INTEGRATION OF SPIRITUAL VALUES IN SCIENCE LEARNING CONTENT AS AN EFFORT TO BUILD STUDENT CHARACTER FROM THE SCHOOL LEVEL
Bibliographic record
Abstract
The concept of science has the potential to be explored in instilling spiritual values. Competence of a good science teacher is needed to integrate science learning and inculcate spiritual values. This PPM activity aims to provide knowledge and improve the competence of science teachers in presenting science learning activities that focus on cognitive abilities as well as spiritual attitudes. Community service activities (PPM) will be carried out in the form of workshop training involving students and science teachers in Rejang Lebong Regency, Bengkulu Province. Based on the results of the questionnaire, it is known that teachers and students agree on the need for the integration of spiritual values through science learning. The results of the teacher's worksheet show the teacher is able to relate the concept of science to spiritual values of the greatness of God. The teacher can describe the plan for presenting these values in learning activities. Students are very enthusiastic about learning science with spiritual values and students are able to give examples of various phenomena related to the concept of physics and the spiritual values in it. Based on the results of the response questionnaire and working papers, it can be concluded that students and teachers are highly motivated by learning science with spiritual values. After participating in the workshop, the teacher can integrate spiritual values into basic competencies in science learning. Students are able to mention spiritual values in various phenomena related to scientific concepts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".