Environmental Learning Workshop: Lichen as Biological Indicator of Air Quality and Impact on Secondary Students’ Performance
Bibliographic record
Abstract
In this study, the learning of science outside the classroom is believe to be an added value to science learning as well as it offers students to interact with the environment. This study presents data obtained from two days’ workshop on Lichen as Biological Indicator for Air Quality. The aim of the workshop is for the students to gain an understanding on various aspects on Lichens and its role as a biological indicator. Students are exposed to the following concepts and skills in the workshop: characteristics of algae, the concept of Lichen, causes of polluted air, the concept of quadrant, how to measure the frequency of Lichen, types of Lichen, determining the air quality index, factors determining the quality of air, the role of Lichen, the function of Lichen as a biological indicator and determining the level of air pollution. Four schools participated in the two day workshop whereby two schools were located in rural and urban areas respectively. A total of 125 students were given a pre-test on the concept of Lichen before the workshop followed by brief lecture sessions, hands-on and field work activities, presentation by students. At the end of the workshop, students were given a post-test. Overall, from the pre and post-test results, there is a significant difference in terms of students’ performance from all the four schools, in knowledge and skills relating to Lichen and its role as a biological indicator in determining the quality of air.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".