Humanizing the Classroom: Praxis of Full Day School System in Indonesia
Bibliographic record
Abstract
The full day school program is a new model in the education management system in Indonesia. This phenomenon is interesting because there is a paradox in it. Education in Indonesia is often criticized for the learning which is too heavy, but the full day school program gets a positive response from the community although it has longer school hours. This research aims to answer the questions; 1) How is a full day school praxis program in Indonesia? Second, how is the implementation of humanizing the classroom learning program in schools that implement the full day school program? This research is a qualitative research with locus at Islam Terpadu Salsabila Elementary School (SDIT Salsabila), Yogyakarta. Research subject is determined by purposive sampling. Method of data collections are: 1) participant observation, 2) in-depth interview, 3) questionnaire, and 4) document analysis. Data analysis technique is inductive-qualitative. The results are; First, the full day school program in Indonesia is growing as a result of the increasing number of busy working parents. They do not have the opportunity to accompany their children at home because they are busy working from morning to evening. The full day school program is an educative solution for them. Secondly, humanizing the classroom is implemented through students’ active involvement in the classroom and the learning process is democratic and fun. This approach is a solution so that students do not feel boredom in learning although they spent their time throughout the day at school.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".