Information and Communication Technology (ICT) Skills Among Teachers in the Division of Lanao Del Norte
Bibliographic record
Abstract
<p><span>This study Information and Communication Technology (ICT) Skills Among Secondary Teachers in the Division of Lanao Del Norte with respondents of one hundred twenty (120) teachers and was conducted in the 2nd quarter of the School Year 2022-2023. The study used the descriptive-correlational research design.<span>  </span>Descriptive research was used in describing the demographic profile in terms of age, gender, marital status, educational attainment, length of service and hours of ICT training; and the Information and Communication Skills (ICT) level competency of teachers in terms of their setup, maintenance, and troubleshooting skills, word processing skills, spreadsheet skills, telecommunications skills, basic programming, video editing, and graphics design. It was also correlation research since the demographic profile of the respondents will be correlated to the Information and Communication Skills (ICT). The data collected were subjected to analysis, the mean and standard deviation were used to answer the research questions while the hypotheses were tested with an F-value of 9.799 with a corresponding p-value of 0.000 level of significance. Based on the results of the study, most of the teachers were of millennial age, female, married, with Master’s Units, have 6-10 years length of service, and 8 hours and below ICT hours of training. It was recommended that teachers should have high confidence and competency in using ICT in the classroom since ICT is a tool that could help in the learning process, especially with real-life practices and they can facilitate instruction without losing time and energy in achieving the learning outcomes.</span></p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".