Project VLOGI (Video Lectures on Giving Instructions): Effects on Learners’ Performance in Probability and Statistics
Bibliographic record
Abstract
This quantitative study ascertained the benefits of project VLOGI as an intervention to the eighth-grade students in resolving the usual unattained learning competencies in Probability and Statistics, especially in the last quarter of the school year. The researcher utilized the true-experimental research design using vlogs as an intervention to this study. There were two (2) randomly selected classes out of the six (6) heterogeneous classes in school as (1) the control group (prevailing method) and (2) the experimental group (project VLOGI) in teaching. Respondents underwent pre-test and post-test utilizing the quality assured 20-item multiple-choice type of questionnaires, reviewed and verified by an expert panel of evaluators. The researcher used descriptive and inferential analyses using the SPSS 2.0 tool to analyze and interpret the outcomes. The study's significant results showed that learners improved their academic performance in Probability and Statistics using these vlogs. Likewise, learners gained knowledge while working on their projects to create vlogs. Furthermore, by capturing learners' attention and engaging them in their learning through various social media platforms, project VLOGI was served as an alternative teaching method in any discipline. Hence, project VLOGI was strongly recommended for teachers as a replacement when they are out of class due to ancillary functions in school to foster unattained learning competencies before the school year ends.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".