Comparison of Oral Reading Errors between Contextual Sentences and Random Words among Schoolchildren
Bibliographic record
Abstract
This paper compares the oral reading errors between the contextual sentences and random words among schoolchildren. Two sets of reading materials were developed to test the oral reading errors in 30 schoolchildren (10.00±1.44 years). Set A was comprised contextual sentences while Set B encompassed random words. The schoolchildren were asked to read both contextual sentences and random words reading charts at random order, loudly at normal reading speed. The reading errors were quantified based on the number of mistakes made during reading. The errors were classified into 6 categories; mispronunciations, substitutions, refusals, additions, omissions, and reversals. The results indicated the mean number of errors made by schoolchildren in reading sentence of Set A and Set B were 1.30±0.23 words and 2.70±0.41 words respectively. Random words, Set B, gave a significantly higher number of reading errors compared to contextual sentences, Set A, (U=287, z=-2.46, p=0.01). Reading the random words gave higher number of errors compared to reading the contextual sentences. Mispronunciations and substitutions were the most possible types of errors made when reading Set B (U=234, z=-3.60, p=<0.01 and U=325, z=-2.00, p=0.04 respectively). Schoolchildren tended to mispronounce and substitute some words during reading the random words. In comparing the number of oral reading errors made between schoolchildren and young adults, there was no significant difference. A similar pattern of the type of errors was also found in oral reading errors in both schoolchildren and young adults. Overall findings could be linked to the existence of comprehension during reading the contextual sentences compared to reading the random words.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".