Improving the Students’ Spiritual Intelligence in English Writing through Whole Brain Learning
Bibliographic record
Abstract
The objective of this research was to improve the students’ spiritual intelligence in English writing through Whole Brain Learning strategy. Therefore, this study was conducted as a classroom action research. The research pocedure followed the cyclonic process of planning, action, observation, and reflection. This process was preceeded by pre-liminary study in order to know the students’ spiritual intelligence in English writing before being taught by the whole brain learning. The data was collected from the results of spiritual intelligence questionnaire, observation, interview, and documentation. The subjects of the research were 30 students in English Education Department, Universitas Islam Negeri Sumatra Utara. The quantitative data were analysed by using t-test in Statistical Package for the Social Science (SPSS) and the qualitative data were analysed by using Miles and Huberman technique: data reduction, data display, and verification. As a result, there was a significant improvement in students’ spritual inteligent in English writing when they were taught through Whole Brain Learning.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".