Enhancing Student’s Performance in Arnis Using Teachers’ Made Instructional Video
Bibliographic record
Abstract
This study measured the Effectiveness of Instructional Video in Teaching Arnis among Grade 7 students during the third quarter of the Academic Year 2020-2021 at Gulod National High School. This study aims to help the MAPEH teacher in enhancing their strategies in teaching Arnis. This is also to motivate the learners to learn Arnis and to showcase Filipino martial arts since most of the youth today are engaged in multimedia. This study is a Quasi-experimental design that has two groups, the Experimental Group, and the Comparison Group. The respondents of the study were composed of 36 males and 30 females of grade 7 under the Physical Education class of Gulod National High School, Cabuyao, Laguna.The respondents were clustered into two groups that consist of 18 males for the experimental group and 18 males for the comparison group, while females were 15 in the experimental group and 15 for the comparison group. Descriptive statistics such as frequency count, percentage, weighted mean, and standard deviation were used in the study.The analysis has been detected that there is a significant difference in the performance in the pre-test, formative and posttest mean obtained by experimental and comparison groups. It is suggested for further study to determine the effectiveness of flipped classrooms or digital platforms on the academic performance of the students. But in addition, the study on students’ critical thinking, learning experiences, motivation, and the likes will not only be in Physical Education subjects but also in other subjects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".