Utilization of Quizizz-Assisted Instructional Materials for Mathematics 8
Bibliographic record
Abstract
This study focused on the utilization of Quizizz-Assisted Instructional Materials for Mathematics for the Grade 8 students at San Jose National High School, City Schools Division Office of Antipolo, School Year 2022 – 2023. The topics that were developed into Quizizz-Assisted Instructional Materials based on the school quarterly test result for two consecutive years’ school year 2020 – 2020 and 2021 – 2022 were the topics under the second quarter on which gained least mean percentage score. The evaluation of Math experts and Math teacher respondents on the developed instructional materials in terms of content, organization and presentation, ease of use, usefulness, impact was interpreted as very highly acceptable, with a grand weighted mean of 3.89 and 3.92, respectively which also showed no significant difference. Meanwhile, the level of performance of the control group and the experimental group based on the pretest revealed that the performance of the control and experimental groups has mean scores of 7.87 and 7.67, respectively, and standard deviations of 2.97 and 3.10, with both interpreted as Not Proficient, On the other hand, the posttest performance of two groups of students, the control group has the mean score of 16.83 and standard deviation of 5.14 with verbal interpretations of Nearly Proficient, while the experimental group has the mean score of 23.20 and standard deviation of 4.73 with verbal interpretation of Proficient. Also, there was a significant difference between the pretest and posttest mean scores of the experimental groups. Comments and suggestions were given by the respondents to further improve the instructional material.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".