The Effect of Textbooks on Eliminating Reflection Errors and Number Omission Errors in Early Childhood Teaching
Bibliographic record
Abstract
The study investigated the effect of instructional materials in eradicating reflection errors and number omission errors.The population of the study involved 450 children from 15 pre-schools which had been selected by random sampling.The research design used was Quasi experimental.It had both control and experimental group and the data was analysed by means of frequencies and percentages.The study was based on constructivist theory by John Dewey.The study had sought to achieve the following objectives:-(i) The pretest performance of control and experimental group (ii) The posttest performance of both control and experimental group (iii) The relationship between performance of the two groups.The data showed that the control group in both pretest and posttest made many errors while the experimental group which had used instructional materials made few errors and performed better.The findings of the study in the pretest and posttest of the control group in reflection errors recorded 76.5% and 72.5% respectively.There was a 4% improvement.The pretest and posttest of the Experimental group in reflection errors recorded 73.5% and 16.5%.There was 57% improvement.The pretest and posttest for number omission errors in control group showed 68.5% errors and 64.5% respectively.There was a 4% improvement.The pretest and posttest for the Experimental group in number omission errors recorded 64.5% and 21.5%.There was 43% improvement.The study recommended use of adequate and age appropriate materials during instruction.Children should be discouraged from copying other children's work in order to avoid transferring mistakes to their work.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".