YouTube Channel Integration in Teaching Science and Pupils’ Academic Performance
Bibliographic record
Abstract
The study was conducted to compare the pupils’ academic performance in science who are exposed to a YouTube channel in the first and second quarters of the school year. The study employed a mixed-methods sequential explanatory design, where forty-five (45) Grade-VI pupils were selected using a convenience sampling method. To analyze and interpret data, the study used descriptive statistics and the t-test Paired Two Sample for Means for quantitative data. In contrast, for qualitative data, the study adopted Colaizzi’s 1978 method of data analysis. The study revealed that the academic performance of the pupils in the first quarter was at a proficient and advanced level in the second quarter. Furthermore, the study discovered that the p-value is 0.03, less than the 0.5 level of significance, which shows that there is a significant difference in academic performance between the first and second quarters using the YouTube channel. Meanwhile, themes emerged on how pupils perceived the use of videos as engaging and motivating. Additionally, themes such as uniformity of instruction and internet fluctuation emerged as challenges encountered using the YouTube channel. It is recommended that teachers adopt the intervention since it is found effective not just in science but in other subject areas, and attend seminars and training on differentiated and ICT integration to address the gaps in learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".