Development of Android-Based Strategic Intervention Material for Science 6
Bibliographic record
Abstract
This study was conducted to put forward the development of an Android-based Strategic Intervention Material (SIM) that addresses the least mastered competency in the first quarter of Science 6. Both qualitative and quantitative approaches were employed in this study using purposive sampling technique. Key informant interview was used to identify the least mastered competency. A focus-group discussion was also conducted to validate and triangulate the gathered data. Meanwhile, descriptive statistics were used to determine the level of acceptability of the intervention material. Four (4) expert evaluators were also tapped to assess and validate the practical utility and acceptability of the SIM. Results revealed that the recurring least mastered competency for the first quarter is on “classification of the different types of colloids”. Utilizing the ADDIE model, an Android-based SIM was developed. The level of acceptability for implementation of the Android-based SIM generally appeared as “good” in terms of technical, instructional, activity, content and significance. Hence, it is recommended that teachers should venture into designing intervention materials that are technologically assisted. Moreover, future studies may explore on assessing the effectiveness of the Android-based SIM in improving students’ performance on the identified least mastered competency.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".