Prediction Skills, Reading Comprehension and Learning Achievement in Vihiga County Kenya. Addressing Constraints and Prospects
Bibliographic record
Abstract
Prediction skill may be used in reading comprehension passages as encapsulated in interactive approach instruction. Prediction skills assist learners to decode the meaning of comprehension passages by constructing guesses about the contents of texts to be read in comprehension passages. Learners in Vihiga County register low achievement in English language examinations than peers in neighbouring counties over the years. The performance is much weaker in comprehension passages than grammar sections. Although there are low grades, the nexus between use of prediction skills and learners’ achievement in reading comprehension passages has not been assessed. This study applied the Solomon Four Non-Equivalent Group Design to obtain primary data from 279 primary school learners and 8 teachers in 2017. Multiple linear regression used generated two models, one for the experimental group (Model 1) and one for the control group (Model 2). Findings indicate that the influence of prediction skills on learner achievement in reading comprehension passages was significant in experimental, but insignificant in the control groups. However, influence was stronger in the experimental than in the control groups, suggesting that training English language teachers on correct application of prediction skills improves learner achievement in reading comprehension passages. The study recommends need to: sensitise teachers on textbook usage, while supplementing with improvised materials; guide learners through titles; as well as update teacher training curriculum by integrating inter alia, emerging instructional methods embracing Information and Communication Technology and entrenching innovation in resource mobilization and use.
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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 | 0.002 |
| 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 | 0.000 |
| 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".