Effectiveness of Graphic Organizer Instruction on Students’ Achievement in Social Science of Sta. Maria Integrated School: Basis for Improved Instruction
Bibliographic record
Abstract
The research traced the aftermath of using graphic organizers (GOs) to the achievement of Grade XI students in Social Science. Six specific problems on the pre-post-test of students in Social Studies were measured. The one-shot time-series experimental design and the complete enumeration method were utilized with the mean, mean percentage, standard deviation, z-test, chi-square, and t-test to treat data findings. The pre-test bears a very low performance on the six skills tested. However, there is a notable variation and increase in mastery level of Grade XI students during the second and third quarter examinations. The post-test achievement of students is at the average level. There is a significant relationship between the achievement level of students and their attitude towards the use of GOs. The efforts of the teachers to use graphic organizers in teaching Social Science were fruitful. The shift in paradigm in teaching and learning Social Science has developed the meta-cognitive skills of a student and their higher-level thinking skills. It was recommended that the administrator, teachers, and parents strongly support students’ learning activities by collaboratively providing the much-needed materials in GO’s and developing students’ affective domain to make it tuned to the constructivist and learner-centered K-12 curriculum.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".