Group Dynamics Strategy in Teaching Araling Panlipunan in Calaca District
Bibliographic record
Abstract
The main objective of this study was to determine the extent of utilizing group dynamics as an effective strategy in teaching Araling Panlipunan. The respondents of the study were the entire three hundred twenty (320) teachers of the above-mentioned district. The data gathering instrument used in this study was the questionnaire which was composed of assessment checklist. The data gathered were tabulated, analyzed and interpreted using Frequency Counts and Percentage, Weighted Mean, Standard Deviation and coefficient of correlation. The results of the study were as follows: the respondents utilized the group dynamics as an effective strategy in teaching ‘to a great extent’ in terms of concept mapping, group discussion, dramatization, problem-solving, peer-tutoring, role playing/assimilation group reporting, project making, tableau interpretation and topic analysis. They were also assessed ‘to a Great Extent’ that includes the attitudes of the pupils affected by the group dynamics strategies in terms of cognitive, affective, behavioral and social. The challenges met by the teachers was done by using group dynamics as strategy in teaching Araling Panlipunan were also assessed “to a great extent’. There was significant relationship between group dynamic strategies employed and the extent of pupils attitude. There was also significant relationship between group dynamics strategies employed and the challenges experienced on it. There is a significant relationship between the extent of the pupils attitude and the challenges experienced on it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".