INFLUENCE OF GENDER ON EDUCATORS’ COMPETENCE AND USE OF DIGITAL TECHNOLOGIES IN HIGHER EDUCATION
Bibliographic record
Abstract
The study was carried out to determine the influence of gender on digital competence of educators in tertiary institutions in Imo State.The population of the study consists of 722 academic staff referred to as "educators" of Alvan Ikoku Federal College of Education Owerri, Imo State.A sample of 225 multidisciplinary educators determined using stratified random sampling was used for study.Based on the objectives of the study, 2 research questions and a hypothesis guided the study.The descriptive survey research design was adopted in conducting the study.The instrument for data collection was a researcher made likert 4-points type questionnaire titled "Educators Gender and Digital Competence (EGDC)".It had a reliability coefficient of 0.87 determined using Cronbach alpha method.The data generated were analyzed using mean and standard deviation to answer research questions while the hypothesis was tested at 0.05 level of significance using t-test statistical tool.The result of the study revealed that educators in tertiary institutions were digitally competent; gender had no influence on the digital competence of the educators.Based on the result of the study, it was recommended that, in-service trainings, workshops, conferences should be organized to sustain and improve the competence level of educators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".