Assessment of the Level of Knowledge of Climate Change of Undergraduate Science and Agriculture Students
Bibliographic record
Abstract
Introduction of climate change (CC) courses in universities is critical for helping future generations and leaders in recognizing the global challenges of CC and finding ways for adapting with it. People's knowledge of CC can influence success of any planned CC mitigation and adaptation programs and activities. Thereupon, it is vital for environmental planners and researchers to conduct regular assessments of this knowledge to determine need for curriculum reform, if any. This study was conducted to assess the level of CC knowledge of undergraduate physical science and agricultural science students in Jarash University, Jordan. The study used specifically-designed Climate Change Knowledge Test (CCKT) as the data collection tool. Population of the study was undergraduate science and agriculture students enrolled in the Faculty of Agriculture and Science. The study sample consisted of 285 students, comprising 103 science students and 182 agriculture students. The results indicate that the sample students have high levels of knowledge of the nature, causes, and effects of CC. However, on the average, a higher number of the sample students posses knowledge of effects of CC (n = 223, % = 79.3%) than its nature (209, 73.5%) and causes (190, 66.9%). Additionally, it was found that the female students have higher levels of overall CC knowledge than their male peers and that the agriculture students possess higher levels of CC knowledge than their science peers. These results emphasize the need for curriculum review and reform to ensure equipping the university graduates with comprehensive knowledge of CC.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".