Combining problem‐based learning and team‐based learning in a sustainable soil management course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it