Use of problem-based learning in the teaching and learning of horticultural production
Bibliographic record
Abstract
Purpose: Problem-based learning (PBL), a relatively novel teaching and learning process in horticulture, was investigated. Proper application of PBL can potentially create a learning context that enhances student learning. Design/Methodology/Approach: Students worked on two complex ill-structured problems: (1) to produce fresh baby greens for a 4-week catering event and (2) to produce seedlings for a grower. Data collected were analyzed by the concurrent method and presented as case studies. Findings: Students developed positive attitudes through active engagement. Their presentations and reports demonstrated leadership roles, critical thinking and conflict management. Practical professional, social and affective skills were developed through production of 5 kg baby greens, and 2500 vegetable seedlings. Successes and limitations were identified. Theoretical Implication: The quality of the PBL problem is critical for the stimulation and elaboration of prior knowledge, development of epistemic curiosity and the relevant semantic framework. These are motivators that inspire effective learning. Practical Implication: Cognitive and emotional intelligence skills are realized by trusting the PBL process, identifying enhancers and inhibitors. Enhancement of creativity, social and employability skills manifest through challenges that help to develop for the ‘whole’. Originality/Value: In the horticulture industry, stakeholders interact with each other and the agro-ecological system. Consequently, competencies in production and emotional intelligence are invaluable.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".