THE FORMATIVE ASSESSMENT BACKWASH IN ENGLISH INSTRUCTION AT KRISTEN NUSANTARA VOCATIONAL SCHOOL
Bibliographic record
Abstract
This case study focuses on covert backwash of formative assessment in English instruction and aims to provide framework of teacher’s instruction realizations in administering formative assessment.The subject was a teacher selected by certain established categories. Data collection techniques are observational recording, interview, and questionnaire. The instruments of data collection are checklist of lesson plan, interview, observational recording, and questionnaire. The findings consist of twenty claims. The conclusions are (1) teacher’s elicitation as key point, (2) elicitations to develop cognitive, (3) numbers of elicitation depending on the existence of students’ responses, (4) deeper involvement by teacher’s feedbacks, (5) no gap during grammar class orientation, (6) slow response, (7) active and interactive demands for the teacher, and (8) life on-going process of learning. The research suggests English teachers (1) to implement formative assessment conversation (2) to implement the claim because it is helpful in developing student cognition; (4) to provide sometimes of FACC absence for students to get ready into the next step and (5) the non-verbal attributes seen on the teacher facilitated the realization of formative assessment conversation to be understood by the students. This research is only limited on teacher without seeing the backwash on the students’ sides. Since it was sought to see the covert backwash, then the unit of analysis was classroom activity, specifically in formative assessment conversation cycle, in which it was administered orally. Further investigation is expected to see the backwash on other types of formative assessment administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".