PENINGKATAN KEMAMPUAN MEMBACA PERMULAAN MELALUI PENDEKATAN WHOLE LANGUAGE
Bibliographic record
Abstract
The purpose of this study was to obtain empirical data about the increased ability of one grade student’ reading at SDN Kramat Pela 07 Pagi Jakarta Selatan, using the whole language. The research was conducted at SDN Kramat Pela 07 Pagi Jakarta Selatan to the subject of research is the one grade students, amounting 30 studens in the first semester of academic year 2014/2015. Classroom action research was conducted using a cycle of John Elliot. Classroom action research conducted through the stages of planning, implementation actions, monitoring/observation, of reflection and evaluation as a basis for re-planning of each subsequent cycle. The research was carried out as much as 2 cycles where each cycle consists of 3 x meetings with each meeting time allocation of 2 x 35 minutes. The results obtained from student evaluations at each cycle are as follows : I cycle 53 % of students who have reached the score of 75, whereas in cycle 2 there was an increase to 100 % of students who have reached the score of 75. The implications of this research is the use of whole language can be one of alternative menthods to improve student’ reading grade I at SDN Kramat Pela 07 Pagi Jakarta Selatan. Keywords : reading ability and whole language
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".