Implementation of Project-Based Learning (PjBL) through One Man One Tree to Improve Students’ Attitude and Behavior to Support
Bibliographic record
Abstract
The attitude and behavior of the students of class XI-6 in relation to environmental awareness is very low. It proves that there is no student involvement in environmental conservation. The purpose of this study is to increase students’ attitude and behavior related to environmental conservation using “One Man One Tree” Project Based Learning (PjBL). The study is a Classroom Action Research (CAR) conducted within two cycles. The subjects in this study are XI-6 students of SMAN 1 Torjun. It is conducted during the 205/2016 academic year. The data are scores of the attitude test and student’s behavioral manifestations. The findings show that there is an increase in the students’ attitude and behavior related to environmental awareness from the first cycle to the second cycle. The average yield postes attitude of the students on the first cycle is 86, while that on the second cycle is 93.2. The average yield postes behavior on the first cycle is 69 while the average score increases to 90.4 on the second cycle. It is suggested that teachers become more creative in applying the learning model that encourage students to protect the environment.
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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".