource managers require a solid understanding of sustainable soil practices. Postsecondary institutions are increasingly integrating innovative approaches such as hybrid problem‐based learning (PBL) and team‐based learning (TBL) to train future professional land managers to tackle complex problems. This article describes the application of a hybrid PBL–TBL approach in a combined undergraduate and graduate level course, Sustainable Soil Management, offered at the University of British Columbia (UBC), Vancouver, Canada. The course utilizes 15 modified PBL cases, where “modified PBL” refers to a hybrid PBL–TBL approach. The course aims to provide experiential learning opportunities for students to connect with practicing professionals and community partners in addressing real‐world issues. Course instructors identified several challenges related to the modified PBL approach including multiple outcomes based on data interpretation, imbalanced team composition, and complex cases that demand advanced education and/or experience. However, course instructors and students were favorable of the enhanced teaching and learning opportunities offered by the hybrid PBL–TBL format. Student engagement was facilitated by the practical relevance of the cases, the opportunity to incorporate fieldwork, and interactions with external (guest) case contributors; and a balance between knowledge‐based and competency‐based learning outcomes was achieved. This hybrid PBL–TBL approach could serve as a framework for other postsecondary courses focused on sustainable management of natural resources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".