Analysis of Teacher Beliefs and Efficacy for Teaching Writing to Weak Learners
Bibliographic record
Abstract
The present study investigated the beliefs and efficacy of a teacher teaching English to students who were weak at the language. The objective of the study was mainly to investigate the beliefs and efficacy of the ESL teacher for teaching writing to weak learners. The research was a case study of the English Language teacher teaching Form Three class of students whose English proficiency was very low. An interview was conducted with the teacher to further probe the instructional strategies applied to enhance her beliefs and efficacy in her own capabilities to make learning happen in her classroom. Observations were made to investigate the teacher’s efficacy in teaching and the performance of the students specifically for writing. Results show the teacher’s beliefs of her students’ capabilities and their language needs helped shape the teacher’s instructional strategies. The teacher’s efficacy enabled her to decide to undertake the task of teaching writing to her students because she was confident in her ability. The teacher provided clues to the students to facilitate their learning. This kind of feedback from the teacher indirectly motivated them to learn. The teacher’s beliefs and efficacy contributed to her teaching practice and the instructional strategies that she used in turn enhanced her beliefs and efficacy. The study implicates that teacher’s beliefs and efficacy can assist the weak learners in improving their writing skills and also facilitate language 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 | 0.005 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".