ENHANCED MODULE IN ART SKILLS: AN INNOVATIVE INSTRUCTIONAL MATERIAL IN ILLUSTRATIONS
Bibliographic record
Abstract
This studys focus is on the production of a Enhanced Module in Art Skills which can be used as an innovative instructional material in teaching Illustrations 9. The study was limited to the assessment of whether the use of intervention material would be effective. The gathering of data school was done during the Second Quarter of the School-Year 2022-2023 in Gov. Felicisimo T. San Luis Integrated Senior High School where the researcher is teaching. The respondents were ten (70) illustrations students and some TLE teachers from different schools from Pagsanjan, Sta. Cruz and Pila who validated the Enhanced Module in Art Skills. Mean and Standard Deviation were used to determine the respondents perception of the utilization of innovative instructional material. T-test was used to determine the significant relationship between Enhanced Module in Art Skills and the second-quarter performance of Grade 9 students. Thus, this helped the researcher to answer the hypothesis. Teachers perceived that the use of Enhanced Module in Art Skills instructional material (i.e., activities, content, objectives, design was very effective), and it helped the respondents enhance their performance in Illustrations 9. They benefited from using the module as innovative instructional material to easily understand the lesson. Moreover, there was a significant effect to the performance of the grade 9 students with the use of the Enhanced Module in Art Skills. It is recommended that TLE teachers focus and give emphasis of the objectives of the lesson or any instructional materials and resources. Also, that the performance monitoring may focus on the students needs and enable them to learn especially on laboratory performances. They may find the meaning of laboratory skills out of the context if they experience more hands on activities.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".