Effects of Technohome Project on the Hands-on-Skills and Academic Performance of Grade 10 Stduents Final
Bibliographic record
Abstract
The study aimed to determine the extent of effects of TechnoHome Project on the hands-on skills and academic performance of grade 10 students during the School Year 2024-2025. The respondents of the study were the total population of grade 10 students, which is composed of 141 students. They were described in terms of their sex, sibling position, number of children in the family, monthly family income, parents’ educational attainment and parents’ occupation. Descriptive research design was used in the study to determine the extent of effects of TechnoHome Project on the hands-on skills and academic performance of grade 10 students utilizing a researcher-made questionnaire-checklist. The aspects of the extent effects of TechnoHome Project include drawing skills, editing skills, layout integration skills, collaboration and exporting skills. Moreover, the extent of the problems encountered by the students in the utilization of TechnoHome Project was also considered as well. Documentary analysis was also utilized since the level of academic performance of the grade 10 students in Computer, as revealed in the third quarter was used as basis of their performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".