Comparison of Quarterly Performance in Science of Grade 7 Students in Public School
Bibliographic record
Abstract
In a typical science curriculum with different scientific disciplines (Chemistry, Biology, Physics and Earth Sciences) taught in every quarter, it is important to determine the difference in the performance of students under each discipline. In this study, the comparison of quarterly performance in Science of Grade 7 Students was examined. Wherein, the consolidated quarterly grades in science from the previous school year (June 2018 - April 2019) from five sections with a total of 272 male and female grade 7 students were used as data. It employed a quantitative research design using a One-way Analysis of Variance ( ANOVA) to determine the significant difference in the mean grades per quarter. Additionally, an interview with the science teachers who handled the samples was conducted to gather qualitative data to further explain the results. The results of this study revealed a mean of 83.50 from the combined grades in the second quarter which is the lowest among the four quarters. However, the results from ANOVA generated a significance of .123 (p≥.05) which means that there is no significant difference between the grades in the first, second, third and fourth quarter.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".