Development and Validation of Video Lessons in Teaching Science 7
Bibliographic record
Abstract
This study aimed to determine the least mastered competencies in Science 7, Quarter 3, under the MATATAG Curriculum as the basis for developing and validating video lessons in teaching science. This study's respondents were junior high school students from Angadanan East District, under the Division of Isabela. Stratified allocation was employed to compute the sample size of the learners, which was 167. At the same time, all science teachers were purposively selected, with a total of nine, who evaluated the developed video lessons using the adopted checklist composed of three factors: content, structure, and usability. The researcher utilized a 4-D Model to develop and validate video lessons. The mean percentage score was used to determine the "Not Mastered" competencies in Science 7 Quarter 3, and the Wilcoxon Signed-Rank Test was used to understand whether there was a difference between the pre-test and post-test after implementing the teacher-made video lessons. The study's findings revealed that customized video lessons need to consider the video elements in designing and developing to produce a more substantial learning gain. However, the science-teacher respondents who evaluated the four developed video lessons rated them "Very Much Useful" in content, structure, and usability. Based on this study, a proposed guideline aligned to the three video elements, namely Cognitive Load, Student Engagement, and Active Learning, in developing video lessons for teaching Science 7 under the MATATAG Curriculum. This guideline will serve as a framework for developing supplementary materials to help and support students struggling to learn science concepts.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".