INTEGRATING DIGITAL TECHNOLOGY SKILLS IN EARLYCHILDHOOD EDUCATION FOR SUSTAINABLE INNOVATIVE TEACHING IN PRIMARY SCHOOLS IN OYO STATE
Bibliographic record
Abstract
The paper examines the integrating digital technology skills in early childhood education for sustainable innovative teaching in primary schools in Oyo State. The study adopted a descriptive survey research design. Population comprises of all primary school teachers in Afijio Local Government in Oyo State, Nigeria. The sample size for this study comprises of ten (10) primary schools in which ten (10) teachers were randomly selected to make a total of 100 respondents for the study. A self-developed questionnaire was used as instrument for data collection. It was developed in closed-ended of Agree or Disagree. The instrument was moderated by an expert who affirmed its validity. Reliability of the instrument was determined using Cronbach Alpha and the value of 0.84 was obtained which is reliable enough for this study. Data collected were analyzed using simple percentage, mean and standard deviation statistical tools. Findings revealed that the integration of digital technology skills in Early Childhood Education (ECE) is crucial for fostering sustainable and innovative teaching practices in primary schools in Oyo State. By equipping educators with digital competencies, schools can enhance the quality of teaching and learning, making education more engaging, interactive, and aligned with 21st-century skills. The adoption of technology not only facilitates access to a broader range of educational resources but also supports personalized learning experiences and collaboration among pupils. It was therefore recommended that teachers should receive continuous professional development to enhance their digital technology skills and keep up with emerging educational technologies. This training will ensure they are well-equipped to implement innovative teaching practices effectively.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".