Binary Logistic Regression Analysis of Teacher Self-Efficacy Factors Influencing Literacy and Numeracy
Bibliographic record
Abstract
This paper discusses the teacher efficacy factors contributing to student achievement in literacy and numeracy in 105primary schools within Sibu division, Sarawak, Malaysia. The study observed high levels of practice for teacherefficacy. The t-test and one-way analysis of variance (ANOVA) were used to analyze the differences between gender,teaching experience and academic qualification. The study reported significant differences in respondent perceptionsbased on teaching experience. Here, the post hoc Tukey test revealed that efficaciousness grows with years of teachingexperience. A correlation test observed a significant relationship between the independent variable with studentachievement in literacy. Binary logistic regression was applied to predict the influence of teacher efficacy on literacyand numeracy. The findings revealed that a dimension of teacher self-efficacy – efficacy in student engagement -emerged as the best predictor for student achievement for English literacy (LBI). The result indicated that for every1-point increase in the self-reported efficacy for student engagement, the school was .014 times less likely to achieve100% literacy rate for LBI. In conclusion, the teacher’s self-efficacy in student engagement had a negative influence onthe mastery of basic literacy for the English language, hence necessitating a closer inspection of the variable within thecontext of LINUS2.0. However, more comprehensive studies are needed to ascertain its consistency as well asinvestigating positive predictors for literacy.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".