TECHNOLOGY ACTIVITIES AND LANGUAGE DEVELOPMENT OF THE PRESCHOOLERS IN OGBA/EGBEMA/NDONI LOCAL GOVERNMENT AREA OF RIVERS STATE
Bibliographic record
Abstract
This study investigated the influence of technology activities on the language development of the preschoolers. It was directed to explore the influence of technology activities on language development. Two research questions and corresponding hypotheses guided the study. The design adopted in this study is descriptive survey. The instrument used was a researcher - made questionnaire which was given to three experts in Measurement and Evaluation for face and content validity. The reliability of the instrument was determined, using test-retest The reliability index was 0.75. Census sample and sampling techniques were used for the study. The result reveals that there are significant influences of digital storytelling and forming digital pictures on the language development of the preschoolers. Some recommendations were made among others that caregivers and parents should utilize digital storytelling as a tool to enhance the language development and ensure technology activities as specified in the study in addition to the language development of the preschoolers stipulated in the National Policy on Educat on while teaching Reading Skills in Early Childhood Care Development and Education (ECCDE) centres. This will help the Preschoolers to use the already developed language skills and the lessons will be based on the preschoolers' interests and suggestions were made.
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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 | 0.000 |
| 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".