Writing Descriptive Essays Using ‘the Tree Diagram’ as a Tool
Bibliographic record
Abstract
The study is experimental in nature. Both the experimental group and the control group comprised respondents who are sixth form students of SMK Rantau Panjang, Kelantan. The experimental group was exposed to the ‘tree-diagram’ writing strategy for a period of one month. This meant a total exposure of sixteen 40-minute periods for the duration of the month. Both the groups were subjected to a pre-test at the onset of the experiment and a post test one month later. A t-test showed that there was a significant difference between the pre test and the post test writing marks of the experimental group whereas no such significant difference was found in the control group. A similar t-test showed that there was a significant difference in the grammar scores of the experimental group but none for the control group. There was also a significant difference between the pre test and post test motivation score of the experimental group. However, a t-test indicated that there was no significant difference in the motivational scores of the males and females at the conclusion at the experimental period. Future researchers should focus on the use of tree-diagram to assist other types of essays such as argumentative essays.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".