Integration of Problem‐Based Learning and Web‐Based Multimedia to Enhance a Soil Management Course
Bibliographic record
Abstract
In an attempt to address declining enrollment in soil science programs and the changing learning needs of 21st century students, several North American universities have re‐organized their soil science curriculum and adopted innovative educational approaches and web‐based teaching resources. An interdisciplinary team set out to integrate teaching approaches to address this trend. The objective of this project was to develop a web‐based teaching tool, which combined a face‐to‐face problem‐based learning (PBL) case study with multimedia to illustrate the impacts of three land‐uses on soil transformation and quality. The Land Use Impacts (LUI) tool ( http://soilweb.landfood.ubc.ca/luitool/ ; verified 4 Oct. 2011) was a collaborative and concentrated effort to maximize the advantages of two educational approaches—the web's adaptability and accessibility, and PBL's capability to foster an authentic learning environment, apply core concepts, and encourage group work. The design of the LUI case study was guided by Herrington's development principles for web‐based authentic learning. The LUI tool presented students with rich multimedia (streaming videos, text, data, photographs, maps, and weblinks) and real world tasks (site assessment and soil analysis) to encourage students to utilize knowledge of soil science in collaborative problem‐solving. Preliminary student feedback indicated that the LUI tool conveyed case study objectives and was appealing to students. The tool is intended primarily for students enrolled in an upper level undergraduate/graduate university course titled Sustainable Soil Management, but it is flexible enough to be adopted for other natural resource courses.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".