Readiness for Blended Learning and ICT Classroom Teaching Practices of Secondary School Teachers in Bambang I District
Bibliographic record
Abstract
This study focused on the assessment of the ICT readiness of Secondary School teachers in Bambang I District of the Schools Division of Nueva Vizcaya, Philippines. An adopted three-part questionnaire was distributed to 113 teachers and the consolidation of their IRCR rating for school year 2020 – 2021 was utilized in acquiring data needed for the study. Both descriptive and inferential statistics were utilized in treating and analyzing the research data. Wherein it was found out that their ICT readiness was highly positive with a mean score of 3.25. Furthermore, it was gleaned from the consolidated IPCR rating of the teachers that they have a very satisfactory performance during the rating period with significant differences with their ICT readiness/skills and COT performance rating with a t – value of 14.7691 and p – value of 0.0000. The very satisfactory rating of the respondents in their PPST – COT performance for quarter 1 – 4 of SY 2020 – 2021, indicates the readiness of the respondents to integrate ICT based instruction as part of the blended learning modality. The significant difference in the research findings implies that their ICT skill affects their teaching performance through the use of higher- level online teaching materials such google forms, google meet, and other online tools necessary for the integration of ICT – based instruction. The proposed intervention is gleaned to be necessary in providing hands-on training for teachers in using new and more accessible software’s for instruction in the new normal. Since the ICT classroom pedagogy/skills of the participants are highly positive
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.000 |
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".