Comparative Analysis of Teaching Strategies Used in Lessons on Culture and Lessons on Heritage of a Selected Chinese-Filipino School
Bibliographic record
Abstract
This paper seeks to identify the teaching strategies used in Grade 4 Culture and Heritage lessons in a Chinese-Filipino School. Further, it is important to effectively address these with constructivist-based teaching strategies to help maximize the learning experience. This study used qualitative method where Ethnographic research design and Descriptive method were used to conduct classroom observations. The instruments used to obtain the results were patterned and modified from Craig Kissock’s (1981) Curriculum Planning for Social Studies Teaching: A Cross-cultural Approach, a self-made Teachers’ Self-evaluation form and Peer Observation form participated by nine teachers of a Chinese-Filipino School in Quezon City, Philippines. The analysis of the results identified five teaching strategies with Discussion and Lecture strategy as the most applied alongside Game strategy, Question and Answer strategy, and Project method. Other activities that strengthened the Culture and Heritage awareness were integrated through a collaborative effort with other subject areas. As a result, the study supported that Social Development theory affects the teaching-learning process where opportunities for social interaction helps in the development of a child. Using various teaching strategies encouraged students to interact with one another, to freely express their thoughts, and to participate in fun and healthy competition. It also encouraged students to think and to unleash their creativity and imagination. At the same time, the use of tools and other resources helped increase student engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".