Bibliographic record
Abstract
The presents study investigated study habits (delay avoidance, work methods, teacher approval, and education acceptance) as predictors of grades in mathematics and English in a path model. There were several assumptions in past reviews accounting on how study habits directly explain grades in the presence of other factors but the present study isolated the effect of four study habits. There were 259 Filipino high school students who were requested to answer the Survey of Study Habits and Attitudes (SSHA) and their grades in mathematics and English for the first quarter were also asked. The four factors of study habits were first tested using a Confirmatory Factor Analysis (CFA) and the four-factor structure was proven having adequate fit (χ 2 =47432.81, df=8745, RMS Standardized Residual=.01, RMSEA=.01, NFI=.94, GFI=.95, PGI=.97). Path analysis was used to test the prediction of the four study habits to grade in mathematics and English and the model also had an adequate fit (χ 2 =366.48, IFI=.98, NFI=.98, CFI=.98, and RMSEA=.09). The path analysis revealed that work methods significantly predicted both grades in mathematics in science. Work method was the only predictor for mathematics and only teacher approval did not significantly predict grades in English. Further implications of the findings are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".