TEACHER’S STRATEGIES IN TEACHING WRITING RECOUNT TEXT AT GRADE 8th OF SMP GAJAH MADA MEDAN
Bibliographic record
Abstract
This study was conducted to analyze the types of teacher’s strategies in teaching writing recount text and to explore the process of applying teacher’s strategies in teaching writing recount text at grade 8 of SMP Gajah Mada Medan. This study used decriptive qualitative method. The subject of this research was one of the English teacher at SMP Gajah Mada Medan. The instruments of this research were observation, questionnaire, and interview questions. The results showed that the teacher applied four strategies from five strategies by Saskatchewan (1991) in teaching writing recount text, they were direct instruction strategy, indirect instruction strategy, interactive instruction strategy and experiential strategy. Here are the steps of applying each strategies : first, direct instruction strategy namely : (1) preparation, (2) introduction, and (3) direct instruction. The second , indirect instuction strategy namely : (1) facilitating discussions, (2) providing resources, and (3) guiding and supporting. The third, interactive instruction strategy namely : (1) select appropriate activities, (2) encourage participation, (3) create engaging content, (4) assess learning (5) facilitate discussion, and (6) guiding and supporting. The last, experiential strategy namely : (1) know your audience and (2) conceptualize the experience. It is suggested for the teacher to use all typs of teaching strategies because all of them are correlated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".